{ "version": 1, "documents": [ { "id": ".agents/plugins/marketplace.json#0", "path": ".agents/plugins/marketplace.json", "chunk": 0, "text": "{ \"name\": \"[TODO: marketplace-name]\", \"interface\": { \"displayName\": \"[TODO: Marketplace Display Name]\" }, \"plugins\": [ { \"name\": \"hops-memory-fabric\", \"source\": { \"source\": \"local\", \"path\": \"./plugins/hops-memory-fabric\" }, \"policy\": { \"installation\": \"AVAILABLE\", \"authentication\": \"ON_INSTALL\" }, \"category\": \"Productivity\" } ] }", "tokens": 30, "vector": { "386": 0.5163977794943222, "352": -0.2581988897471611, "479": -0.3872983346207417, "199": -0.12909944487358055, "418": 0.12909944487358055, "402": 0.2581988897471611, "152": 0.2581988897471611, "192": -0.2581988897471611, "173": 0.2581988897471611, "321": 0.2581988897471611, "293": -0.12909944487358055, "416": -0.12909944487358055, "130": 0.12909944487358055, "342": 0.12909944487358055, "477": 0.12909944487358055, "441": -0.12909944487358055, "467": 0.12909944487358055, "237": 0.12909944487358055, "445": -0.12909944487358055 } }, { "id": ".wrangler/cache/pages.json#0", "path": ".wrangler/cache/pages.json", "chunk": 0, "text": "{ \"account_id\": \"957c732a6349123bfc288326c3095dd8\", \"project_name\": \"hops-ops-dashboard\" }", "tokens": 6, "vector": { "371": -0.4082482904638631, "325": 0.4082482904638631, "67": 0.4082482904638631, "152": 0.4082482904638631, "169": 0.4082482904638631, "358": 0.4082482904638631 } }, { "id": ".wrangler/cache/wrangler-account.json#0", "path": ".wrangler/cache/wrangler-account.json", "chunk": 0, "text": "{ \"account\": { \"id\": \"957c732a6349123bfc288326c3095dd8\", \"name\": \"\" } }", "tokens": 4, "vector": { "154": -0.5, "421": 0.5, "325": 0.5, "386": 0.5 } }, { "id": "README.md#0", "path": "README.md", "chunk": 0, "text": "# Everett Engine Scientific Harness This is a reproducible testbed for the proposed complex-domain accumulator: ```text W_future = sum(C * S0 * A * a_i * E * S1_i) Q = C * S0 * E * S_target a_rec = W_future * conj(Q) * conj(A) ``` It compares three retrieval modes: - `complex/raw`: unit complex phase binding and raw superposition. - `bipolar/raw`: +/-1 binding with raw real-valued accumulation. - `bipolar/majority`: +/-1 binding followed by majority/sign bundling. The harness reports target resonance, maximum background resonance, SNR, top-1 accuracy, top-5 accuracy, aggregate statistics across repeated seeds, capacity indicators, a Markdown report, and PNG plots. ## Quick Run ```bash python3 run_benchmark.py --profile tiny ``` Results are written to: - `results/raw_trials.csv` - `results/raw_trials.json` - `results/aggregate.csv` - `results/aggregate.json` - `results/report.md` - `results/plots/*.png` The default profile is `fast`: ```bash python3 run_benchmark.py ``` It runs: - dimensions: `1000,5000,10000` - branches: `10,100,1000` - distractor similarity: `0.0,0.1,0.25` - target corruption: `0.0,0.05,0.1` - seeds per condition: `5` ## Stress Examples Increase branch load: ```ba", "tokens": 145, "vector": { "430": -0.060522753266880246, "493": 0.060522753266880246, "133": 0.060522753266880246, "329": 0.12104550653376049, "286": -0.060522753266880246, "506": -0.12104550653376049, "9": 0.12104550653376049, "52": 0.060522753266880246, "16": 0.060522753266880246, "441": -0.18156825980064073, "385": -0.060522753266880246, "490": 0.18156825980064073, "498": 0.12104550653376049, "230": -0.060522753266880246, "408": -0.060522753266880246, "1": 0.12104550653376049, "265": 0.060522753266880246, "236": -0.12104550653376049, "109": -0.060522753266880246, "472": 0.060522753266880246, "418": -0.060522753266880246, "363": -0.060522753266880246, "465": 0.12104550653376049, "42": -0.12104550653376049, "225": 0.0, "395": -0.060522753266880246, "366": 0.060522753266880246, "0": -0.060522753266880246, "471": 0.24209101306752098, "56": 0.060522753266880246, "258": 0.18156825980064073, "384": -0.18156825980064073, "354": -0.12104550653376049, "457": -0.060522753266880246, "262": 0.060522753266880246, "426": 0.060522753266880246, "353": -0.060522753266880246, "217": -0.060522753266880246, "290": 0.18156825980064073, "23": 0.060522753266880246, "167": -0.060522753266880246, "267": -0.060522753266880246, "311": -0.060522753266880246, "127": -0.060522753266880246, "41": 0.12104550653376049, "138": 0.060522753266880246, "393": -0.060522753266880246, "24": -0.060522753266880246, "40": -0.12104550653376049, "344": 0.12104550653376049, "238": -0.18156825980064073, "73": -0.060522753266880246, "478": 0.060522753266880246, "477": 0.060522753266880246, "68": 0.18156825980064073, "492": -0.060522753266880246, "188": -0.060522753266880246, "132": -0.12104550653376049, "143": -0.12104550653376049, "406": -0.12104550653376049, "172": -0.060522753266880246, "55": -0.060522753266880246, "193": 0.12104550653376049, "194": -0.12104550653376049, "25": 0.12104550653376049, "137": -0.060522753266880246, "448": -0.42365927286816174, "186": 0.060522753266880246, "204": -0.060522753266880246, "102": -0.060522753266880246, "205": -0.12104550653376049, "339": 0.12104550653376049, "33": -0.060522753266880246, "273": -0.060522753266880246, "31": -0.060522753266880246, "112": -0.060522753266880246, "320": 0.12104550653376049, "15": -0.060522753266880246, "313": -0.060522753266880246, "476": -0.060522753266880246, "74": 0.060522753266880246, "429": -0.060522753266880246, "140": 0.060522753266880246, "439": -0.060522753266880246, "436": -0.060522753266880246, "345": -0.060522753266880246, "500": 0.060522753266880246, "197": 0.060522753266880246, "226": -0.060522753266880246, "243": 0.060522753266880246, "12": -0.060522753266880246, "407": 0.060522753266880246 } }, { "id": "README.md#1", "path": "README.md", "chunk": 1, "text": ",100,1000` - distractor similarity: `0.0,0.1,0.25` - target corruption: `0.0,0.05,0.1` - seeds per condition: `5` ## Stress Examples Increase branch load: ```bash python3 run_benchmark.py --dimensions 10000 --branches 100,1000,5000 --seeds-per-condition 5 ``` Add distractor future states that partially resemble the target: ```bash python3 run_benchmark.py \\ --dimensions 10000 \\ --branches 100,1000 \\ --distractor-similarity 0.0,0.1,0.25,0.5 \\ --seeds-per-condition 5 ``` Corrupt the target-state query: ```bash python3 run_benchmark.py \\ --dimensions 10000 \\ --branches 1000 \\ --target-corruption 0.0,0.05,0.1,0.2 \\ --seeds-per-condition 5 ``` Skip plots: ```bash python3 run_benchmark.py --profile fast --no-plots ``` ## Interpretation This harness does not claim literal free planning. Building `W_future` still costs work proportional to the number of encoded branches and the vector dimension unless that world vector has already been computed, cached, or built by parallel hardware. The measurable claim this project is designed to test is narrower and stronger: > Given a precomputed speculative superposition, complex-phase accumulation can preserve recoverable causal bindings with cleaner", "tokens": 148, "vector": { "429": -0.2102353324910547, "320": 0.2102353324910547, "9": 0.15767649936829103, "140": 0.10511766624552735, "439": -0.10511766624552735, "52": 0.2102353324910547, "225": 0.10511766624552735, "457": 0.10511766624552735, "68": 0.2102353324910547, "436": -0.2102353324910547, "345": -0.2102353324910547, "500": 0.052558833122763673, "197": 0.052558833122763673, "226": -0.052558833122763673, "243": 0.052558833122763673, "12": -0.052558833122763673, "55": -0.2102353324910547, "193": 0.2102353324910547, "194": -0.2102353324910547, "366": 0.26279416561381835, "112": -0.15767649936829103, "313": -0.15767649936829103, "476": -0.2102353324910547, "15": -0.052558833122763673, "126": -0.052558833122763673, "491": -0.052558833122763673, "402": 0.052558833122763673, "398": 0.10511766624552735, "75": 0.10511766624552735, "469": -0.052558833122763673, "441": -0.26279416561381835, "273": -0.15767649936829103, "424": -0.052558833122763673, "322": -0.10511766624552735, "406": -0.10511766624552735, "25": 0.052558833122763673, "147": -0.052558833122763673, "286": -0.10511766624552735, "329": 0.052558833122763673, "150": -0.052558833122763673, "293": -0.052558833122763673, "477": 0.10511766624552735, "386": -0.052558833122763673, "66": 0.052558833122763673, "337": 0.052558833122763673, "1": 0.052558833122763673, "148": -0.10511766624552735, "188": -0.052558833122763673, "256": -0.052558833122763673, "238": -0.052558833122763673, "102": -0.10511766624552735, "18": 0.052558833122763673, "296": 0.052558833122763673, "118": 0.052558833122763673, "354": -0.10511766624552735, "432": 0.10511766624552735, "328": -0.052558833122763673, "200": -0.052558833122763673, "72": 0.052558833122763673, "145": -0.052558833122763673, "490": 0.10511766624552735, "389": -0.052558833122763673, "346": 0.052558833122763673, "310": 0.052558833122763673, "369": -0.052558833122763673, "88": -0.052558833122763673, "167": -0.052558833122763673, "131": -0.052558833122763673, "38": -0.052558833122763673, "501": -0.052558833122763673, "36": 0.052558833122763673, "506": -0.10511766624552735, "508": -0.052558833122763673, "159": 0.0, "30": -0.052558833122763673, "272": 0.052558833122763673, "91": -0.052558833122763673, "207": 0.052558833122763673, "498": 0.052558833122763673, "258": 0.052558833122763673, "217": -0.052558833122763673, "285": 0.052558833122763673, "78": -0.052558833122763673, "287": -0.052558833122763673, "262": 0.052558833122763673 } }, { "id": "README.md#2", "path": "README.md", "chunk": 2, "text": "test is narrower and stronger: > Given a precomputed speculative superposition, complex-phase accumulation can preserve recoverable causal bindings with cleaner retrieval than majority-bundled bipolar HDC under branch load, distractor similarity, and query corruption. ## SAT Annealer The workspace also includes a continuous phase SAT annealer: ```bash python3 run_sat_annealer.py --mode both ``` It maps Boolean variables into phase/probability coordinates, minimizes a smooth clause-unsatisfaction energy, collapses the result back to Boolean assignments, and validates small instances against brute force. Results are written to: - `results/sat/sat_results.csv` - `results/sat/sat_results.json` - `results/sat/sat_report.md` This is an empirical continuous-relaxation solver, not a proof that NP-hardness has been eliminated. ## Memory-Native Compute Fabric Run the tiered memory, provenance HMK, and resonant routing proof harness: ```bash python3 run_memory_fabric.py ``` Results are written to: - `results/memory_fabric/fabric_results.csv` - `results/memory_fabric/fabric_results.json` - `results/memory_fabric/fabric_report.md` The harness demonstrates common complex-hypervector operations a", "tokens": 149, "vector": { "159": 0.0, "506": -0.11846977555181847, "30": -0.05923488777590923, "354": -0.23693955110363693, "272": 0.05923488777590923, "91": -0.05923488777590923, "148": 0.0, "207": 0.05923488777590923, "498": 0.05923488777590923, "490": 0.11846977555181847, "258": 0.29617443887954614, "217": -0.05923488777590923, "285": 0.05923488777590923, "78": -0.05923488777590923, "322": -0.05923488777590923, "287": -0.05923488777590923, "262": 0.05923488777590923, "273": -0.05923488777590923, "366": 0.11846977555181847, "371": -0.05923488777590923, "290": 0.05923488777590923, "332": -0.05923488777590923, "457": -0.05923488777590923, "55": -0.05923488777590923, "471": 0.05923488777590923, "243": 0.05923488777590923, "12": -0.11846977555181847, "9": 0.05923488777590923, "140": 0.05923488777590923, "424": -0.05923488777590923, "225": 0.05923488777590923, "51": 0.29617443887954614, "103": 0.11846977555181847, "441": -0.23693955110363693, "289": -0.05923488777590923, "187": -0.05923488777590923, "503": -0.11846977555181847, "193": 0.11846977555181847, "404": 0.05923488777590923, "230": -0.05923488777590923, "511": 0.05923488777590923, "42": -0.05923488777590923, "306": -0.05923488777590923, "191": 0.11846977555181847, "122": 0.05923488777590923, "107": 0.05923488777590923, "313": 0.05923488777590923, "297": 0.05923488777590923, "174": 0.05923488777590923, "448": -0.4146442144313646, "157": 0.05923488777590923, "98": 0.05923488777590923, "56": 0.05923488777590923, "40": -0.05923488777590923, "246": -0.05923488777590923, "60": -0.05923488777590923, "102": -0.1777046633277277, "495": 0.05923488777590923, "206": 0.05923488777590923, "385": -0.05923488777590923, "32": -0.05923488777590923, "167": 0.05923488777590923, "50": 0.05923488777590923, "186": 0.11846977555181847, "204": -0.11846977555181847, "250": 0.11846977555181847, "339": 0.11846977555181847, "440": -0.05923488777590923, "33": -0.11846977555181847, "286": -0.05923488777590923, "234": -0.05923488777590923, "66": -0.05923488777590923, "57": -0.05923488777590923, "184": 0.05923488777590923, "293": 0.0, "449": 0.11846977555181847, "398": 0.05923488777590923, "266": -0.05923488777590923, "145": -0.05923488777590923, "389": -0.05923488777590923, "429": 0.05923488777590923, "192": -0.11846977555181847, "446": -0.05923488777590923, "176": 0.05923488777590923, "173": 0.05923488777590923, "172": -0.05923488777590923, "150": 0.05923488777590923, "31": 0.05923488777590923, "487": 0.05923488777590923, "252": 0.05923488777590923, "329": 0.05923488777590923, "72": 0.05923488777590923, "437": -0.1777046633277277, "81": 0.11846977555181847, "114": -0.05923488777590923, "402": -0.05923488777590923, "189": -0.05923488777590923, "116": -0.05923488777590923 } }, { "id": "README.md#3", "path": "README.md", "chunk": 3, "text": "s.csv` - `results/memory_fabric/fabric_results.json` - `results/memory_fabric/fabric_report.md` The harness demonstrates common complex-hypervector operations across memory retrieval, provenance scoring, and semantic execution routing. Provenance acceptance is threshold-based vector evidence, not a cryptographic signature. ## Paid Pilot Suite Build the test-driven pilot package: ```bash python3 run_pilot_suite.py ``` Outputs: - `results/pilot_suite/site/index.html` - `results/pilot_suite/pilot_manifest.json` - `results/pilot_suite/pilot_brief.md` - `results/pilot_suite/hops/hops_results.csv` - `results/pilot_suite/memory_fabric/fabric_results.csv` The pilot suite runs the unit tests and regenerates optimization plus memory-fabric evidence in one command. ## Ops Dashboard, RAG, and Agent Mesh Build the organizer dashboard for local, GitHub, Cloudflare, RAG, and Cloudflare Workers AI agent specs: ```bash python3 run_ops_dashboard.py ``` Outputs: - `results/ops_dashboard/site/index.html` - `results/ops_dashboard/asset_catalog.json` - `results/ops_dashboard/rag_index.json` - `results/ops_dashboard/kimi_agents.json` Deploy to Cloudflare Pages after Wrangler auth is available: ```bash wr", "tokens": 143, "vector": { "339": 0.14944064441601537, "448": -0.5479490295253897, "437": -0.14944064441601537, "81": 0.09962709627734358, "258": 0.24906774069335894, "329": 0.0, "33": -0.09962709627734358, "441": -0.24906774069335894, "114": -0.04981354813867179, "402": -0.04981354813867179, "490": 0.04981354813867179, "189": 0.0, "116": -0.04981354813867179, "478": 0.04981354813867179, "192": -0.04981354813867179, "366": 0.04981354813867179, "150": 0.09962709627734358, "364": 0.04981354813867179, "354": -0.19925419255468715, "55": -0.19925419255468715, "164": 0.04981354813867179, "252": 0.04981354813867179, "449": 0.04981354813867179, "506": -0.09962709627734358, "73": -0.04981354813867179, "221": 0.04981354813867179, "432": 0.09962709627734358, "238": 0.09962709627734358, "293": -0.09962709627734358, "61": 0.04981354813867179, "282": -0.04981354813867179, "38": -0.04981354813867179, "7": -0.14944064441601537, "504": 0.09962709627734358, "324": 0.09962709627734358, "159": 0.04981354813867179, "308": 0.04981354813867179, "188": 0.04981354813867179, "193": 0.09962709627734358, "123": 0.09962709627734358, "483": 0.24906774069335894, "251": 0.09962709627734358, "27": -0.09962709627734358, "229": -0.09962709627734358, "336": 0.04981354813867179, "174": 0.04981354813867179, "152": 0.04981354813867179, "128": -0.04981354813867179, "31": -0.04981354813867179, "56": 0.09962709627734358, "345": 0.04981354813867179, "197": -0.04981354813867179, "190": 0.04981354813867179, "173": 0.04981354813867179, "344": -0.04981354813867179, "118": -0.04981354813867179, "93": 0.04981354813867179, "169": 0.04981354813867179, "358": 0.09962709627734358, "163": 0.09962709627734358, "212": -0.09962709627734358, "211": -0.04981354813867179, "267": -0.04981354813867179, "16": 0.04981354813867179, "115": 0.14944064441601537, "493": 0.04981354813867179, "50": 0.04981354813867179, "120": -0.04981354813867179, "194": -0.04981354813867179, "42": 0.19925419255468715, "499": 0.09962709627734358, "360": 0.04981354813867179, "249": -0.04981354813867179, "439": -0.04981354813867179, "102": -0.04981354813867179, "191": 0.04981354813867179, "182": -0.04981354813867179, "477": 0.04981354813867179, "444": 0.04981354813867179 } }, { "id": "README.md#4", "path": "README.md", "chunk": 4, "text": "son` - `results/ops_dashboard/rag_index.json` - `results/ops_dashboard/kimi_agents.json` Deploy to Cloudflare Pages after Wrangler auth is available: ```bash wrangler pages deploy results/ops_dashboard/site --project-name hops-ops-dashboard ``` Cloudflare-hosted inference uses a Workers AI binding named `AI` and defaults to `@cf/moonshotai/kimi-k2.6`. If the binding is unavailable, the deployed agent falls back to deterministic local RAG snippets.", "tokens": 61, "vector": { "152": 0.20100756305184242, "448": -0.30151134457776363, "42": 0.30151134457776363, "360": 0.10050378152592121, "258": 0.20100756305184242, "249": -0.10050378152592121, "439": -0.20100756305184242, "102": -0.30151134457776363, "115": 0.20100756305184242, "191": 0.20100756305184242, "499": 0.10050378152592121, "182": -0.20100756305184242, "189": 0.10050378152592121, "506": -0.20100756305184242, "477": 0.10050378152592121, "55": -0.10050378152592121, "251": 0.10050378152592121, "36": 0.10050378152592121, "386": 0.10050378152592121, "169": 0.10050378152592121, "358": 0.10050378152592121, "332": -0.10050378152592121, "402": -0.10050378152592121, "244": -0.10050378152592121, "493": 0.10050378152592121, "50": 0.20100756305184242, "384": -0.20100756305184242, "417": -0.10050378152592121, "354": -0.10050378152592121, "271": 0.10050378152592121, "371": 0.10050378152592121, "228": -0.10050378152592121, "484": -0.10050378152592121, "257": -0.10050378152592121, "403": -0.10050378152592121, "441": -0.20100756305184242, "442": 0.10050378152592121, "193": -0.10050378152592121, "212": -0.10050378152592121, "449": 0.10050378152592121, "60": -0.10050378152592121, "267": -0.10050378152592121, "293": -0.10050378152592121, "163": 0.10050378152592121, "289": -0.10050378152592121 } }, { "id": "everett_engine.py#0", "path": "everett_engine.py", "chunk": 0, "text": "\"\"\"Everett Engine benchmark primitives. This module implements a small, reproducible HDC-style retrieval harness for comparing complex-phase accumulation against bipolar/XOR-style binding. \"\"\" from __future__ import annotations from dataclasses import dataclass from typing import Dict, Literal import numpy as np Domain = Literal[\"complex\", \"bipolar\"] BundleMode = Literal[\"raw\", \"majority\"] @dataclass(frozen=True) class TrialConfig: dimension: int = 10_000 branches: int = 100 target_index: int = 0 distractor_similarity: float = 0.0 target_corruption: float = 0.0 seed: int = 7 domain: Domain = \"complex\" bundle_mode: BundleMode = \"raw\" @dataclass(frozen=True) class TrialResult: domain: Domain bundle_mode: BundleMode dimension: int branches: int target_index: int distractor_similarity: float target_corruption: float target_resonance: float max_background_resonance: float snr: float top1_index: int top1_correct: bool top5_correct: bool def as_dict(self) -> Dict[str, object]: return { \"domain\": self.domain, \"bundle_mode\": self.bundle_mode, \"dimension\": self.dimension, \"branches\": self.branches, \"target_index\": self.target_index, \"distractor_similarity\": self.distractor_similarity, \"targe", "tokens": 130, "vector": { "430": -0.04856429311786321, "493": 0.04856429311786321, "270": 0.04856429311786321, "340": -0.04856429311786321, "286": -0.04856429311786321, "274": 0.04856429311786321, "352": -0.04856429311786321, "385": -0.04856429311786321, "9": 0.04856429311786321, "55": 0.04856429311786321, "203": -0.09712858623572641, "366": -0.04856429311786321, "329": 0.04856429311786321, "16": 0.04856429311786321, "168": 0.04856429311786321, "490": 0.14569287935358963, "258": 0.04856429311786321, "217": 0.14569287935358963, "167": 0.04856429311786321, "457": -0.09712858623572641, "282": 0.04856429311786321, "384": -0.04856429311786321, "373": -0.14569287935358963, "201": -0.04856429311786321, "297": 0.04856429311786321, "278": 0.04856429311786321, "296": 0.14569287935358963, "504": -0.04856429311786321, "106": -0.09712858623572641, "386": -0.14569287935358963, "371": 0.04856429311786321, "500": -0.04856429311786321, "266": -0.04856429311786321, "498": 0.33995005182504245, "477": -0.14569287935358963, "471": 0.09712858623572641, "290": 0.04856429311786321, "511": 0.09712858623572641, "181": 0.09712858623572641, "264": 0.09712858623572641, "506": -0.04856429311786321, "328": -0.19425717247145283, "109": 0.38851434494290565, "313": 0.04856429311786321, "476": -0.19425717247145283, "429": -0.04856429311786321, "363": -0.19425717247145283, "175": 0.19425717247145283, "374": 0.33995005182504245, "5": -0.09712858623572641, "25": -0.04856429311786321, "320": 0.19425717247145283, "424": -0.04856429311786321, "187": 0.04856429311786321, "317": -0.04856429311786321, "24": -0.04856429311786321, "383": 0.04856429311786321, "45": -0.04856429311786321, "183": -0.09712858623572641, "182": 0.04856429311786321, "459": 0.04856429311786321, "151": 0.04856429311786321, "6": 0.33995005182504245, "396": 0.04856429311786321, "41": 0.04856429311786321, "369": 0.04856429311786321, "483": 0.04856429311786321 } }, { "id": "everett_engine.py#1", "path": "everett_engine.py", "chunk": 1, "text": "dle_mode, \"dimension\": self.dimension, \"branches\": self.branches, \"target_index\": self.target_index, \"distractor_similarity\": self.distractor_similarity, \"target_corruption\": self.target_corruption, \"target_resonance\": self.target_resonance, \"max_background_resonance\": self.max_background_resonance, \"snr\": self.snr, \"top1_index\": self.top1_index, \"top1_correct\": self.top1_correct, \"top5_correct\": self.top5_correct, } def random_complex_hv(rng: np.random.Generator, shape: tuple[int, ...]) -> np.ndarray: phases = rng.uniform(0.0, 2.0 * np.pi, size=shape) return np.exp(1j * phases).astype(np.complex128) def random_bipolar_hv(rng: np.random.Generator, shape: tuple[int, ...]) -> np.ndarray: return rng.choice(np.array([-1.0, 1.0], dtype=np.float64), size=shape) def _copy_correlated_rows( rng: np.random.Generator, rows: np.ndarray, target: np.ndarray, similarity: float, target_index: int, ) -> np.ndarray: if similarity <= 0.0: rows[target_index] = target return rows if similarity >= 1.0: rows[:] = target return rows mask = rng.random(size=rows.shape) < similarity mask[target_index] = True rows = np.where(mask, target[np.newaxis, :], rows) rows[target_index] = target return rows def _corru", "tokens": 137, "vector": { "450": -0.0363456182092407, "328": 0.0363456182092407, "6": 0.3998018003016477, "476": -0.0726912364184814, "363": -0.2180737092554442, "175": 0.0726912364184814, "5": -0.0726912364184814, "187": 0.0726912364184814, "317": -0.0726912364184814, "24": -0.0726912364184814, "383": 0.0726912364184814, "45": -0.0726912364184814, "182": 0.0363456182092407, "459": 0.1453824728369628, "346": -0.0363456182092407, "177": 0.2180737092554442, "266": -0.5451842731386105, "420": -0.1453824728369628, "401": -0.1090368546277221, "501": -0.1817280910462035, "251": -0.0726912364184814, "109": 0.1090368546277221, "376": 0.1817280910462035, "390": 0.0726912364184814, "495": -0.0363456182092407, "133": 0.0363456182092407, "204": -0.1090368546277221, "369": 0.1817280910462035, "221": 0.0363456182092407, "94": 0.0363456182092407, "347": -0.0363456182092407, "499": -0.0363456182092407, "200": 0.0363456182092407, "463": 0.0363456182092407, "219": 0.0363456182092407, "107": 0.0363456182092407, "486": 0.0363456182092407, "444": -0.363456182092407, "52": 0.1817280910462035, "140": 0.1453824728369628, "374": 0.0363456182092407, "403": -0.0726912364184814, "181": 0.0363456182092407, "436": 0.0363456182092407, "281": 0.0363456182092407, "126": 0.0363456182092407 } }, { "id": "everett_engine.py#2", "path": "everett_engine.py", "chunk": 2, "text": "om(size=rows.shape) < similarity mask[target_index] = True rows = np.where(mask, target[np.newaxis, :], rows) rows[target_index] = target return rows def _corrupt_query_target( rng: np.random.Generator, target: np.ndarray, corruption: float, domain: Domain, ) -> np.ndarray: if corruption <= 0.0: return target.copy() mask = rng.random(size=target.shape) < corruption query = target.copy() if domain == \"complex\": query[mask] = random_complex_hv(rng, (int(mask.sum()),)) else: query[mask] *= -1.0 return query def run_trial(config: TrialConfig) -> TrialResult: if not 0 <= config.target_index < config.branches: raise ValueError(\"target_index must be inside the branch range\") if not 0.0 <= config.distractor_similarity <= 1.0: raise ValueError(\"distractor_similarity must be in [0, 1]\") if not 0.0 <= config.target_corruption <= 1.0: raise ValueError(\"target_corruption must be in [0, 1]\") rng = np.random.default_rng(config.seed) d = config.dimension n = config.branches if config.domain == \"complex\": cause = random_complex_hv(rng, (d,)) effect = random_complex_hv(rng, (d,)) action_role = random_complex_hv(rng, (d,)) state_0 = random_complex_hv(rng, (d,)) actions = random_complex_hv(rng, (n, d)", "tokens": 135, "vector": { "302": -0.042835293687811936, "204": -0.08567058737562387, "444": -0.21417646843905966, "501": -0.08567058737562387, "140": 0.042835293687811936, "328": 0.21417646843905966, "363": -0.17134117475124774, "181": 0.042835293687811936, "266": -0.2570117621268716, "436": 0.042835293687811936, "52": 0.2570117621268716, "281": 0.042835293687811936, "369": 0.1285058810634358, "459": 0.08567058737562387, "96": 0.042835293687811936, "177": 0.3855176431903074, "420": -0.1285058810634358, "401": -0.042835293687811936, "376": 0.08567058737562387, "225": 0.1285058810634358, "374": 0.042835293687811936, "498": 0.17134117475124774, "403": -0.2570117621268716, "111": -0.08567058737562387, "424": -0.21417646843905966, "490": 0.08567058737562387, "346": -0.2570117621268716, "109": 0.042835293687811936, "265": 0.042835293687811936, "381": -0.042835293687811936, "417": 0.042835293687811936, "183": 0.3855176431903074, "506": -0.042835293687811936, "293": -0.1285058810634358, "476": -0.042835293687811936, "484": 0.1285058810634358, "165": -0.1285058810634358, "321": -0.1285058810634358, "326": -0.1285058810634358, "272": 0.042835293687811936, "441": -0.042835293687811936, "243": 0.042835293687811936, "290": 0.042835293687811936, "175": 0.08567058737562387, "344": -0.08567058737562387, "5": -0.08567058737562387, "279": -0.042835293687811936, "25": -0.042835293687811936, "106": -0.042835293687811936, "345": -0.042835293687811936, "218": 0.042835293687811936, "299": 0.042835293687811936 } }, { "id": "everett_engine.py#3", "path": "everett_engine.py", "chunk": 3, "text": " effect = random_complex_hv(rng, (d,)) action_role = random_complex_hv(rng, (d,)) state_0 = random_complex_hv(rng, (d,)) actions = random_complex_hv(rng, (n, d)) target_state = random_complex_hv(rng, (d,)) future_states = random_complex_hv(rng, (n, d)) future_states = _copy_correlated_rows( rng, future_states, target_state, config.distractor_similarity, config.target_index ) query_target = _corrupt_query_target( rng, target_state, config.target_corruption, config.domain ) world = cause * state_0 * action_role * effect * np.sum(actions * future_states, axis=0) query = cause * state_0 * effect * query_target recovered = world * np.conj(query) * np.conj(action_role) resonances = np.real(np.einsum(\"ij,j->i\", np.conj(actions), recovered) / d) else: cause = random_bipolar_hv(rng, (d,)) effect = random_bipolar_hv(rng, (d,)) action_role = random_bipolar_hv(rng, (d,)) state_0 = random_bipolar_hv(rng, (d,)) actions = random_bipolar_hv(rng, (n, d)) target_state = random_bipolar_hv(rng, (d,)) future_states = random_bipolar_hv(rng, (n, d)) future_states = _copy_correlated_rows( rng, future_states, target_state, config.distractor_similarity, config.target_index ) query_target = _corrupt_query_ta", "tokens": 101, "vector": { "476": 0.16155847834440387, "346": -0.24233771751660582, "177": 0.6462339133776155, "345": -0.16155847834440387, "218": 0.16155847834440387, "299": 0.16155847834440387, "87": 0.20194809793050483, "393": -0.28272733710270676, "486": 0.08077923917220194, "183": 0.24233771751660582, "175": 0.08077923917220194, "363": -0.08077923917220194, "193": -0.12116885875830291, "96": 0.04038961958610097, "5": -0.04038961958610097, "498": 0.04038961958610097, "72": 0.08077923917220194, "106": -0.12116885875830291, "266": -0.24233771751660582, "265": 0.04038961958610097, "211": -0.04038961958610097, "424": -0.08077923917220194, "246": -0.08077923917220194, "465": 0.12116885875830291, "55": 0.04038961958610097, "426": 0.04038961958610097, "83": 0.04038961958610097, "457": -0.04038961958610097, "381": -0.04038961958610097, "200": 0.28272733710270676, "314": -0.04038961958610097 } }, { "id": "everett_engine.py#4", "path": "everett_engine.py", "chunk": 4, "text": ")) future_states = _copy_correlated_rows( rng, future_states, target_state, config.distractor_similarity, config.target_index ) query_target = _corrupt_query_target( rng, target_state, config.target_corruption, config.domain ) accumulator = cause * state_0 * action_role * effect * np.sum(actions * future_states, axis=0) if config.bundle_mode == \"majority\": accumulator = np.sign(accumulator) accumulator[accumulator == 0.0] = 1.0 world = accumulator query = cause * state_0 * effect * query_target recovered = world * query * action_role resonances = np.einsum(\"ij,j->i\", actions, recovered) / d target_resonance = float(resonances[config.target_index]) background = np.delete(resonances, config.target_index) max_background = float(np.max(background)) if len(background) else 0.0 snr = float(target_resonance / max(abs(max_background), 1e-12)) ranking = np.argsort(resonances)[::-1] top1 = int(ranking[0]) top5 = set(int(idx) for idx in ranking[: min(5, len(ranking))]) return TrialResult( domain=config.domain, bundle_mode=config.bundle_mode, dimension=d, branches=n, target_index=config.target_index, distractor_similarity=config.distractor_similarity, target_corruption=config.target_corruption", "tokens": 119, "vector": { "393": -0.2776468880296485, "486": 0.046274481338274755, "177": 0.09254896267654951, "87": 0.09254896267654951, "183": 0.555293776059297, "175": 0.13882344401482424, "363": -0.32392136936792326, "193": -0.09254896267654951, "96": 0.046274481338274755, "5": -0.13882344401482424, "498": 0.13882344401482424, "230": -0.2776468880296485, "106": -0.09254896267654951, "218": 0.09254896267654951, "345": -0.09254896267654951, "476": 0.046274481338274755, "266": -0.2776468880296485, "265": 0.046274481338274755, "299": 0.09254896267654951, "211": -0.046274481338274755, "403": -0.09254896267654951, "320": 0.13882344401482424, "290": 0.046274481338274755, "267": -0.046274481338274755, "72": 0.09254896267654951, "424": -0.09254896267654951, "246": -0.09254896267654951, "55": 0.18509792535309902, "83": 0.046274481338274755, "457": -0.046274481338274755, "187": 0.09254896267654951, "374": 0.13882344401482424, "353": -0.046274481338274755, "319": -0.09254896267654951, "411": -0.09254896267654951, "113": 0.09254896267654951, "381": -0.046274481338274755, "24": -0.046274481338274755, "120": 0.046274481338274755, "494": -0.18509792535309902, "338": -0.046274481338274755, "21": -0.046274481338274755, "109": 0.09254896267654951, "110": -0.046274481338274755, "198": 0.09254896267654951, "16": 0.046274481338274755, "344": -0.046274481338274755, "287": -0.046274481338274755, "369": 0.046274481338274755, "328": -0.046274481338274755 } }, { "id": "everett_engine.py#5", "path": "everett_engine.py", "chunk": 5, "text": "_mode, dimension=d, branches=n, target_index=config.target_index, distractor_similarity=config.distractor_similarity, target_corruption=config.target_corruption, target_resonance=target_resonance, max_background_resonance=max_background, snr=snr, top1_index=top1, top1_correct=top1 == config.target_index, top5_correct=config.target_index in top5, ) def summarize_result(result: TrialResult) -> str: return ( f\"{result.domain:7s} {result.bundle_mode:8s} \" f\"D={result.dimension:<6d} N={result.branches:<5d} \" f\"target={result.target_resonance: .4f} \" f\"bg_max={result.max_background_resonance: .4f} \" f\"snr={result.snr: .2f} \" f\"top1={result.top1_index:<5d} \" f\"ok={str(result.top1_correct):5s}\" )", "tokens": 68, "vector": { "510": 0.06375767130633382, "328": -0.12751534261266764, "476": -0.12751534261266764, "363": -0.31878835653166915, "183": 0.31878835653166915, "175": 0.12751534261266764, "5": -0.12751534261266764, "187": 0.19127301391900148, "317": -0.12751534261266764, "319": -0.06375767130633382, "24": -0.2550306852253353, "383": 0.12751534261266764, "21": -0.19127301391900148, "45": -0.12751534261266764, "182": 0.06375767130633382, "344": -0.06375767130633382, "459": 0.06375767130633382, "467": -0.06375767130633382, "246": -0.6375767130633383, "424": -0.06375767130633382, "396": 0.12751534261266764, "369": 0.06375767130633382, "498": 0.19127301391900148, "479": 0.06375767130633382, "320": 0.06375767130633382, "165": 0.06375767130633382, "132": 0.06375767130633382, "52": 0.06375767130633382, "361": -0.12751534261266764, "12": -0.06375767130633382, "312": -0.06375767130633382, "294": 0.06375767130633382, "403": -0.06375767130633382 } }, { "id": "memory_fabric.py#0", "path": "memory_fabric.py", "chunk": 0, "text": "\"\"\"Memory-native compute fabric primitives. This module implements a compact algebraic runtime demo for: - Fractal Memory Pyramid tiering and resonant retrieval. - Provenance-bound HMKs with actor/trust/tool unbinding. - Resonant Execution Mesh routing. The implementation is deliberately small and deterministic so benchmark output can be audited. It is a proof harness, not a production memory subsystem. \"\"\" from __future__ import annotations import hashlib from dataclasses import dataclass from typing import Dict, Iterable, List, Optional, Sequence, Tuple import numpy as np def _seed(label: str) -> int: digest = hashlib.sha256(label.encode(\"utf-8\")).digest() return int.from_bytes(digest[:8], \"little\", signed=False) class ComplexSpace: def __init__(self, dimension: int = 4096): self.dimension = dimension self._cache: Dict[str, np.ndarray] = {} def vector(self, label: str) -> np.ndarray: if label not in self._cache: rng = np.random.default_rng(_seed(label)) phases = rng.uniform(0.0, 2.0 * np.pi, size=self.dimension) self._cache[label] = np.exp(1j * phases).astype(np.complex128) return self._cache[label] def bind(self, *vectors: np.ndarray) -> np.ndarray: out = np.ones(self.dimension,", "tokens": 166, "vector": { "192": -0.12587720374450337, "446": -0.041959067914834454, "176": 0.041959067914834454, "173": 0.041959067914834454, "340": -0.041959067914834454, "286": -0.041959067914834454, "274": 0.041959067914834454, "352": -0.041959067914834454, "438": 0.08391813582966891, "53": -0.041959067914834454, "217": 0.25175440748900674, "298": -0.041959067914834454, "16": 0.0, "96": 0.041959067914834454, "34": 0.041959067914834454, "320": 0.041959067914834454, "354": -0.08391813582966891, "487": 0.08391813582966891, "366": -0.041959067914834454, "150": 0.041959067914834454, "94": 0.08391813582966891, "262": 0.041959067914834454, "336": 0.041959067914834454, "247": 0.041959067914834454, "380": -0.041959067914834454, "133": 0.08391813582966891, "164": 0.041959067914834454, "211": -0.041959067914834454, "252": 0.08391813582966891, "441": -0.041959067914834454, "474": 0.041959067914834454, "506": -0.08391813582966891, "256": 0.041959067914834454, "385": -0.041959067914834454, "267": -0.041959067914834454, "417": 0.041959067914834454, "270": 0.041959067914834454, "224": 0.041959067914834454, "159": -0.041959067914834454, "326": -0.041959067914834454, "433": -0.041959067914834454, "42": -0.041959067914834454, "449": 0.041959067914834454, "329": 0.041959067914834454, "293": -0.08391813582966891, "171": 0.041959067914834454, "102": -0.041959067914834454, "373": -0.16783627165933782, "201": -0.041959067914834454, "297": 0.041959067914834454, "45": 0.08391813582966891, "278": 0.041959067914834454, "296": 0.041959067914834454, "504": -0.041959067914834454, "106": -0.08391813582966891, "163": 0.041959067914834454, "492": -0.041959067914834454, "447": 0.041959067914834454, "251": -0.041959067914834454, "371": 0.041959067914834454, "500": -0.041959067914834454, "266": -0.4195906791483446, "459": 0.33567254331867563, "64": -0.08391813582966891, "26": -0.2937134754038412, "396": 0.12587720374450337, "109": 0.12587720374450337, "11": -0.12587720374450337, "349": 0.041959067914834454, "439": -0.041959067914834454, "153": -0.041959067914834454, "369": 0.08391813582966891, "502": -0.041959067914834454, "24": -0.041959067914834454, "74": -0.041959067914834454, "264": 0.041959067914834454, "355": 0.041959067914834454, "6": 0.4195906791483446, "328": -0.2097953395741723, "139": -0.041959067914834454, "376": 0.16783627165933782, "432": 0.041959067914834454, "403": -0.041959067914834454, "344": -0.041959067914834454, "177": 0.08391813582966891, "420": -0.041959067914834454, "279": -0.041959067914834454, "390": 0.08391813582966891, "495": -0.041959067914834454, "204": -0.041959067914834454, "221": 0.041959067914834454, "347": -0.041959067914834454, "499": -0.041959067914834454, "496": 0.041959067914834454, "41": -0.041959067914834454, "118": -0.041959067914834454, "468": 0.041959067914834454 } }, { "id": "memory_fabric.py#1", "path": "memory_fabric.py", "chunk": 1, "text": "e[label] = np.exp(1j * phases).astype(np.complex128) return self._cache[label] def bind(self, *vectors: np.ndarray) -> np.ndarray: out = np.ones(self.dimension, dtype=np.complex128) for vector in vectors: out *= vector return out def unbind(self, vector: np.ndarray, role: np.ndarray) -> np.ndarray: return vector * np.conj(role) def bundle(self, vectors: Iterable[np.ndarray]) -> np.ndarray: items = list(vectors) if not items: return np.zeros(self.dimension, dtype=np.complex128) return np.sum(items, axis=0) / max(len(items), 1) def resonance(self, left: np.ndarray, right: np.ndarray) -> float: denom = np.linalg.norm(left) * np.linalg.norm(right) if denom == 0.0: return 0.0 return float(np.real(np.vdot(right, left)) / denom) @dataclass(frozen=True) class MemoryRecord: key: str value: str actor: str trust: str tool: str @dataclass(frozen=True) class RetrievalResult: query: str value: str tier: str confidence: float repaired: bool success: bool def as_dict(self) -> Dict[str, object]: return { \"query\": self.query, \"value\": self.value, \"tier\": self.tier, \"confidence\": self.confidence, \"repaired\": self.repaired, \"success\": self.success, } class FractalMemoryPyramid: def __init__( self, spa", "tokens": 177, "vector": { "26": -0.058050322416014204, "266": -0.6095283853681491, "221": 0.029025161208007102, "94": 0.029025161208007102, "390": 0.029025161208007102, "347": -0.029025161208007102, "499": -0.0870754836240213, "369": 0.23220128966405681, "6": 0.4353774181201065, "459": 0.2031761284560497, "496": 0.029025161208007102, "41": -0.058050322416014204, "376": 0.2612264508720639, "118": -0.0870754836240213, "468": 0.029025161208007102, "328": -0.058050322416014204, "182": -0.058050322416014204, "16": 0.0, "432": 0.11610064483202841, "344": -0.029025161208007102, "383": 0.029025161208007102, "75": 0.058050322416014204, "465": 0.029025161208007102, "30": -0.029025161208007102, "95": 0.11610064483202841, "163": 0.029025161208007102, "403": -0.058050322416014204, "293": -0.029025161208007102, "46": 0.029025161208007102, "265": 0.029025161208007102, "211": -0.029025161208007102, "411": -0.029025161208007102, "113": 0.029025161208007102, "310": -0.0870754836240213, "39": 0.0870754836240213, "374": 0.0870754836240213, "63": -0.0870754836240213, "458": -0.058050322416014204, "115": 0.058050322416014204, "426": 0.029025161208007102, "21": -0.029025161208007102, "296": 0.058050322416014204, "511": 0.1451258060400355, "181": 0.058050322416014204, "264": 0.0870754836240213, "485": 0.029025161208007102, "44": 0.029025161208007102, "396": 0.2612264508720639, "205": 0.11610064483202841, "336": 0.029025161208007102, "247": 0.029025161208007102, "380": -0.029025161208007102, "409": 0.029025161208007102, "424": -0.0870754836240213, "226": 0.0870754836240213, "315": 0.0870754836240213, "183": -0.058050322416014204, "174": -0.0870754836240213, "151": 0.029025161208007102, "106": -0.029025161208007102, "356": 0.029025161208007102, "373": -0.029025161208007102, "257": 0.029025161208007102 } }, { "id": "memory_fabric.py#2", "path": "memory_fabric.py", "chunk": 2, "text": "lue, \"tier\": self.tier, \"confidence\": self.confidence, \"repaired\": self.repaired, \"success\": self.success, } class FractalMemoryPyramid: def __init__( self, space: ComplexSpace, tier0_capacity: int = 2, tier1_capacity: int = 2, tier2_capacity: int = 2, threshold: float = 0.18, ): self.space = space self.tier0_capacity = tier0_capacity self.tier1_capacity = tier1_capacity self.tier2_capacity = tier2_capacity self.threshold = threshold self.tier0: List[MemoryRecord] = [] self.tier1: List[MemoryRecord] = [] self.tier2_records: List[MemoryRecord] = [] self.tier3_records: List[MemoryRecord] = [] self.hkvc = np.zeros(space.dimension, dtype=np.complex128) self.world_vector = np.zeros(space.dimension, dtype=np.complex128) self.exact_sidecar: Dict[str, MemoryRecord] = {} def _entry_vector(self, record: MemoryRecord) -> np.ndarray: return self.space.bind(self.space.vector(f\"key:{record.key}\"), self.space.vector(f\"value:{record.value}\")) def add(self, record: MemoryRecord) -> None: self.exact_sidecar[record.key] = record self.tier0.append(record) while len(self.tier0) > self.tier0_capacity: self.tier1.append(self.tier0.pop(0)) while len(self.tier1) > self.tier1_capacity: moved = self.tier1.po", "tokens": 143, "vector": { "245": 0.027126281959038646, "226": 0.05425256391807729, "6": 0.840914740730198, "511": 0.05425256391807729, "315": 0.05425256391807729, "174": -0.10850512783615458, "264": 0.027126281959038646, "356": 0.027126281959038646, "459": 0.08137884587711594, "373": -0.027126281959038646, "319": 0.21701025567230917, "355": 0.027126281959038646, "243": -0.10850512783615458, "109": 0.08137884587711594, "398": -0.10850512783615458, "237": 0.08137884587711594, "73": -0.08137884587711594, "374": 0.027126281959038646, "334": 0.027126281959038646, "25": 0.10850512783615458, "163": 0.10850512783615458, "485": 0.18988397371327054, "444": -0.10850512783615458, "178": 0.027126281959038646, "263": -0.027126281959038646, "445": 0.027126281959038646, "266": -0.13563140979519323, "46": 0.05425256391807729, "328": -0.05425256391807729, "182": -0.05425256391807729, "499": -0.05425256391807729, "361": -0.027126281959038646, "271": -0.05425256391807729, "106": -0.027126281959038646, "396": 0.027126281959038646, "482": 0.027126281959038646, "112": 0.18988397371327054, "376": 0.027126281959038646, "369": 0.027126281959038646, "496": 0.027126281959038646, "432": 0.05425256391807729, "44": 0.08137884587711594, "205": 0.05425256391807729, "126": -0.027126281959038646, "276": 0.027126281959038646, "7": 0.05425256391807729, "113": 0.05425256391807729, "222": 0.027126281959038646, "350": -0.027126281959038646, "353": -0.027126281959038646 } }, { "id": "memory_fabric.py#3", "path": "memory_fabric.py", "chunk": 3, "text": "end(record) while len(self.tier0) > self.tier0_capacity: self.tier1.append(self.tier0.pop(0)) while len(self.tier1) > self.tier1_capacity: moved = self.tier1.pop(0) self.tier2_records.append(moved) self.hkvc += self._entry_vector(moved) while len(self.tier2_records) > self.tier2_capacity: moved = self.tier2_records.pop(0) self.tier3_records.append(moved) self.world_vector += self._entry_vector(moved) self.hkvc -= self._entry_vector(moved) def _recover_from_vector(self, query: str, vector: np.ndarray, candidates: Sequence[MemoryRecord]) -> Tuple[Optional[MemoryRecord], float]: if not candidates: return None, 0.0 recovered = self.space.unbind(vector, self.space.vector(f\"key:{query}\")) scored = [ (record, self.space.resonance(recovered, self.space.vector(f\"value:{record.value}\"))) for record in candidates ] return max(scored, key=lambda item: item[1]) def retrieve(self, query: str) -> RetrievalResult: for record in reversed(self.tier0): if record.key == query: return RetrievalResult(query, record.value, \"tier0_exact\", 1.0, False, True) qvec = self.space.vector(f\"key:{query}\") tier1_scores = [(record, self.space.resonance(qvec, self.space.vector(f\"key:{record.key}\"))) for record in sel", "tokens": 156, "vector": { "54": 0.02797271194322297, "112": 0.27972711943222967, "7": 0.08391813582966891, "113": 0.08391813582966891, "6": 0.7832359344102431, "25": 0.08391813582966891, "243": -0.02797271194322297, "444": -0.08391813582966891, "174": -0.08391813582966891, "222": 0.08391813582966891, "398": -0.02797271194322297, "350": -0.19580898360256077, "178": 0.08391813582966891, "445": 0.05594542388644594, "482": 0.08391813582966891, "237": 0.02797271194322297, "263": -0.02797271194322297, "361": -0.05594542388644594, "459": 0.05594542388644594, "417": -0.02797271194322297, "424": -0.16783627165933782, "396": 0.05594542388644594, "432": 0.16783627165933782, "266": -0.02797271194322297, "376": 0.02797271194322297, "442": -0.08391813582966891, "447": 0.02797271194322297, "485": 0.05594542388644594, "251": -0.02797271194322297, "492": -0.02797271194322297, "374": 0.02797271194322297, "403": -0.05594542388644594, "293": -0.02797271194322297, "369": 0.08391813582966891, "276": 0.02797271194322297, "246": -0.05594542388644594, "319": 0.19580898360256077, "383": 0.02797271194322297, "44": 0.16783627165933782, "89": -0.05594542388644594, "41": 0.05594542388644594, "205": 0.08391813582966891, "16": 0.08391813582966891, "344": -0.08391813582966891, "411": -0.02797271194322297, "85": 0.02797271194322297, "330": -0.05594542388644594, "80": -0.02797271194322297, "409": 0.05594542388644594, "188": 0.02797271194322297, "170": 0.02797271194322297, "252": 0.02797271194322297, "181": 0.02797271194322297, "267": -0.05594542388644594, "281": -0.02797271194322297 } }, { "id": "memory_fabric.py#4", "path": "memory_fabric.py", "chunk": 4, "text": "e, True) qvec = self.space.vector(f\"key:{query}\") tier1_scores = [(record, self.space.resonance(qvec, self.space.vector(f\"key:{record.key}\"))) for record in self.tier1] if tier1_scores: record, confidence = max(tier1_scores, key=lambda item: item[1]) if confidence >= self.threshold and record.key == query: return RetrievalResult(query, record.value, \"tier1_sparse\", confidence, False, True) record, confidence = self._recover_from_vector(query, self.hkvc, self.tier2_records) if record is not None and confidence >= self.threshold and record.key == query: return RetrievalResult(query, record.value, \"tier2_hkvc\", confidence, False, True) record, confidence = self._recover_from_vector(query, self.world_vector, self.tier3_records) if record is not None and confidence >= self.threshold and record.key == query: return RetrievalResult(query, record.value, \"tier3_rsm\", confidence, False, True) repaired = self.exact_sidecar.get(query) if repaired is None: return RetrievalResult(query, \"\", \"miss\", 0.0, False, False) return RetrievalResult(query, repaired.value, \"geodesic_repair_loop\", confidence, True, True) @dataclass(frozen=True) class ProvenanceResult: actor: str authorized_actor: str actor_", "tokens": 147, "vector": { "181": 0.2219149187318845, "267": -0.06340426249482413, "6": 0.443829837463769, "319": 0.0951063937422362, "432": 0.06340426249482413, "44": 0.2219149187318845, "424": -0.3804255749689448, "281": -0.0951063937422362, "112": 0.443829837463769, "41": 0.0, "16": 0.031702131247412065, "344": -0.031702131247412065, "444": -0.031702131247412065, "403": -0.15851065623706034, "511": 0.34872344372153274, "411": -0.031702131247412065, "85": 0.031702131247412065, "330": -0.06340426249482413, "73": -0.0951063937422362, "354": -0.15851065623706034, "369": 0.15851065623706034, "409": 0.15851065623706034, "205": 0.12680852498964826, "89": -0.031702131247412065, "252": 0.15851065623706034, "417": -0.06340426249482413, "445": 0.031702131247412065, "178": 0.031702131247412065, "506": -0.0951063937422362, "293": -0.06340426249482413, "276": 0.0951063937422362, "239": 0.031702131247412065, "361": -0.031702131247412065, "263": -0.031702131247412065, "259": 0.031702131247412065, "315": 0.0951063937422362, "271": -0.031702131247412065, "128": -0.031702131247412065, "392": 0.031702131247412065, "296": 0.031702131247412065, "264": 0.031702131247412065, "464": -0.031702131247412065, "336": 0.031702131247412065, "396": 0.06340426249482413, "505": 0.031702131247412065, "37": 0.031702131247412065 } }, { "id": "memory_fabric.py#5", "path": "memory_fabric.py", "chunk": 5, "text": "t(query, repaired.value, \"geodesic_repair_loop\", confidence, True, True) @dataclass(frozen=True) class ProvenanceResult: actor: str authorized_actor: str actor_similarity: float trust_similarity: float accepted: bool def as_dict(self) -> Dict[str, object]: return { \"actor\": self.actor, \"authorized_actor\": self.authorized_actor, \"actor_similarity\": self.actor_similarity, \"trust_similarity\": self.trust_similarity, \"accepted\": self.accepted, } class ProvenanceHMK: def __init__(self, space: ComplexSpace): self.space = space self.roles = { \"CAUSE\": space.vector(\"role:cause\"), \"ACTOR\": space.vector(\"role:actor\"), \"TRUST\": space.vector(\"role:trust\"), \"TOOL\": space.vector(\"role:tool\"), } def encode(self, cause: str, actor: str, trust: str, tool: str) -> np.ndarray: return self.space.bundle( [ self.space.bind(self.roles[\"CAUSE\"], self.space.vector(f\"cause:{cause}\")), self.space.bind(self.roles[\"ACTOR\"], self.space.vector(f\"actor:{actor}\")), self.space.bind(self.roles[\"TRUST\"], self.space.vector(f\"trust:{trust}\")), self.space.bind(self.roles[\"TOOL\"], self.space.vector(f\"tool:{tool}\")), ] ) def verify( self, cv: np.ndarray, actual_actor: str, authorized_actor: str, required_trust: str, thresh", "tokens": 150, "vector": { "424": -0.027056689037447044, "315": 0.027056689037447044, "205": 0.027056689037447044, "392": 0.027056689037447044, "511": 0.05411337807489409, "181": 0.08117006711234113, "296": 0.027056689037447044, "264": 0.05411337807489409, "464": -0.027056689037447044, "336": 0.2435102013370234, "396": 0.2705668903744704, "505": 0.10822675614978818, "242": 0.08117006711234113, "374": 0.05411337807489409, "244": 0.08117006711234113, "7": 0.08117006711234113, "183": -0.027056689037447044, "459": 0.10822675614978818, "151": 0.027056689037447044, "6": 0.6493605368987291, "106": -0.1893968232621293, "41": 0.027056689037447044, "369": 0.05411337807489409, "252": 0.027056689037447044, "373": -0.027056689037447044, "319": 0.4329070245991527, "355": 0.027056689037447044, "358": -0.1352834451872352, "432": 0.21645351229957635, "75": 0.10822675614978818, "247": 0.16234013422468227, "380": -0.16234013422468227, "439": -0.027056689037447044, "266": -0.05411337807489409, "376": 0.05411337807489409, "30": -0.027056689037447044, "496": 0.10822675614978818, "417": -0.027056689037447044, "297": 0.027056689037447044, "117": -0.027056689037447044, "14": -0.027056689037447044, "398": 0.027056689037447044 } }, { "id": "memory_fabric.py#6", "path": "memory_fabric.py", "chunk": 6, "text": "f.roles[\"TOOL\"], self.space.vector(f\"tool:{tool}\")), ] ) def verify( self, cv: np.ndarray, actual_actor: str, authorized_actor: str, required_trust: str, threshold: float = 0.36, ) -> ProvenanceResult: actor_recovered = self.space.unbind(cv, self.roles[\"ACTOR\"]) trust_recovered = self.space.unbind(cv, self.roles[\"TRUST\"]) actor_similarity = self.space.resonance(actor_recovered, self.space.vector(f\"actor:{authorized_actor}\")) trust_similarity = self.space.resonance(trust_recovered, self.space.vector(f\"trust:{required_trust}\")) return ProvenanceResult( actor=actual_actor, authorized_actor=authorized_actor, actor_similarity=actor_similarity, trust_similarity=trust_similarity, accepted=actor_similarity >= threshold and trust_similarity >= threshold, ) @dataclass(frozen=True) class Node: name: str hardware: str latency: str tool: str @dataclass(frozen=True) class RoutingResult: task: str selected_node: str expected_node: str resonance: float success: bool def as_dict(self) -> Dict[str, object]: return { \"task\": self.task, \"selected_node\": self.selected_node, \"expected_node\": self.expected_node, \"resonance\": self.resonance, \"success\": self.success, } class ResonantExecutionMesh: def __in", "tokens": 129, "vector": { "358": -0.11396057645963795, "380": -0.15194743527951726, "6": 0.607789741118069, "319": 0.2279211529192759, "432": 0.11396057645963795, "459": 0.11396057645963795, "417": -0.037986858819879316, "297": 0.11396057645963795, "266": -0.037986858819879316, "376": 0.037986858819879316, "117": -0.07597371763975863, "396": 0.4178554470186725, "505": 0.15194743527951726, "14": -0.07597371763975863, "73": -0.11396057645963795, "374": 0.11396057645963795, "464": -0.07597371763975863, "270": 0.18993429409939658, "383": 0.07597371763975863, "336": 0.11396057645963795, "407": 0.07597371763975863, "247": 0.07597371763975863, "242": 0.15194743527951726, "41": 0.2279211529192759, "244": 0.15194743527951726, "369": 0.07597371763975863, "7": 0.037986858819879316, "354": -0.037986858819879316, "296": 0.07597371763975863, "511": 0.07597371763975863, "181": 0.07597371763975863, "264": 0.11396057645963795, "84": 0.037986858819879316, "386": 0.07597371763975863, "38": -0.037986858819879316, "477": 0.037986858819879316, "145": 0.11396057645963795, "355": -0.11396057645963795, "174": -0.11396057645963795, "183": -0.037986858819879316, "151": 0.037986858819879316, "106": -0.037986858819879316, "471": -0.037986858819879316 } }, { "id": "memory_fabric.py#7", "path": "memory_fabric.py", "chunk": 7, "text": "ed_node\": self.selected_node, \"expected_node\": self.expected_node, \"resonance\": self.resonance, \"success\": self.success, } class ResonantExecutionMesh: def __init__(self, space: ComplexSpace, nodes: Sequence[Node]): self.space = space self.nodes = list(nodes) def capability_vector(self, node: Node) -> np.ndarray: return self.space.bundle( [ self.space.vector(f\"hardware:{node.hardware}\"), self.space.vector(f\"latency:{node.latency}\"), self.space.vector(f\"tool:{node.tool}\"), ] ) def task_vector(self, hardware: str, latency: str, tool: str) -> np.ndarray: return self.space.bundle( [ self.space.vector(f\"hardware:{hardware}\"), self.space.vector(f\"latency:{latency}\"), self.space.vector(f\"tool:{tool}\"), ] ) def route(self, task: str, hardware: str, latency: str, tool: str, expected_node: str) -> RoutingResult: vector = self.task_vector(hardware, latency, tool) scores = [ (node, self.space.resonance(vector, self.capability_vector(node))) for node in self.nodes ] node, score = max(scores, key=lambda item: item[1]) return RoutingResult(task, node.name, expected_node, score, node.name == expected_node)", "tokens": 140, "vector": { "290": 0.03152833305597158, "6": 0.6936233272313749, "145": 0.03152833305597158, "355": -0.12611333222388632, "41": 0.09458499916791475, "174": -0.06305666611194316, "264": 0.03152833305597158, "471": -0.03152833305597158, "459": 0.12611333222388632, "373": -0.03152833305597158, "319": 0.15764166527985793, "325": -0.12611333222388632, "447": 0.03152833305597158, "84": 0.378339996671659, "163": 0.03152833305597158, "254": 0.06305666611194316, "266": -0.06305666611194316, "376": 0.06305666611194316, "369": 0.09458499916791475, "30": -0.06305666611194316, "432": 0.25222666444777264, "38": -0.2206983313918011, "380": -0.2206983313918011, "56": 0.06305666611194316, "396": 0.25222666444777264, "138": 0.03152833305597158, "270": 0.06305666611194316, "477": 0.06305666611194316, "422": -0.06305666611194316, "16": 0.03152833305597158, "344": -0.03152833305597158, "421": 0.06305666611194316, "411": -0.03152833305597158, "44": 0.03152833305597158, "85": 0.03152833305597158, "330": -0.06305666611194316, "386": 0.06305666611194316 } }, { "id": "memory_fabric.py#8", "path": "memory_fabric.py", "chunk": 8, "text": "(task, node.name, expected_node, score, node.name == expected_node)", "tokens": 8, "vector": { "270": 0.2672612419124244, "84": 0.5345224838248488, "386": 0.5345224838248488, "355": -0.5345224838248488, "421": 0.2672612419124244 } }, { "id": "ops_agents.py#0", "path": "ops_agents.py", "chunk": 0, "text": "\"\"\"Agent definitions for the HOPS organizer dashboard.\"\"\" from __future__ import annotations import argparse import json from pathlib import Path ROOT = Path(__file__).resolve().parent AGENTS = [ { \"name\": \"atlas-catalog-agent\", \"model\": \"@cf/moonshotai/kimi-k2.6\", \"purpose\": \"Maintain the Cloudflare/GitHub/local asset catalog and detect stale deployments.\", \"system_prompt\": \"You are Atlas, an asset catalog agent. Use the RAG index and asset catalog to summarize deployed assets, missing metadata, ownership, URLs, and freshness. Do not invent live Cloudflare resources that are not in the catalog.\", \"tools\": [\"asset_catalog\", \"rag_search\", \"cloudflare_refresh\"], }, { \"name\": \"pilot-ops-agent\", \"model\": \"@cf/moonshotai/kimi-k2.6\", \"purpose\": \"Track paid pilot readiness, test status, buyer-facing proof artifacts, and next actions.\", \"system_prompt\": \"You are Pilot Ops. Convert HOPS proof artifacts into practical pilot todo lists, acceptance metrics, and customer-ready summaries. Keep claims careful and evidence-backed.\", \"tools\": [\"pilot_manifest\", \"rag_search\", \"todo_board\"], }, { \"name\": \"research-synthesis-agent\", \"model\": \"@cf/moonshotai/kimi-k2.6\", \"purpose\": \"Organize research no", "tokens": 153, "vector": { "212": -0.27482485496465636, "2": -0.05496497099293127, "16": 0.05496497099293127, "441": -0.21985988397172507, "152": 0.10992994198586253, "267": -0.05496497099293127, "358": 0.05496497099293127, "373": -0.10992994198586253, "201": -0.05496497099293127, "217": 0.1648949129787938, "297": 0.05496497099293127, "427": 0.05496497099293127, "258": 0.05496497099293127, "64": 0.05496497099293127, "416": -0.05496497099293127, "328": -0.05496497099293127, "253": 0.05496497099293127, "484": -0.21985988397172507, "140": -0.05496497099293127, "386": 0.1648949129787938, "124": 0.10992994198586253, "357": 0.27482485496465636, "147": 0.10992994198586253, "371": 0.1648949129787938, "228": -0.1648949129787938, "257": -0.1648949129787938, "129": 0.1648949129787938, "401": -0.05496497099293127, "115": 0.10992994198586253, "192": 0.05496497099293127, "293": -0.1648949129787938, "469": 0.1648949129787938, "354": -0.3297898259575876, "207": -0.05496497099293127, "1": 0.0, "66": 0.10992994198586253, "443": 0.10992994198586253, "186": 0.1648949129787938, "234": -0.05496497099293127, "419": 0.05496497099293127, "163": 0.05496497099293127, "27": -0.05496497099293127, "102": -0.1648949129787938, "442": -0.05496497099293127, "193": -0.05496497099293127, "395": 0.05496497099293127, "255": -0.05496497099293127, "69": -0.05496497099293127, "300": 0.05496497099293127, "467": -0.05496497099293127, "428": 0.05496497099293127, "286": -0.05496497099293127, "36": 0.05496497099293127, "321": -0.05496497099293127, "398": 0.05496497099293127, "344": -0.05496497099293127, "505": -0.10992994198586253, "499": 0.05496497099293127, "494": 0.10992994198586253, "394": -0.05496497099293127, "7": -0.1648949129787938, "169": 0.10992994198586253, "43": -0.05496497099293127, "38": -0.05496497099293127, "14": 0.05496497099293127, "159": 0.05496497099293127, "365": 0.05496497099293127, "435": 0.05496497099293127, "449": 0.1648949129787938, "15": 0.10992994198586253, "454": 0.05496497099293127, "299": 0.05496497099293127, "452": 0.05496497099293127, "107": 0.05496497099293127, "302": -0.05496497099293127, "352": -0.05496497099293127, "76": -0.05496497099293127, "23": -0.05496497099293127, "182": -0.05496497099293127, "434": -0.05496497099293127, "198": -0.05496497099293127, "364": 0.05496497099293127, "166": 0.05496497099293127, "25": -0.05496497099293127, "238": 0.05496497099293127, "340": 0.05496497099293127, "336": 0.05496497099293127, "47": 0.05496497099293127, "418": 0.05496497099293127, "438": -0.05496497099293127 } }, { "id": "ops_agents.py#1", "path": "ops_agents.py", "chunk": 1, "text": ": [\"pilot_manifest\", \"rag_search\", \"todo_board\"], }, { \"name\": \"research-synthesis-agent\", \"model\": \"@cf/moonshotai/kimi-k2.6\", \"purpose\": \"Organize research notes, mathematical claims, benchmark evidence, and claim discipline.\", \"system_prompt\": \"You are Research Synthesis. Connect formulas, code, benchmark outputs, and caveats. Separate demonstrated evidence from hypotheses and commercial positioning.\", \"tools\": [\"rag_search\", \"benchmark_reports\"], }, { \"name\": \"memory-fabric-agent\", \"model\": \"@cf/moonshotai/kimi-k2.6\", \"purpose\": \"Use the HOPS MCP memory fabric to remember decisions and retrieve prior context with provenance.\", \"system_prompt\": \"You are Memory Fabric. Store concise decisions with actor/trust/tool provenance, retrieve project state, and suggest what should be promoted into durable memory.\", \"tools\": [\"hops_remember\", \"hops_retrieve\", \"hops_verify_provenance\"], }, ] def write_agents(out_dir: Path) -> dict[str, object]: payload = { \"provider\": { \"name\": \"Cloudflare Workers AI\", \"model\": \"@cf/moonshotai/kimi-k2.6\", \"binding\": \"AI\", \"model_env\": \"CF_AI_MODEL\", \"status\": \"configured for Cloudflare AI inference via env.AI.run\", }, \"agents\": AGENTS, } out_dir.mkdir(pare", "tokens": 137, "vector": { "336": 0.12194215216993846, "494": 0.12194215216993846, "47": 0.06097107608496923, "386": 0.12194215216993846, "102": -0.24388430433987693, "418": 0.12194215216993846, "212": -0.1829132282549077, "147": 0.1829132282549077, "371": 0.1829132282549077, "228": -0.1829132282549077, "484": -0.1829132282549077, "257": -0.1829132282549077, "129": 0.12194215216993846, "438": -0.06097107608496923, "171": 0.12194215216993846, "216": -0.06097107608496923, "166": 0.06097107608496923, "270": 0.12194215216993846, "238": 0.12194215216993846, "354": -0.30485538042484617, "477": 0.06097107608496923, "66": 0.12194215216993846, "443": 0.12194215216993846, "186": 0.12194215216993846, "346": 0.06097107608496923, "85": -0.06097107608496923, "86": 0.06097107608496923, "123": 0.06097107608496923, "233": -0.06097107608496923, "384": 0.0, "333": 0.06097107608496923, "373": -0.06097107608496923, "318": -0.06097107608496923, "208": 0.06097107608496923, "430": -0.06097107608496923, "505": -0.12194215216993846, "379": -0.06097107608496923, "192": -0.24388430433987693, "173": 0.1829132282549077, "419": 0.06097107608496923, "441": -0.06097107608496923, "152": 0.06097107608496923, "16": 0.0, "309": 0.06097107608496923, "305": -0.12194215216993846, "80": -0.12194215216993846, "229": -0.06097107608496923, "490": -0.06097107608496923, "262": 0.12194215216993846, "150": 0.12194215216993846, "487": -0.06097107608496923, "247": 0.12194215216993846, "380": -0.06097107608496923, "36": 0.06097107608496923, "75": 0.06097107608496923, "220": 0.06097107608496923, "116": 0.06097107608496923, "504": 0.06097107608496923, "326": -0.06097107608496923, "308": -0.06097107608496923, "107": 0.06097107608496923, "84": -0.06097107608496923, "178": -0.06097107608496923, "459": 0.06097107608496923, "205": 0.06097107608496923, "153": -0.12194215216993846, "416": -0.06097107608496923, "106": -0.06097107608496923, "396": 0.06097107608496923, "41": 0.06097107608496923, "291": -0.06097107608496923, "92": 0.06097107608496923, "115": 0.12194215216993846, "493": 0.06097107608496923, "50": 0.24388430433987693, "9": -0.06097107608496923, "403": -0.06097107608496923, "7": 0.06097107608496923, "288": -0.06097107608496923, "402": -0.06097107608496923, "77": -0.06097107608496923, "439": -0.06097107608496923, "172": -0.06097107608496923, "140": -0.12194215216993846, "405": 0.06097107608496923, "362": 0.06097107608496923 } }, { "id": "ops_agents.py#2", "path": "ops_agents.py", "chunk": 2, "text": "k2.6\", \"binding\": \"AI\", \"model_env\": \"CF_AI_MODEL\", \"status\": \"configured for Cloudflare AI inference via env.AI.run\", }, \"agents\": AGENTS, } out_dir.mkdir(parents=True, exist_ok=True) (out_dir / \"kimi_agents.json\").write_text(json.dumps(payload, indent=2), encoding=\"utf-8\") return payload def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--out-dir\", type=Path, default=ROOT / \"results\" / \"ops_dashboard\") args = parser.parse_args() payload = write_agents(args.out_dir) print(f\"wrote {len(payload['agents'])} Kimi agent specs\") print(f\"wrote {args.out_dir / 'kimi_agents.json'}\") if __name__ == \"__main__\": main()", "tokens": 78, "vector": { "257": -0.08111071056538127, "384": -0.08111071056538127, "50": 0.24333213169614382, "9": -0.08111071056538127, "403": -0.16222142113076254, "7": 0.08111071056538127, "288": -0.08111071056538127, "16": 0.08111071056538127, "115": 0.08111071056538127, "402": -0.08111071056538127, "77": -0.08111071056538127, "439": -0.08111071056538127, "172": -0.08111071056538127, "140": -0.24333213169614382, "153": -0.40555355282690636, "405": 0.08111071056538127, "108": -0.08111071056538127, "181": 0.16222142113076254, "495": 0.08111071056538127, "249": -0.16222142113076254, "258": 0.24333213169614382, "230": -0.08111071056538127, "336": 0.08111071056538127, "291": -0.3244428422615251, "383": 0.08111071056538127, "232": -0.08111071056538127, "369": 0.08111071056538127, "459": 0.08111071056538127, "13": -0.16222142113076254, "276": 0.08111071056538127, "433": -0.24333213169614382, "427": 0.08111071056538127, "271": 0.08111071056538127, "201": -0.08111071056538127, "208": 0.08111071056538127, "475": -0.08111071056538127, "118": -0.08111071056538127, "43": -0.08111071056538127, "275": -0.08111071056538127, "416": -0.08111071056538127, "55": 0.08111071056538127, "328": -0.08111071056538127, "448": -0.08111071056538127, "42": 0.08111071056538127, "265": 0.24333213169614382, "322": 0.08111071056538127, "205": 0.08111071056538127, "462": 0.16222142113076254, "346": -0.16222142113076254, "113": 0.08111071056538127, "484": -0.08111071056538127, "212": -0.08111071056538127, "120": -0.08111071056538127, "34": -0.08111071056538127, "333": 0.08111071056538127 } }, { "id": "ops_catalog.py#0", "path": "ops_catalog.py", "chunk": 0, "text": "\"\"\"Catalog local, GitHub, and Cloudflare-facing assets for the HOPS ops dashboard.\"\"\" from __future__ import annotations import argparse import json import os import subprocess import time from pathlib import Path from typing import Any, Dict, List ROOT = Path(__file__).resolve().parent def run_command(args: list[str], timeout: int = 20) -> dict[str, object]: env = { **os.environ, \"WRANGLER_LOG_PATH\": str(ROOT / \"results\" / \"ops_dashboard\" / \"wrangler-logs\"), } started = time.perf_counter() try: completed = subprocess.run( args, cwd=str(ROOT), env=env, capture_output=True, text=True, timeout=timeout, check=False, ) return { \"command\": args, \"returncode\": completed.returncode, \"seconds\": time.perf_counter() - started, \"stdout\": completed.stdout.strip(), \"stderr\": completed.stderr.strip(), \"success\": completed.returncode == 0, } except Exception as exc: return { \"command\": args, \"returncode\": -1, \"seconds\": time.perf_counter() - started, \"stdout\": \"\", \"stderr\": str(exc), \"success\": False, } def list_files() -> list[dict[str, object]]: assets = [] for path in sorted(ROOT.rglob(\"*\")): if not path.is_file(): continue rel = path.relative_to(ROOT) parts = rel.parts if parts[0] in {\".git\",", "tokens": 151, "vector": { "357": 0.09889363528682975, "293": -0.09889363528682975, "192": 0.049446817643414874, "354": -0.049446817643414874, "115": 0.049446817643414874, "435": 0.049446817643414874, "395": 0.09889363528682975, "16": 0.09889363528682975, "441": -0.049446817643414874, "152": 0.049446817643414874, "169": 0.049446817643414874, "358": 0.049446817643414874, "373": -0.14834045293024462, "201": -0.049446817643414874, "217": 0.34612772350390414, "297": 0.049446817643414874, "427": 0.049446817643414874, "258": 0.049446817643414874, "132": 0.09889363528682975, "503": -0.049446817643414874, "51": -0.1977872705736595, "64": 0.049446817643414874, "416": -0.24723408821707438, "504": -0.049446817643414874, "470": -0.14834045293024462, "106": -0.14834045293024462, "163": 0.14834045293024462, "328": -0.24723408821707438, "253": 0.049446817643414874, "484": -0.049446817643414874, "459": 0.09889363528682975, "430": 0.049446817643414874, "265": 0.1977872705736595, "396": 0.29668090586048923, "109": 0.049446817643414874, "245": 0.049446817643414874, "41": 0.09889363528682975, "439": -0.14834045293024462, "76": -0.049446817643414874, "448": -0.049446817643414874, "42": 0.049446817643414874, "182": -0.049446817643414874, "408": -0.09889363528682975, "259": 0.14834045293024462, "211": -0.14834045293024462, "30": -0.049446817643414874, "333": 0.24723408821707438, "172": -0.049446817643414874, "367": -0.049446817643414874, "116": -0.049446817643414874, "181": 0.09889363528682975, "32": -0.049446817643414874, "252": 0.09889363528682975, "369": 0.09889363528682975, "93": 0.09889363528682975, "306": -0.1977872705736595, "89": -0.09889363528682975, "99": -0.14834045293024462, "382": 0.14834045293024462, "174": -0.09889363528682975, "177": -0.049446817643414874, "33": 0.049446817643414874, "500": -0.049446817643414874, "248": 0.09889363528682975, "166": -0.049446817643414874, "344": -0.09889363528682975, "219": 0.049446817643414874, "509": 0.049446817643414874, "403": -0.09889363528682975, "97": 0.049446817643414874, "226": 0.049446817643414874, "104": -0.09889363528682975, "496": 0.049446817643414874, "472": 0.14834045293024462, "154": -0.049446817643414874 } }, { "id": "ops_catalog.py#1", "path": "ops_catalog.py", "chunk": 1, "text": "bject]]: assets = [] for path in sorted(ROOT.rglob(\"*\")): if not path.is_file(): continue rel = path.relative_to(ROOT) parts = rel.parts if parts[0] in {\".git\", \"backups\", \".pycache\"}: continue if \".mplconfig\" in parts: continue assets.append( { \"path\": str(rel), \"bytes\": path.stat().st_size, \"category\": categorize_path(rel), } ) return assets def categorize_path(path: Path) -> str: text = str(path) if text.startswith(\"plugins/\"): return \"codex_plugin\" if text.startswith(\"tests/\"): return \"tests\" if text.startswith(\"results/pilot_suite\"): return \"pilot_artifact\" if text.startswith(\"results/\"): return \"evidence_artifact\" if text.endswith(\".py\"): return \"source\" if text.endswith(\".md\"): return \"documentation\" return \"workspace\" def github_catalog() -> list[dict[str, object]]: repos = [ \"CrewRiz/hops-memory-fabric-pilot-suite\", \"CrewRiz/hops-memory-fabric-pilot-suite-backup\", ] rows = [] for repo in repos: result = run_command( [\"gh\", \"repo\", \"view\", repo, \"--json\", \"nameWithOwner,visibility,url,updatedAt,defaultBranchRef\"], timeout=20, ) parsed: dict[str, Any] | None = None if result[\"success\"] and result[\"stdout\"]: try: parsed = json.loads(str(result[\"stdout\"])) except json.JSONDeco", "tokens": 154, "vector": { "54": -0.042257712736425826, "395": 0.1267731382092775, "16": 0.08451542547285165, "416": -0.3380617018914066, "344": -0.1267731382092775, "219": 0.042257712736425826, "328": -0.08451542547285165, "509": 0.042257712736425826, "403": -0.4225771273642583, "293": -0.042257712736425826, "97": 0.042257712736425826, "226": 0.1267731382092775, "104": -0.1690308509457033, "496": 0.042257712736425826, "472": 0.1690308509457033, "154": -0.042257712736425826, "157": -0.042257712736425826, "504": 0.1267731382092775, "264": -0.042257712736425826, "174": -0.08451542547285165, "396": 0.253546276418555, "39": 0.042257712736425826, "64": 0.042257712736425826, "474": 0.042257712736425826, "237": 0.042257712736425826, "373": -0.08451542547285165, "369": 0.3380617018914066, "459": 0.08451542547285165, "408": -0.2958039891549808, "239": 0.1690308509457033, "402": 0.042257712736425826, "467": 0.08451542547285165, "345": 0.08451542547285165, "448": -0.08451542547285165, "483": 0.042257712736425826, "488": 0.042257712736425826, "81": -0.1267731382092775, "366": 0.042257712736425826, "321": 0.042257712736425826, "33": -0.042257712736425826, "458": -0.042257712736425826, "289": -0.042257712736425826, "455": 0.042257712736425826, "163": 0.042257712736425826, "106": -0.08451542547285165, "41": 0.042257712736425826, "266": 0.08451542547285165, "152": 0.08451542547285165, "192": -0.08451542547285165, "173": 0.08451542547285165, "7": -0.21128856368212914, "84": -0.042257712736425826, "444": -0.042257712736425826, "246": -0.1690308509457033, "430": 0.042257712736425826, "507": -0.042257712736425826, "299": 0.042257712736425826, "258": 0.1267731382092775, "417": 0.042257712736425826, "381": -0.042257712736425826, "296": -0.042257712736425826, "503": -0.042257712736425826, "245": 0.042257712736425826, "58": -0.08451542547285165, "470": -0.042257712736425826, "276": 0.08451542547285165, "354": -0.042257712736425826, "99": -0.08451542547285165, "30": -0.042257712736425826, "193": 0.042257712736425826, "177": -0.042257712736425826, "205": 0.042257712736425826 } }, { "id": "ops_catalog.py#2", "path": "ops_catalog.py", "chunk": 2, "text": "meout=20, ) parsed: dict[str, Any] | None = None if result[\"success\"] and result[\"stdout\"]: try: parsed = json.loads(str(result[\"stdout\"])) except json.JSONDecodeError: parsed = None rows.append( { \"repo\": repo, \"success\": result[\"success\"], \"data\": parsed, \"stderr\": result[\"stderr\"], } ) return rows def cloudflare_catalog() -> list[dict[str, object]]: commands = [ (\"whoami\", [\"wrangler\", \"whoami\"]), (\"pages_projects\", [\"wrangler\", \"pages\", \"project\", \"list\"]), (\"kv_namespaces\", [\"wrangler\", \"kv\", \"namespace\", \"list\"]), (\"r2_buckets\", [\"wrangler\", \"r2\", \"bucket\", \"list\"]), (\"d1_databases\", [\"wrangler\", \"d1\", \"list\"]), (\"vectorize_indexes\", [\"wrangler\", \"vectorize\", \"list\"]), (\"queues\", [\"wrangler\", \"queues\", \"list\"]), ] rows = [] for name, args in commands: result = run_command(args, timeout=25) rows.append( { \"name\": name, \"command\": \" \".join(args), \"success\": result[\"success\"], \"stdout\": result[\"stdout\"], \"stderr\": result[\"stderr\"], } ) return rows def suggested_todos(catalog: dict[str, object]) -> list[dict[str, str]]: todos = [ { \"priority\": \"P0\", \"title\": \"Authenticate Wrangler and refresh Cloudflare inventory\", \"why\": \"Live Cloudflare asset cataloging needs a valid Wrangler s", "tokens": 134, "vector": { "366": 0.04319342127906801, "245": 0.04319342127906801, "58": -0.21596710639534003, "106": -0.17277368511627203, "396": 0.30235394895347606, "470": -0.04319342127906801, "276": 0.12958026383720403, "403": 0.0, "246": -0.38874079151161206, "174": -0.30235394895347606, "354": -0.08638684255813601, "99": -0.17277368511627203, "30": -0.04319342127906801, "258": 0.08638684255813601, "193": 0.04319342127906801, "177": 0.0, "432": -0.04319342127906801, "444": -0.21596710639534003, "7": -0.08638684255813601, "382": 0.17277368511627203, "369": 0.08638684255813601, "459": 0.08638684255813601, "219": 0.04319342127906801, "163": 0.34554737023254406, "41": 0.12958026383720403, "272": -0.08638684255813601, "242": 0.08638684255813601, "182": -0.38874079151161206, "400": -0.04319342127906801, "191": 0.04319342127906801, "36": 0.08638684255813601, "158": 0.04319342127906801, "311": -0.04319342127906801, "475": -0.04319342127906801, "453": -0.04319342127906801, "490": 0.04319342127906801, "395": -0.04319342127906801, "90": 0.04319342127906801, "421": -0.08638684255813601, "16": 0.04319342127906801, "386": 0.12958026383720403, "265": 0.12958026383720403, "344": -0.08638684255813601, "430": 0.04319342127906801, "503": -0.04319342127906801, "439": -0.04319342127906801, "93": 0.04319342127906801, "188": -0.04319342127906801, "357": 0.04319342127906801, "456": 0.04319342127906801, "48": -0.04319342127906801, "278": 0.04319342127906801, "170": 0.04319342127906801, "32": -0.04319342127906801, "214": 0.04319342127906801, "115": 0.08638684255813601, "43": -0.04319342127906801, "469": 0.04319342127906801, "447": 0.04319342127906801, "294": 0.04319342127906801, "492": -0.04319342127906801 } }, { "id": "ops_catalog.py#3", "path": "ops_catalog.py", "chunk": 3, "text": "dos = [ { \"priority\": \"P0\", \"title\": \"Authenticate Wrangler and refresh Cloudflare inventory\", \"why\": \"Live Cloudflare asset cataloging needs a valid Wrangler session or API token.\", }, { \"priority\": \"P0\", \"title\": \"Deploy ops dashboard to Cloudflare Pages\", \"why\": \"The dashboard is generated locally and ready for Pages once Cloudflare auth is available.\", }, { \"priority\": \"P1\", \"title\": \"Attach customer pilot datasets\", \"why\": \"Paid pilots need bounded customer data slices with agreed baselines and acceptance metrics.\", }, { \"priority\": \"P1\", \"title\": \"Verify Cloudflare Workers AI binding on Pages\", \"why\": \"The agent prefers env.AI.run with @cf/moonshotai/kimi-k2.6 and falls back to local RAG if the binding is missing.\", }, { \"priority\": \"P2\", \"title\": \"Promote repo-local Codex plugin to home-level plugin\", \"why\": \"This makes the memory fabric skill/MCP available across all future projects.\", }, ] cf_rows = catalog.get(\"cloudflare\", []) if isinstance(cf_rows, list) and any(row.get(\"success\") for row in cf_rows): todos = [todo for todo in todos if \"Authenticate Wrangler\" not in todo[\"title\"]] return todos def build_catalog(out_dir: Path, include_cloudflare: bool = True) -> dict[str", "tokens": 166, "vector": { "449": 0.15152288168283162, "48": -0.2525381361380527, "278": 0.10101525445522107, "170": 0.30304576336566325, "32": -0.050507627227610534, "182": -0.2525381361380527, "354": -0.2525381361380527, "214": 0.050507627227610534, "115": 0.2525381361380527, "177": 0.050507627227610534, "43": -0.2525381361380527, "36": 0.050507627227610534, "469": 0.050507627227610534, "447": 0.050507627227610534, "294": 0.050507627227610534, "492": -0.050507627227610534, "63": -0.050507627227610534, "369": 0.0, "20": 0.050507627227610534, "60": 0.0, "439": -0.10101525445522107, "169": 0.050507627227610534, "358": 0.10101525445522107, "102": -0.15152288168283162, "191": 0.15152288168283162, "441": -0.20203050891044214, "506": -0.15152288168283162, "480": 0.050507627227610534, "111": -0.050507627227610534, "434": -0.050507627227610534, "16": 0.10101525445522107, "189": 0.050507627227610534, "477": 0.10101525445522107, "502": 0.10101525445522107, "425": -0.050507627227610534, "7": -0.10101525445522107, "113": 0.050507627227610534, "38": -0.050507627227610534, "224": 0.050507627227610534, "446": 0.050507627227610534, "58": -0.050507627227610534, "237": 0.050507627227610534, "262": 0.10101525445522107, "158": -0.050507627227610534, "436": 0.050507627227610534, "23": -0.050507627227610534, "417": -0.050507627227610534, "493": 0.050507627227610534, "50": 0.10101525445522107, "384": -0.10101525445522107, "440": -0.050507627227610534, "212": -0.050507627227610534, "171": 0.050507627227610534, "172": -0.050507627227610534, "371": 0.050507627227610534, "228": -0.050507627227610534, "484": -0.050507627227610534, "257": -0.050507627227610534, "293": -0.20203050891044214, "163": 0.10101525445522107, "403": -0.15152288168283162, "255": -0.050507627227610534, "57": -0.050507627227610534, "54": -0.050507627227610534, "87": -0.050507627227610534, "94": -0.10101525445522107, "334": -0.050507627227610534, "256": 0.050507627227610534, "286": -0.050507627227610534, "495": -0.050507627227610534, "192": -0.050507627227610534, "173": 0.050507627227610534, "412": -0.050507627227610534, "478": 0.050507627227610534, "350": 0.050507627227610534, "491": -0.050507627227610534, "146": -0.15152288168283162, "357": 0.050507627227610534, "41": -0.10101525445522107, "330": -0.050507627227610534, "470": -0.050507627227610534, "355": -0.10101525445522107, "174": -0.050507627227610534, "344": -0.15152288168283162, "456": 0.15152288168283162, "352": -0.15152288168283162, "459": 0.050507627227610534, "277": 0.050507627227610534, "153": -0.050507627227610534, "416": -0.050507627227610534, "208": -0.050507627227610534, "183": -0.050507627227610534, "181": 0.050507627227610534, "106": -0.050507627227610534, "396": 0.050507627227610534 } }, { "id": "ops_catalog.py#4", "path": "ops_catalog.py", "chunk": 4, "text": "do for todo in todos if \"Authenticate Wrangler\" not in todo[\"title\"]] return todos def build_catalog(out_dir: Path, include_cloudflare: bool = True) -> dict[str, object]: catalog = { \"generated_at_unix\": time.time(), \"workspace\": str(ROOT), \"local_assets\": list_files(), \"github\": github_catalog(), \"cloudflare\": cloudflare_catalog() if include_cloudflare else [], } catalog[\"todos\"] = suggested_todos(catalog) out_dir.mkdir(parents=True, exist_ok=True) (out_dir / \"asset_catalog.json\").write_text(json.dumps(catalog, indent=2), encoding=\"utf-8\") return catalog def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--out-dir\", type=Path, default=ROOT / \"results\" / \"ops_dashboard\") parser.add_argument(\"--no-cloudflare\", action=\"store_true\") args = parser.parse_args() catalog = build_catalog(args.out_dir, include_cloudflare=not args.no_cloudflare) print(f\"cataloged {len(catalog['local_assets'])} local assets\") print(f\"wrote {args.out_dir / 'asset_catalog.json'}\") if __name__ == \"__main__\": main()", "tokens": 114, "vector": { "428": 0.06085806194501846, "16": 0.06085806194501846, "352": -0.12171612389003691, "344": -0.12171612389003691, "456": 0.18257418583505539, "403": -0.18257418583505539, "32": -0.06085806194501846, "182": -0.06085806194501846, "293": -0.18257418583505539, "170": 0.06085806194501846, "369": 0.12171612389003691, "459": 0.12171612389003691, "277": 0.12171612389003691, "153": -0.36514837167011077, "416": -0.12171612389003691, "208": -0.12171612389003691, "183": -0.06085806194501846, "181": 0.18257418583505539, "106": -0.06085806194501846, "396": 0.12171612389003691, "41": 0.06085806194501846, "357": 0.42600643361512924, "65": -0.06085806194501846, "51": -0.12171612389003691, "289": -0.06085806194501846, "328": -0.12171612389003691, "15": -0.12171612389003691, "166": -0.06085806194501846, "192": 0.06085806194501846, "455": 0.06085806194501846, "115": 0.12171612389003691, "219": 0.06085806194501846, "381": -0.06085806194501846, "188": -0.06085806194501846, "405": 0.06085806194501846, "108": -0.06085806194501846, "495": 0.06085806194501846, "499": 0.12171612389003691, "258": 0.18257418583505539, "230": -0.06085806194501846, "336": 0.06085806194501846, "383": 0.06085806194501846, "232": -0.06085806194501846, "13": -0.12171612389003691, "276": 0.06085806194501846, "433": -0.24343224778007383, "427": 0.06085806194501846, "271": 0.06085806194501846, "201": -0.06085806194501846, "475": -0.12171612389003691, "118": -0.06085806194501846, "43": -0.06085806194501846, "275": -0.06085806194501846, "55": 0.06085806194501846, "448": -0.06085806194501846, "42": 0.06085806194501846, "147": -0.06085806194501846, "445": -0.06085806194501846, "99": -0.06085806194501846, "265": 0.24343224778007383, "322": 0.06085806194501846, "21": -0.06085806194501846, "462": 0.12171612389003691, "64": -0.06085806194501846, "113": 0.06085806194501846, "395": 0.06085806194501846, "346": -0.06085806194501846, "34": -0.06085806194501846, "333": 0.06085806194501846 } }, { "id": "ops_catalog.py#5", "path": "ops_catalog.py", "chunk": 5, "text": "n()", "tokens": 0, "vector": {} }, { "id": "ops_rag.py#0", "path": "ops_rag.py", "chunk": 0, "text": "\"\"\"Build and query a lightweight local RAG index over HOPS workspace artifacts.\"\"\" from __future__ import annotations import argparse import hashlib import json import math import re from pathlib import Path from typing import Dict, Iterable, List ROOT = Path(__file__).resolve().parent TOKEN_RE = re.compile(r\"[a-zA-Z0-9_]{2,}\") def tokenize(text: str) -> list[str]: return [token.lower() for token in TOKEN_RE.findall(text)] def should_index(path: Path) -> bool: rel = path.relative_to(ROOT) parts = rel.parts if parts[0] in {\".git\", \"backups\", \".pycache\"}: return False if len(parts) >= 2 and parts[0] == \"results\" and parts[1] == \"ops_dashboard\": return False if \".mplconfig\" in parts: return False if path.stat().st_size > 2_000_000: return False return path.suffix.lower() in {\".py\", \".md\", \".json\", \".csv\", \".yaml\", \".yml\", \".toml\", \".html\"} def read_text(path: Path) -> str: try: return path.read_text(encoding=\"utf-8\") except UnicodeDecodeError: return path.read_text(encoding=\"utf-8\", errors=\"ignore\") def chunks(text: str, size: int = 1200, overlap: int = 160) -> Iterable[str]: text = re.sub(r\"\\s+\", \" \", text).strip() if not text: return start = 0 while start < len(text): yield text[sta", "tokens": 167, "vector": { "324": 0.041415768328129106, "354": -0.12424730498438732, "424": -0.041415768328129106, "305": 0.041415768328129106, "293": -0.08283153665625821, "163": 0.12424730498438732, "27": -0.041415768328129106, "95": 0.0, "152": 0.041415768328129106, "289": -0.041415768328129106, "15": 0.041415768328129106, "373": -0.12424730498438732, "201": -0.041415768328129106, "217": 0.28991037829690375, "297": 0.041415768328129106, "427": 0.041415768328129106, "45": 0.041415768328129106, "258": 0.08283153665625821, "88": 0.041415768328129106, "314": 0.12424730498438732, "64": 0.12424730498438732, "416": -0.4555734516094202, "504": 0.0, "106": -0.041415768328129106, "16": -0.041415768328129106, "328": -0.08283153665625821, "253": 0.041415768328129106, "484": -0.041415768328129106, "111": -0.08283153665625821, "235": 0.041415768328129106, "60": 0.12424730498438732, "248": 0.041415768328129106, "355": -0.041415768328129106, "459": 0.16566307331251642, "146": -0.041415768328129106, "408": -0.28991037829690375, "396": 0.12424730498438732, "369": 0.372741914953162, "344": -0.16566307331251642, "455": 0.041415768328129106, "264": 0.0, "183": -0.041415768328129106, "104": -0.08283153665625821, "496": 0.041415768328129106, "472": 0.28991037829690375, "403": -0.20707884164064555, "154": -0.041415768328129106, "157": -0.041415768328129106, "252": 0.16566307331251642, "113": 0.08283153665625821, "448": -0.041415768328129106, "42": 0.041415768328129106, "474": 0.041415768328129106, "356": 0.041415768328129106, "417": 0.041415768328129106, "366": 0.041415768328129106, "33": -0.041415768328129106, "339": 0.041415768328129106, "430": 0.041415768328129106, "191": -0.041415768328129106, "229": -0.041415768328129106, "4": -0.12424730498438732, "30": -0.041415768328129106, "232": -0.08283153665625821, "153": -0.08283153665625821, "177": -0.041415768328129106, "446": 0.041415768328129106, "400": 0.041415768328129106, "204": -0.041415768328129106, "109": 0.08283153665625821, "277": -0.041415768328129106, "510": -0.041415768328129106, "165": -0.041415768328129106, "221": 0.041415768328129106, "470": -0.041415768328129106, "460": 0.08283153665625821, "7": 0.041415768328129106, "139": 0.041415768328129106, "188": 0.041415768328129106 } }, { "id": "ops_rag.py#1", "path": "ops_rag.py", "chunk": 1, "text": "int = 1200, overlap: int = 160) -> Iterable[str]: text = re.sub(r\"\\s+\", \" \", text).strip() if not text: return start = 0 while start < len(text): yield text[start : start + size] start += max(1, size - overlap) def vector(tokens: list[str], dims: int = 512) -> dict[int, float]: counts: Dict[int, float] = {} for token in tokens: digest = hashlib.sha256(token.encode(\"utf-8\")).digest() idx = int.from_bytes(digest[:4], \"little\") % dims sign = 1.0 if digest[4] % 2 == 0 else -1.0 counts[idx] = counts.get(idx, 0.0) + sign norm = math.sqrt(sum(value * value for value in counts.values())) or 1.0 return {idx: value / norm for idx, value in counts.items()} def cosine(left: dict[int, float], right: dict[int, float]) -> float: if len(left) > len(right): left, right = right, left return sum(value * right.get(idx, 0.0) for idx, value in left.items()) def build_index(out_dir: Path) -> dict[str, object]: docs = [] for path in sorted(ROOT.rglob(\"*\")): if not path.is_file() or not should_index(path): continue rel = str(path.relative_to(ROOT)) text = read_text(path) for idx, chunk in enumerate(chunks(text)): docs.append( { \"id\": f\"{rel}#{idx}\", \"path\": rel, \"chunk\": idx, \"text\": chunk, \"tokens\": len(t", "tokens": 177, "vector": { "109": 0.29834965625128057, "277": -0.03729370703141007, "510": -0.07458741406282014, "165": -0.03729370703141007, "16": 0.18646853515705036, "396": 0.14917482812564029, "408": -0.29834965625128057, "314": 0.03729370703141007, "221": 0.03729370703141007, "470": -0.03729370703141007, "403": -0.14917482812564029, "293": -0.11188112109423022, "369": 0.03729370703141007, "460": 0.18646853515705036, "7": 0.03729370703141007, "113": 0.14917482812564029, "139": 0.03729370703141007, "204": -0.07458741406282014, "411": -0.03729370703141007, "459": 0.11188112109423022, "432": 0.03729370703141007, "197": -0.11188112109423022, "163": 0.03729370703141007, "70": 0.0, "148": -0.07458741406282014, "106": -0.18646853515705036, "374": 0.18646853515705036, "461": -0.18646853515705036, "60": 0.07458741406282014, "344": -0.22376224218846044, "11": -0.14917482812564029, "45": 0.03729370703141007, "349": 0.03729370703141007, "439": -0.03729370703141007, "153": -0.07458741406282014, "198": 0.3729370703141007, "502": -0.03729370703141007, "24": -0.03729370703141007, "267": -0.07458741406282014, "381": -0.03729370703141007, "41": -0.03729370703141007, "115": 0.07458741406282014, "88": 0.03729370703141007, "382": 0.03729370703141007, "265": 0.07458741406282014, "205": 0.2610559492198705, "137": -0.03729370703141007, "95": 0.07458741406282014, "173": -0.03729370703141007, "310": -0.18646853515705036, "39": 0.18646853515705036, "243": 0.03729370703141007, "416": -0.2610559492198705, "219": 0.03729370703141007, "328": -0.07458741406282014, "509": 0.03729370703141007, "97": 0.03729370703141007, "264": -0.03729370703141007, "226": 0.03729370703141007, "104": -0.11188112109423022, "496": 0.03729370703141007, "4": -0.03729370703141007, "364": -0.11188112109423022, "400": 0.03729370703141007, "174": -0.03729370703141007, "421": 0.03729370703141007 } }, { "id": "ops_rag.py#2", "path": "ops_rag.py", "chunk": 2, "text": "text = read_text(path) for idx, chunk in enumerate(chunks(text)): docs.append( { \"id\": f\"{rel}#{idx}\", \"path\": rel, \"chunk\": idx, \"text\": chunk, \"tokens\": len(tokenize(chunk)), \"vector\": vector(tokenize(chunk)), } ) index = {\"version\": 1, \"documents\": docs} out_dir.mkdir(parents=True, exist_ok=True) (out_dir / \"rag_index.json\").write_text(json.dumps(index, indent=2), encoding=\"utf-8\") return index def query_index(index: dict[str, object], query: str, top_k: int = 6) -> list[dict[str, object]]: qv = vector(tokenize(query)) scored = [] for doc in index[\"documents\"]: dv = {int(k): float(v) for k, v in doc[\"vector\"].items()} scored.append((cosine(qv, dv), doc)) scored.sort(key=lambda item: item[0], reverse=True) return [ { \"score\": score, \"path\": doc[\"path\"], \"chunk\": doc[\"chunk\"], \"text\": doc[\"text\"][:700], } for score, doc in scored[:top_k] ] def load_index(path: Path) -> dict[str, object]: return json.loads(path.read_text(encoding=\"utf-8\")) def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--out-dir\", type=Path, default=ROOT / \"results\" / \"ops_dashboard\") parser.add_argument(\"--query\") parser.add_argument(\"--top-k\", type=int, default=6) a", "tokens": 157, "vector": { "408": -0.2256468412032621, "4": -0.09025873648130484, "416": -0.36103494592521934, "16": 0.18051747296260967, "198": 0.13538810472195725, "364": -0.31590557768456695, "344": -0.18051747296260967, "148": -0.04512936824065242, "400": 0.04512936824065242, "70": -0.09025873648130484, "174": -0.09025873648130484, "421": 0.18051747296260967, "104": -0.09025873648130484, "197": -0.04512936824065242, "113": 0.04512936824065242, "146": -0.13538810472195725, "432": 0.18051747296260967, "27": -0.2256468412032621, "92": 0.04512936824065242, "155": 0.09025873648130484, "153": -0.13538810472195725, "405": 0.04512936824065242, "108": -0.04512936824065242, "181": 0.13538810472195725, "495": 0.04512936824065242, "360": 0.04512936824065242, "258": 0.13538810472195725, "230": -0.04512936824065242, "336": 0.04512936824065242, "383": 0.13538810472195725, "232": -0.09025873648130484, "369": 0.13538810472195725, "459": 0.13538810472195725, "60": 0.04512936824065242, "106": -0.13538810472195725, "396": 0.18051747296260967, "41": 0.13538810472195725, "424": -0.13538810472195725, "109": 0.13538810472195725, "163": 0.04512936824065242, "379": -0.09025873648130484, "89": -0.18051747296260967, "275": 0.2256468412032621, "83": 0.09025873648130484, "374": 0.04512936824065242, "95": 0.04512936824065242, "173": -0.04512936824065242, "356": -0.04512936824065242, "44": 0.04512936824065242, "85": 0.04512936824065242, "330": -0.09025873648130484, "434": -0.04512936824065242, "375": 0.04512936824065242, "193": 0.04512936824065242, "13": -0.04512936824065242, "276": 0.04512936824065242, "433": -0.18051747296260967, "427": 0.04512936824065242, "271": 0.04512936824065242, "201": -0.04512936824065242, "208": 0.04512936824065242, "475": -0.13538810472195725, "118": -0.04512936824065242, "43": -0.04512936824065242, "55": 0.09025873648130484, "328": -0.04512936824065242, "448": -0.04512936824065242, "42": 0.04512936824065242, "40": -0.04512936824065242 } }, { "id": "ops_rag.py#3", "path": "ops_rag.py", "chunk": 3, "text": "rgument(\"--out-dir\", type=Path, default=ROOT / \"results\" / \"ops_dashboard\") parser.add_argument(\"--query\") parser.add_argument(\"--top-k\", type=int, default=6) args = parser.parse_args() index_path = args.out_dir / \"rag_index.json\" index = load_index(index_path) if index_path.exists() else build_index(args.out_dir) if args.query: print(json.dumps(query_index(index, args.query, args.top_k), indent=2)) else: print(f\"indexed {len(index['documents'])} chunks\") print(f\"wrote {index_path}\") if __name__ == \"__main__\": main()", "tokens": 63, "vector": { "388": 0.08192319205190406, "118": -0.08192319205190406, "43": -0.08192319205190406, "275": -0.1638463841038081, "416": -0.08192319205190406, "55": 0.1638463841038081, "328": -0.08192319205190406, "448": -0.08192319205190406, "42": 0.08192319205190406, "433": -0.24576957615571215, "475": -0.1638463841038081, "424": -0.24576957615571215, "40": -0.08192319205190406, "109": 0.08192319205190406, "265": 0.4915391523114243, "322": 0.08192319205190406, "56": 0.3276927682076162, "153": -0.1638463841038081, "360": 0.08192319205190406, "258": 0.1638463841038081, "27": -0.24576957615571215, "375": 0.08192319205190406, "403": -0.24576957615571215, "173": -0.08192319205190406, "381": -0.1638463841038081, "243": 0.08192319205190406, "462": 0.24576957615571215, "336": 0.08192319205190406, "60": 0.08192319205190406, "383": 0.1638463841038081, "101": 0.08192319205190406, "113": 0.08192319205190406, "155": 0.08192319205190406, "400": 0.08192319205190406, "346": -0.08192319205190406, "34": -0.08192319205190406, "333": 0.08192319205190406, "13": -0.08192319205190406 } }, { "id": "plugins/hops-memory-fabric/.codex-plugin/plugin.json#0", "path": "plugins/hops-memory-fabric/.codex-plugin/plugin.json", "chunk": 0, "text": "{ \"name\": \"hops-memory-fabric\", \"version\": \"0.1.0\", \"description\": \"Local HOPS memory fabric tools for tiered memory, provenance HMKs, resonant routing, and pilot proof regeneration.\", \"author\": { \"name\": \"Ghostmesh\", \"email\": \"local@example.invalid\", \"url\": \"https://localhost.invalid/hops-memory-fabric\" }, \"homepage\": \"https://localhost.invalid/hops-memory-fabric\", \"repository\": \"local\", \"license\": \"Proprietary\", \"keywords\": [ \"hops\", \"memory\", \"mcp\", \"hypervectors\" ], \"skills\": \"./skills/\", \"mcpServers\": \"./.mcp.json\", \"interface\": { \"displayName\": \"HOPS Memory Fabric\", \"shortDescription\": \"Memory-native pilot and routing tools\", \"longDescription\": \"A local Codex plugin exposing the HOPS memory fabric as a reusable skill and MCP server for tiered memory retrieval, provenance-aware HMKs, semantic execution routing, and pilot proof regeneration.\", \"developerName\": \"Ghostmesh\", \"category\": \"Productivity\", \"capabilities\": [ \"Interactive\", \"Read\", \"Write\" ], \"websiteURL\": \"https://localhost.invalid/hops-memory-fabric\", \"privacyPolicyURL\": \"https://localhost.invalid/privacy\", \"termsOfServiceURL\": \"https://localhost.invalid/terms\", \"defaultPrompt\": [ \"Use $hops-memory-fabric to remember", "tokens": 130, "vector": { "386": 0.08838834764831843, "152": 0.397747564417433, "192": -0.5303300858899106, "173": 0.35355339059327373, "92": 0.044194173824159216, "201": -0.044194173824159216, "293": -0.17677669529663687, "505": -0.08838834764831843, "16": -0.044194173824159216, "366": 0.044194173824159216, "150": 0.08838834764831843, "438": 0.08838834764831843, "487": 0.044194173824159216, "252": 0.13258252147247765, "354": -0.17677669529663687, "7": -0.13258252147247765, "449": 0.08838834764831843, "57": 0.08838834764831843, "99": -0.044194173824159216, "80": 0.13258252147247765, "130": -0.044194173824159216, "497": -0.2651650429449553, "296": -0.044194173824159216, "318": -0.22097086912079608, "73": 0.22097086912079608, "421": 0.044194173824159216, "226": 0.044194173824159216, "460": 0.044194173824159216, "400": -0.044194173824159216, "103": 0.044194173824159216, "263": -0.044194173824159216, "306": 0.08838834764831843, "62": 0.044194173824159216, "258": 0.044194173824159216, "199": -0.044194173824159216, "418": 0.044194173824159216, "281": -0.044194173824159216, "446": -0.044194173824159216, "425": -0.044194173824159216, "87": -0.044194173824159216, "94": -0.044194173824159216, "352": -0.044194173824159216, "441": -0.044194173824159216, "500": -0.044194173824159216, "508": 0.044194173824159216, "412": -0.044194173824159216, "179": -0.044194173824159216, "395": 0.044194173824159216, "55": -0.044194173824159216, "164": 0.08838834764831843, "84": -0.044194173824159216, "237": 0.044194173824159216, "445": -0.044194173824159216, "63": 0.044194173824159216, "14": -0.044194173824159216, "51": 0.044194173824159216, "272": 0.044194173824159216, "321": 0.044194173824159216, "66": 0.044194173824159216, "230": 0.044194173824159216, "81": -0.044194173824159216, "340": 0.044194173824159216, "419": 0.044194173824159216, "102": -0.044194173824159216, "309": 0.044194173824159216 } }, { "id": "plugins/hops-memory-fabric/.codex-plugin/plugin.json#1", "path": "plugins/hops-memory-fabric/.codex-plugin/plugin.json", "chunk": 1, "text": "PolicyURL\": \"https://localhost.invalid/privacy\", \"termsOfServiceURL\": \"https://localhost.invalid/terms\", \"defaultPrompt\": [ \"Use $hops-memory-fabric to remember this decision with provenance.\", \"Use $hops-memory-fabric to route this task to the best execution node.\", \"Use $hops-memory-fabric to regenerate the paid-pilot proof package.\" ], \"brandColor\": \"#2563EB\" } }", "tokens": 47, "vector": { "135": 0.10153461651336192, "318": -0.20306923302672383, "73": 0.20306923302672383, "497": -0.20306923302672383, "164": 0.20306923302672383, "230": 0.10153461651336192, "81": -0.10153461651336192, "340": 0.10153461651336192, "419": 0.30460384954008574, "152": 0.30460384954008574, "192": -0.30460384954008574, "173": 0.30460384954008574, "102": 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-0.3333333333333333 } }, { "id": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py#0", "path": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py", "chunk": 0, "text": "#!/usr/bin/env python3 \"\"\"Minimal stdio MCP server for the local HOPS memory fabric. The server intentionally implements a compact JSON-RPC subset used by MCP clients: initialize, tools/list, and tools/call. Tool calls return textual JSON payloads so they remain easy to inspect in any client. \"\"\" from __future__ import annotations import os import json import subprocess import sys from pathlib import Path from typing import Any, Callable, Dict ROOT = Path(__file__).resolve().parents[3] if str(ROOT) not in sys.path: sys.path.insert(0, str(ROOT)) from memory_fabric import ComplexSpace, FractalMemoryPyramid, MemoryRecord, Node, ProvenanceHMK, ResonantExecutionMesh from run_memory_fabric import run_memory, run_mesh, run_provenance STORE_PATH = Path(os.environ.get(\"HOPS_MEMORY_STORE\", ROOT / \"results\" / \"memory_fabric\" / \"live_memory.json\")) def load_records() -> list[MemoryRecord]: if not STORE_PATH.exists(): return [] payload = json.loads(STORE_PATH.read_text(encoding=\"utf-8\")) return [MemoryRecord(**item) for item in payload] def save_records(records: list[MemoryRecord]) -> None: STORE_PATH.parent.mkdir(parents=True, exist_ok=True) STORE_PATH.write_text( json.dumps([record.__dict__ f", "tokens": 154, "vector": { "126": -0.05157106231293967, "337": 0.20628424925175867, "439": -0.05157106231293967, "193": 0.05157106231293967, "423": -0.05157106231293967, "7": -0.05157106231293967, "16": 0.0, "179": -0.10314212462587934, "441": -0.10314212462587934, "293": -0.154713186938819, "152": 0.05157106231293967, "192": -0.05157106231293967, "173": 0.0, "194": 0.05157106231293967, "352": -0.10314212462587934, "438": 0.05157106231293967, "258": 0.309426373877638, "398": -0.05157106231293967, "474": -0.05157106231293967, "504": -0.10314212462587934, "167": -0.05157106231293967, "399": 0.05157106231293967, "71": -0.05157106231293967, "505": -0.10314212462587934, "163": 0.154713186938819, "354": -0.05157106231293967, "382": 0.05157106231293967, "380": -0.05157106231293967, "500": -0.05157106231293967, "369": 0.154713186938819, "396": 0.05157106231293967, "319": 0.05157106231293967, "417": 0.05157106231293967, "21": -0.05157106231293967, "508": 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-0.05157106231293967, "131": 0.05157106231293967, "459": 0.10314212462587934, "37": 0.05157106231293967, "291": -0.10314212462587934, "4": -0.05157106231293967, "232": -0.05157106231293967, "153": -0.05157106231293967, "330": -0.10314212462587934, "282": -0.05157106231293967, "169": -0.05157106231293967, "276": 0.05157106231293967, "484": -0.05157106231293967, "405": 0.05157106231293967, "181": 0.10314212462587934, "495": 0.05157106231293967, "230": -0.05157106231293967, "336": 0.05157106231293967, "112": 0.05157106231293967, "402": 0.05157106231293967 } }, { "id": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py#1", "path": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py", "chunk": 1, "text": "def save_records(records: list[MemoryRecord]) -> None: STORE_PATH.parent.mkdir(parents=True, exist_ok=True) STORE_PATH.write_text( json.dumps([record.__dict__ for record in records], indent=2), encoding=\"utf-8\", ) def build_pyramid(records: list[MemoryRecord], dimension: int = 2048) -> FractalMemoryPyramid: space = ComplexSpace(dimension) pyramid = FractalMemoryPyramid(space) for record in records: pyramid.add(record) return pyramid def text_result(payload: object) -> dict[str, object]: return {\"content\": [{\"type\": \"text\", \"text\": json.dumps(payload, indent=2)}]} def tool_status(arguments: dict[str, Any]) -> dict[str, object]: dimension = int(arguments.get(\"dimension\", 2048)) space = ComplexSpace(dimension) rows = run_memory(space) + run_provenance(space) + run_mesh(space) return text_result( { \"passed\": sum(1 for row in rows if row[\"success\"]), \"total\": len(rows), \"rows\": rows, \"live_records\": len(load_records()), } ) def tool_remember(arguments: dict[str, Any]) -> dict[str, object]: key = str(arguments[\"key\"]) value = str(arguments[\"value\"]) record = MemoryRecord( key=key, value=value, actor=str(arguments.get(\"actor\", \"user\")), trust=str(arguments.get(\"trust\", \"high\")), tool=str(", "tokens": 147, "vector": { "459": 0.22249707974499242, "282": -0.04449941594899848, "169": -0.1779976637959939, "163": 0.08899883189799696, "485": 0.13349824784699543, "276": 0.04449941594899848, "315": 0.08899883189799696, "484": -0.04449941594899848, "405": 0.04449941594899848, "108": -0.04449941594899848, "181": 0.08899883189799696, "495": 0.04449941594899848, "230": -0.04449941594899848, "258": 0.08899883189799696, "336": 0.1779976637959939, "112": 0.22249707974499242, "402": 0.04449941594899848, "16": 0.13349824784699543, "344": -0.13349824784699543, "383": 0.08899883189799696, "232": -0.04449941594899848, "153": -0.04449941594899848, "372": -0.04449941594899848, "328": -0.22249707974499242, "109": 0.08899883189799696, "191": -0.08899883189799696, "356": 0.08899883189799696, "319": 0.26699649569399087, "355": 0.0, "34": 0.13349824784699543, "126": -0.04449941594899848, "369": 0.13349824784699543, "271": -0.08899883189799696, "291": -0.08899883189799696, "41": 0.04449941594899848, "106": -0.22249707974499242, "396": 0.44499415948998483, "237": -0.04449941594899848, "275": -0.04449941594899848, "408": -0.08899883189799696, "209": -0.04449941594899848, "177": 0.31149591164298934, "470": -0.08899883189799696, "444": -0.22249707974499242, "89": -0.04449941594899848, "59": 0.04449941594899848, "116": 0.04449941594899848, "296": -0.04449941594899848, "265": 0.04449941594899848, "403": -0.04449941594899848, "174": -0.04449941594899848, "273": -0.04449941594899848, "113": 0.08899883189799696, "326": -0.04449941594899848, "37": 0.04449941594899848, "357": -0.04449941594899848, "44": 0.1779976637959939, "205": 0.1779976637959939, "4": -0.04449941594899848, "247": 0.08899883189799696, "366": 0.04449941594899848, "380": -0.04449941594899848 } }, { "id": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py#2", "path": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py", "chunk": 2, "text": "arguments[\"value\"]) record = MemoryRecord( key=key, value=value, actor=str(arguments.get(\"actor\", \"user\")), trust=str(arguments.get(\"trust\", \"high\")), tool=str(arguments.get(\"tool\", \"codex\")), ) records = [item for item in load_records() if item.key != key] records.append(record) save_records(records) return text_result({\"stored\": True, \"record\": record.__dict__, \"records\": len(records)}) def tool_retrieve(arguments: dict[str, Any]) -> dict[str, object]: query = str(arguments[\"query\"]) records = load_records() pyramid = build_pyramid(records) result = pyramid.retrieve(query) return text_result(result.as_dict()) def tool_verify_provenance(arguments: dict[str, Any]) -> dict[str, object]: space = ComplexSpace(int(arguments.get(\"dimension\", 2048))) hmk = ProvenanceHMK(space) actor = str(arguments.get(\"actor\", \"user\")) trust = str(arguments.get(\"trust\", \"high\")) cv = hmk.encode( cause=str(arguments.get(\"cause\", \"memory_update\")), actor=actor, trust=trust, tool=str(arguments.get(\"tool\", \"codex\")), ) result = hmk.verify( cv, actual_actor=actor, authorized_actor=str(arguments.get(\"authorized_actor\", \"user\")), required_trust=str(arguments.get(\"required_trust\", \"high\")), ) return text_result", "tokens": 147, "vector": { "177": 0.49039861087284115, "205": 0.10508541661560881, "112": 0.14011388882081174, "485": 0.035028472205202936, "44": 0.14011388882081174, "336": 0.24519930543642057, "396": 0.49039861087284115, "41": -0.2802277776416235, "4": -0.10508541661560881, "247": 0.21017083323121763, "366": 0.10508541661560881, "380": -0.14011388882081174, "87": -0.07005694441040587, "169": -0.24519930543642057, "330": -0.10508541661560881, "16": 0.035028472205202936, "344": -0.035028472205202936, "37": 0.07005694441040587, "403": -0.035028472205202936, "174": -0.035028472205202936, "282": -0.035028472205202936, "369": 0.10508541661560881, "271": -0.10508541661560881, "135": 0.035028472205202936, "181": 0.035028472205202936, "402": 0.035028472205202936, "113": 0.035028472205202936, "459": 0.07005694441040587, "134": -0.035028472205202936, "106": -0.21017083323121763, "470": -0.07005694441040587, "424": -0.10508541661560881, "34": 0.07005694441040587, "372": -0.035028472205202936, "246": -0.10508541661560881, "80": -0.035028472205202936, "151": 0.035028472205202936, "385": 0.035028472205202936, "319": 0.07005694441040587, "355": 0.035028472205202936, "109": 0.035028472205202936, "328": -0.035028472205202936, "191": -0.035028472205202936, "31": 0.10508541661560881, "252": 0.035028472205202936, "297": 0.07005694441040587, "439": -0.035028472205202936, "509": 0.035028472205202936, "417": -0.035028472205202936, "117": -0.035028472205202936, "505": 0.07005694441040587, "14": -0.07005694441040587 } }, { "id": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py#3", "path": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py", "chunk": 3, "text": "_actor=actor, authorized_actor=str(arguments.get(\"authorized_actor\", \"user\")), required_trust=str(arguments.get(\"required_trust\", \"high\")), ) return text_result(result.as_dict()) def tool_route_task(arguments: dict[str, Any]) -> dict[str, object]: space = ComplexSpace(int(arguments.get(\"dimension\", 2048))) nodes = [ Node(\"edge-router-01\", \"edge\", \"low\", \"router\"), Node(\"gpu-annealer-01\", \"server\", \"medium\", \"annealer\"), Node(\"audit-ledger-01\", \"server\", \"high\", \"provenance\"), Node(\"mobile-cache-01\", \"edge\", \"low\", \"memory\"), ] mesh = ResonantExecutionMesh(space, nodes) result = mesh.route( task=str(arguments[\"task\"]), hardware=str(arguments.get(\"hardware\", \"server\")), latency=str(arguments.get(\"latency\", \"medium\")), tool=str(arguments.get(\"tool\", \"annealer\")), expected_node=str(arguments.get(\"expected_node\", \"\")), ) return text_result(result.as_dict()) def tool_run_pilot_suite(arguments: dict[str, Any]) -> dict[str, object]: out_dir = str(arguments.get(\"out_dir\", \"results/pilot_suite\")) completed = subprocess.run( [sys.executable, \"run_pilot_suite.py\", \"--out-dir\", out_dir], cwd=str(ROOT), capture_output=True, text=True, check=False, ) return text_result( { \"success\": completed.ret", "tokens": 141, "vector": { "327": -0.042835293687811936, "336": 0.042835293687811936, "505": 0.08567058737562387, "396": 0.5568588179415551, "177": 0.4711882305659313, "41": -0.2570117621268716, "4": -0.042835293687811936, "14": -0.08567058737562387, "366": 0.1285058810634358, "369": 0.1285058810634358, "271": -0.1285058810634358, "246": -0.1285058810634358, "151": 0.08567058737562387, "459": 0.08567058737562387, "257": -0.042835293687811936, "106": -0.17134117475124774, "470": -0.08567058737562387, "319": 0.0, "355": -0.042835293687811936, "109": 0.042835293687811936, "328": -0.08567058737562387, "191": -0.042835293687811936, "325": -0.08567058737562387, "84": 0.17134117475124774, "417": -0.1285058810634358, "372": 0.08567058737562387, "403": 0.17134117475124774, "364": -0.08567058737562387, "483": 0.0, "103": 0.1285058810634358, "179": -0.1285058810634358, "192": -0.1285058810634358, "440": -0.042835293687811936, "254": -0.042835293687811936, "150": 0.042835293687811936, "213": -0.042835293687811936, "94": 0.042835293687811936, "211": -0.08567058737562387, "471": -0.042835293687811936, "138": 0.042835293687811936, "270": 0.08567058737562387, "38": -0.08567058737562387, "380": -0.08567058737562387, "200": -0.042835293687811936, "153": -0.1285058810634358, "448": -0.042835293687811936, "333": 0.08567058737562387, "503": 0.042835293687811936, "172": -0.042835293687811936, "337": 0.042835293687811936, "162": 0.042835293687811936, "432": 0.042835293687811936, "118": -0.042835293687811936, "43": -0.042835293687811936, "367": -0.042835293687811936, "116": -0.042835293687811936, "181": 0.08567058737562387, "408": -0.042835293687811936, "32": -0.042835293687811936, "252": 0.042835293687811936, "174": -0.042835293687811936, "133": -0.042835293687811936 } }, { "id": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py#4", "path": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py", "chunk": 4, "text": "table, \"run_pilot_suite.py\", \"--out-dir\", out_dir], cwd=str(ROOT), capture_output=True, text=True, check=False, ) return text_result( { \"success\": completed.returncode == 0, \"returncode\": completed.returncode, \"stdout\": completed.stdout, \"stderr\": completed.stderr, \"out_dir\": out_dir, } ) TOOLS: dict[str, tuple[str, dict[str, object], Callable[[dict[str, Any]], dict[str, object]]]] = { \"hops_fabric_status\": ( \"Run the memory fabric proof checks and report live memory count.\", {\"type\": \"object\", \"properties\": {\"dimension\": {\"type\": \"integer\", \"default\": 2048}}}, tool_status, ), \"hops_remember\": ( \"Persist a provenance-bearing memory record into the local memory fabric store.\", { \"type\": \"object\", \"properties\": { \"key\": {\"type\": \"string\"}, \"value\": {\"type\": \"string\"}, \"actor\": {\"type\": \"string\", \"default\": \"user\"}, \"trust\": {\"type\": \"string\", \"default\": \"high\"}, \"tool\": {\"type\": \"string\", \"default\": \"codex\"}, }, \"required\": [\"key\", \"value\"], }, tool_remember, ), \"hops_retrieve\": ( \"Retrieve a memory record through the Fractal Memory Pyramid.\", {\"type\": \"object\", \"properties\": {\"query\": {\"type\": \"string\"}}, \"required\": [\"query\"]}, tool_retrieve, ), \"hops_verify_provenance\": ( \"Encode ", "tokens": 127, "vector": { "269": -0.048970210687439175, "432": 0.048970210687439175, "366": 0.09794042137487835, "118": -0.048970210687439175, "43": -0.048970210687439175, "153": -0.14691063206231753, "367": -0.048970210687439175, "396": 0.29382126412463505, "328": -0.09794042137487835, "116": -0.048970210687439175, "181": 0.09794042137487835, "408": -0.048970210687439175, "32": -0.048970210687439175, "252": 0.048970210687439175, "369": 0.048970210687439175, "271": -0.048970210687439175, "174": -0.048970210687439175, "333": 0.1958808427497567, "306": -0.14691063206231753, "99": -0.09794042137487835, "382": 0.09794042137487835, "505": -0.048970210687439175, "106": -0.1958808427497567, "251": -0.048970210687439175, "41": 0.24485105343719588, "193": -0.048970210687439175, "470": -0.048970210687439175, "224": -0.048970210687439175, "172": -0.048970210687439175, "441": -0.14691063206231753, "192": -0.29382126412463505, "173": 0.09794042137487835, "449": 0.048970210687439175, "406": 0.1958808427497567, "354": -0.048970210687439175, "132": -0.048970210687439175, "36": 0.048970210687439175, "364": -0.048970210687439175, "275": -0.48970210687439175, "45": 0.048970210687439175, "55": 0.1958808427497567, "191": -0.048970210687439175, "209": -0.048970210687439175, "178": -0.048970210687439175, "284": -0.048970210687439175, "150": 0.048970210687439175, "238": -0.048970210687439175, "112": 0.09794042137487835, "107": 0.048970210687439175, "293": -0.048970210687439175, "386": -0.048970210687439175, "44": 0.09794042137487835, "71": -0.29382126412463505, "205": 0.09794042137487835, "336": 0.048970210687439175, "4": -0.048970210687439175, "247": 0.09794042137487835, "380": -0.048970210687439175, "87": -0.048970210687439175, "464": -0.09794042137487835, "357": -0.048970210687439175, "171": 0.048970210687439175, "80": -0.048970210687439175, "412": 0.048970210687439175, "96": 0.048970210687439175, "34": 0.048970210687439175, "424": -0.09794042137487835, "134": -0.048970210687439175, "439": -0.048970210687439175 } }, { "id": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py#5", "path": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py", "chunk": 5, "text": "Memory Pyramid.\", {\"type\": \"object\", \"properties\": {\"query\": {\"type\": \"string\"}}, \"required\": [\"query\"]}, tool_retrieve, ), \"hops_verify_provenance\": ( \"Encode and verify a provenance-bound HMK against actor/trust requirements.\", { \"type\": \"object\", \"properties\": { \"cause\": {\"type\": \"string\"}, \"actor\": {\"type\": \"string\"}, \"trust\": {\"type\": \"string\"}, \"tool\": {\"type\": \"string\"}, \"authorized_actor\": {\"type\": \"string\", \"default\": \"user\"}, \"required_trust\": {\"type\": \"string\", \"default\": \"high\"}, }, }, tool_verify_provenance, ), \"hops_route_task\": ( \"Route a task to the best semantic execution node.\", { \"type\": \"object\", \"properties\": { \"task\": {\"type\": \"string\"}, \"hardware\": {\"type\": \"string\"}, \"latency\": {\"type\": \"string\"}, \"tool\": {\"type\": \"string\"}, \"expected_node\": {\"type\": \"string\"}, }, \"required\": [\"task\"], }, tool_route_task, ), \"hops_run_pilot_suite\": ( \"Regenerate the HOPS paid-pilot suite and test artifacts.\", {\"type\": \"object\", \"properties\": {\"out_dir\": {\"type\": \"string\", \"default\": \"results/pilot_suite\"}}}, tool_run_pilot_suite, ), } def send(payload: dict[str, object]) -> None: sys.stdout.write(json.dumps(payload) + \"\\n\") sys.stdout.flush() def handle(request: dict[str, An", "tokens": 120, "vector": { "192": -0.04003203845127178, "34": 0.04003203845127178, "275": -0.6805446536716203, "41": 0.20016019225635892, "406": 0.16012815380508713, "424": -0.08006407690254357, "71": -0.5204164998665332, "464": -0.08006407690254357, "134": -0.04003203845127178, "247": 0.12009611535381534, "439": -0.04003203845127178, "354": -0.08006407690254357, "417": -0.04003203845127178, "150": 0.04003203845127178, "94": 0.04003203845127178, "31": 0.0, "167": 0.04003203845127178, "336": 0.12009611535381534, "16": 0.04003203845127178, "106": -0.12009611535381534, "380": -0.08006407690254357, "505": 0.04003203845127178, "55": 0.08006407690254357, "4": -0.04003203845127178, "14": -0.04003203845127178, "366": 0.04003203845127178, "385": 0.04003203845127178, "300": 0.04003203845127178, "138": 0.04003203845127178, "270": 0.12009611535381534, "102": -0.04003203845127178, "441": -0.08006407690254357, "399": -0.04003203845127178, "164": 0.04003203845127178, "84": 0.04003203845127178, "38": -0.08006407690254357, "319": -0.04003203845127178, "355": -0.04003203845127178, "257": -0.04003203845127178, "488": 0.04003203845127178, "325": -0.04003203845127178, "152": 0.04003203845127178, "7": -0.04003203845127178, "504": 0.04003203845127178, "159": 0.04003203845127178, "15": 0.04003203845127178, "153": -0.04003203845127178, "448": -0.04003203845127178, "483": 0.04003203845127178, "200": -0.04003203845127178, "459": 0.08006407690254357, "39": 0.04003203845127178, "291": -0.08006407690254357, "396": 0.08006407690254357, "276": 0.04003203845127178, "337": 0.08006407690254357, "99": -0.08006407690254357, "272": 0.04003203845127178, "258": 0.04003203845127178, "501": -0.04003203845127178, "450": -0.04003203845127178, "234": -0.04003203845127178 } }, { "id": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py#6", "path": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py", "chunk": 6, "text": "pilot_suite, ), } def send(payload: dict[str, object]) -> None: sys.stdout.write(json.dumps(payload) + \"\\n\") sys.stdout.flush() def handle(request: dict[str, Any]) -> dict[str, object]: method = request.get(\"method\") request_id = request.get(\"id\") try: if method == \"initialize\": return { \"jsonrpc\": \"2.0\", \"id\": request_id, \"result\": { \"protocolVersion\": \"2024-11-05\", \"capabilities\": {\"tools\": {}}, \"serverInfo\": {\"name\": \"hops-memory-fabric\", \"version\": \"0.1.0\"}, }, } if method == \"tools/list\": return { \"jsonrpc\": \"2.0\", \"id\": request_id, \"result\": { \"tools\": [ {\"name\": name, \"description\": desc, \"inputSchema\": schema} for name, (desc, schema, _func) in TOOLS.items() ] }, } if method == \"tools/call\": params = request.get(\"params\", {}) name = params.get(\"name\") arguments = params.get(\"arguments\", {}) if name not in TOOLS: raise KeyError(f\"unknown tool: {name}\") result = TOOLS[name][2](arguments) return {\"jsonrpc\": \"2.0\", \"id\": request_id, \"result\": result} if method == \"notifications/initialized\": return {} return { \"jsonrpc\": \"2.0\", \"id\": request_id, \"error\": {\"code\": -32601, \"message\": f\"method not found: {method}\"}, } except Exception as exc: return { \"jsonrpc\": \"2.0\", \"id\": reque", "tokens": 140, "vector": { "483": 0.044721359549995794, "459": 0.08944271909999159, "39": 0.044721359549995794, "291": -0.08944271909999159, "106": -0.13416407864998736, "396": 0.13416407864998736, "41": -0.13416407864998736, "276": 0.044721359549995794, "337": 0.08944271909999159, "99": -0.08944271909999159, "272": 0.044721359549995794, "258": 0.044721359549995794, "336": 0.044721359549995794, "501": -0.044721359549995794, "450": -0.044721359549995794, "31": -0.17888543819998318, "470": -0.044721359549995794, "91": 0.35777087639996635, "371": 0.22360679774997896, "421": 0.2683281572999747, "30": -0.044721359549995794, "403": -0.22360679774997896, "71": -0.044721359549995794, "369": 0.2683281572999747, "231": 0.22360679774997896, "246": -0.22360679774997896, "399": 0.044721359549995794, "357": -0.044721359549995794, "79": 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-0.044721359549995794, "248": 0.044721359549995794, "503": 0.044721359549995794 } }, { "id": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py#7", "path": "plugins/hops-memory-fabric/scripts/hops_memory_mcp.py", "chunk": 7, "text": " \"2.0\", \"id\": request_id, \"error\": {\"code\": -32601, \"message\": f\"method not found: {method}\"}, } except Exception as exc: return { \"jsonrpc\": \"2.0\", \"id\": request_id, \"error\": {\"code\": -32000, \"message\": str(exc)}, } def main() -> None: for line in sys.stdin: if not line.strip(): continue response = handle(json.loads(line)) if response: send(response) if __name__ == \"__main__\": main()", "tokens": 50, "vector": { "421": 0.21566554640687682, "371": 0.21566554640687682, "202": 0.21566554640687682, "86": 0.21566554640687682, "288": 0.10783277320343841, "427": 0.21566554640687682, "91": 0.21566554640687682, "293": -0.21566554640687682, "188": -0.10783277320343841, "177": -0.10783277320343841, "33": 0.10783277320343841, "500": -0.10783277320343841, "248": 0.21566554640687682, "369": 0.10783277320343841, "231": 0.10783277320343841, "329": 0.10783277320343841, "396": 0.10783277320343841, "459": 0.10783277320343841, "13": -0.21566554640687682, "276": 0.10783277320343841, "16": 0.10783277320343841, "312": -0.3234983196103152, "344": -0.10783277320343841, "337": 0.10783277320343841, "425": -0.10783277320343841, "403": -0.3234983196103152, "470": -0.10783277320343841, "226": 0.10783277320343841, "169": 0.3234983196103152, "450": -0.10783277320343841, "258": 0.10783277320343841, "193": 0.10783277320343841, "39": 0.10783277320343841, "34": -0.10783277320343841, "333": 0.10783277320343841 } }, { "id": "plugins/hops-memory-fabric/skills/hops-memory-fabric/SKILL.md#0", "path": "plugins/hops-memory-fabric/skills/hops-memory-fabric/SKILL.md", "chunk": 0, "text": "--- name: hops-memory-fabric description: Use when working with the local HOPS memory fabric, paid pilot suite, Fractal Memory Pyramid, provenance-bound HMKs, resonant execution routing, or when asked to remember/retrieve decisions with provenance using the HOPS MCP tools. --- # HOPS Memory Fabric Use this skill when the user asks to improve Codex with HOPS memory, run paid-pilot proofs, route tasks semantically, or persist/retrieve project decisions. ## Workflow 1. Prefer the `hops-memory-fabric` MCP tools when available: - `hops_remember`: store key/value memory with actor/trust/tool provenance. - `hops_retrieve`: recover stored memory through the Fractal Memory Pyramid. - `hops_verify_provenance`: check actor/trust provenance evidence. - `hops_route_task`: choose a semantic execution node. - `hops_fabric_status`: run the memory fabric proof checks. - `hops_run_pilot_suite`: regenerate paid-pilot artifacts. 2. If MCP tools are unavailable, run the workspace scripts directly: - `python3 run_memory_fabric.py` - `python3 run_pilot_suite.py` 3. Keep claims careful: this is a reproducible local proof and pilot harness, not a universal complexity proof. ## Memory Use When storing user ", "tokens": 156, "vector": { "386": 0.0, "152": 0.2727272727272727, "192": -0.5, "173": 0.22727272727272727, "201": 0.0, "419": 0.13636363636363635, "21": 0.22727272727272727, "221": -0.045454545454545456, "262": 0.18181818181818182, "441": -0.3181818181818182, "293": -0.13636363636363635, "38": -0.13636363636363635, "7": -0.18181818181818182, "504": 0.045454545454545456, "96": 0.09090909090909091, "34": 0.09090909090909091, "150": 0.18181818181818182, "94": 0.045454545454545456, "438": 0.045454545454545456, "487": 0.045454545454545456, "164": 0.09090909090909091, "252": 0.045454545454545456, "369": -0.09090909090909091, "454": -0.045454545454545456, "102": -0.09090909090909091, "309": 0.045454545454545456, "80": -0.09090909090909091, "305": -0.09090909090909091, "162": -0.045454545454545456, "16": -0.13636363636363635, "505": -0.13636363636363635, "286": -0.09090909090909091, "412": 0.0, "4": -0.09090909090909091, "299": 0.045454545454545456, "87": -0.045454545454545456, "172": -0.13636363636363635, "72": 0.09090909090909091, "138": 0.045454545454545456, "264": 0.045454545454545456, "133": 0.045454545454545456, "284": -0.045454545454545456, "36": 0.045454545454545456, "474": 0.045454545454545456, "327": 0.045454545454545456, "477": 0.045454545454545456, "178": -0.045454545454545456, "44": 0.045454545454545456, "205": 0.045454545454545456, "336": 0.09090909090909091, "247": 0.13636363636363635, "380": -0.045454545454545456, "171": 0.045454545454545456, "492": 0.045454545454545456, "135": 0.045454545454545456, "32": -0.045454545454545456, "238": 0.045454545454545456, "300": 0.045454545454545456, "370": 0.09090909090909091, "55": -0.045454545454545456, "84": 0.045454545454545456, "224": -0.045454545454545456, "449": 0.13636363636363635, "406": 0.045454545454545456, "488": 0.045454545454545456, "325": -0.045454545454545456, "15": 0.045454545454545456, "403": -0.045454545454545456, "186": 0.045454545454545456, "442": 0.045454545454545456, "289": -0.045454545454545456, "396": 0.045454545454545456, "402": -0.045454545454545456, "193": 0.09090909090909091, "366": 0.09090909090909091, "432": 0.045454545454545456, "364": 0.045454545454545456, "166": 0.045454545454545456, "25": -0.045454545454545456, "506": -0.045454545454545456, "9": 0.045454545454545456, "354": -0.045454545454545456, "329": 0.045454545454545456, "425": 0.045454545454545456, "415": 0.045454545454545456 } }, { "id": "plugins/hops-memory-fabric/skills/hops-memory-fabric/SKILL.md#1", "path": "plugins/hops-memory-fabric/skills/hops-memory-fabric/SKILL.md", "chunk": 1, "text": "pilot_suite.py` 3. Keep claims careful: this is a reproducible local proof and pilot harness, not a universal complexity proof. ## Memory Use When storing user decisions, use concise keys and provenance: ```text key: pilot_positioning value: paid discovery pilot with exact baselines actor: user trust: high tool: codex ``` Do not store secrets, credentials, private personal data, or anything the user has not asked to persist. ## Verification For production-facing changes: ```bash python3 -m unittest discover -s tests python3 run_pilot_suite.py ```", "tokens": 74, "vector": { "483": 0.09805806756909202, "366": 0.29417420270727607, "364": 0.09805806756909202, "166": 0.09805806756909202, "25": -0.09805806756909202, "286": -0.09805806756909202, "506": 0.0, "9": 0.09805806756909202, "293": -0.3922322702763681, "449": 0.19611613513818404, "354": -0.19611613513818404, "7": -0.19611613513818404, "329": 0.09805806756909202, "425": 0.09805806756909202, "415": 0.09805806756909202, "192": -0.09805806756909202, "419": 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\"target_corruption\": 0.1, \"target_resonance\": 0.0142, \"max_background_resonance\": 0.0404, \"snr\": 0.3514851485148515, \"top1_index\": 953, \"top1_correct\": false, \"top5_correct\": false, \"trial_number\": 1209 }, { \"domain\": \"complex\", \"bundle_mode\": \"raw\", \"dimension\": 10000, \"branches\": 1000, \"target_index\": 0, \"distractor_similarity\": 0.25, \"target_corruption\": 0.1, \"target_resonance\": 1.0621256313851815, \"max_background_resonance\": 1.388652729409804, \"snr\": 0.7648605075198311, \"to", "tokens": 87, "vector": { "433": 0.06286946134619315, "439": -0.2514778453847726, "5": -0.2514778453847726, "187": 0.2514778453847726, "326": -0.06286946134619315, "317": -0.2514778453847726, "273": 0.06286946134619315, "24": -0.2514778453847726, "105": 0.06286946134619315, "383": 0.18860838403857944, "45": -0.18860838403857944, "181": 0.18860838403857944, "182": 0.18860838403857944, "151": 0.18860838403857944, "234": 0.06286946134619315, "498": 0.18860838403857944, 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\"top1_index\": 63, \"top1_correct\": false, \"top5_correct\": true, \"trial_number\": 1210 }, { \"domain\": \"bipolar\", \"bundle_mode\": \"raw\", \"dimension\": 10000, \"branches\": 1000, \"target_index\": 0, \"distractor_similarity\": 0.25, \"target_corruption\": 0.1, \"target_resonance\": 0.2528, \"max_background_resonance\": 1.2424, \"snr\": 0.20347714101738573, \"top1_index\": 472, \"top1_correct\": false, \"top5_correct\": false, \"trial_number\": 1211 }, { \"domain\": \"bipolar\", \"bundle_mode\": \"majority\", \"dimension\": 10000, \"branches\": 1000, \"target_index\": 0, \"distractor_similarity\": 0.25, \"target_corruption\": 0.1, \"target_resonance\": -0.0062, \"max_background_resonance\": 0.0364, \"snr\": -0.1703296703296703, \"top1_index\": 221, \"top1_correct\": false, \"top5_correct\": false, \"trial_number\": 1212 }, { \"domain\": \"complex\", \"bundle_mode\": \"raw\", \"dimension\": 10000, \"branches\": 1000, \"target_index\": 0, \"distractor_similarity\": 0.25, 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\"top1_index\": 124, \"top1_correct\": false, \"top5_correct\": false, \"trial_number\": 1214 }, { \"domain\": \"bipolar\", \"bundle_mode\": \"majority\", \"dimension\": 10000, \"branches\": 1000, \"target_index\": 0, \"distractor_similarity\": 0.25, \"target_corruption\": 0.1, \"target_resonance\": 0.0346, \"max_background_resonance\": 0.0422, \"snr\": 0.8199052132701421, \"top1_index\": 113, \"top1_correct\": false, \"top5_correct\": true, \"trial_number\": 1215 } ]", "tokens": 68, "vector": { "243": 0.07905694150420949, "439": -0.23717082451262844, "5": -0.23717082451262844, "187": 0.23717082451262844, "57": -0.07905694150420949, "317": -0.23717082451262844, "310": 0.07905694150420949, "24": -0.23717082451262844, "208": 0.07905694150420949, "383": 0.23717082451262844, "45": -0.23717082451262844, "181": 0.23717082451262844, "182": 0.23717082451262844, "151": 0.23717082451262844, "478": -0.07905694150420949, "498": 0.15811388300841897, "457": -0.15811388300841897, "320": 0.31622776601683794, "471": 0.07905694150420949, "328": -0.15811388300841897, "313": -0.15811388300841897, "476": -0.15811388300841897, "363": -0.15811388300841897, "175": 0.15811388300841897, "259": 0.07905694150420949, "72": 0.07905694150420949, "509": 0.07905694150420949, "362": -0.07905694150420949, "252": 0.23717082451262844, "344": -0.07905694150420949, "290": 0.07905694150420949, "108": -0.07905694150420949, "248": -0.07905694150420949, "287": -0.07905694150420949, "364": -0.07905694150420949, "231": -0.07905694150420949 } }, { "id": "results/fast/report.md#0", "path": "results/fast/report.md", "chunk": 0, "text": "# Everett Engine Scientific Harness Report Generated by `run_benchmark.py`. ## Summary - Raw trials: `1215` - Aggregate conditions: `243` - Runtime: `111.94` seconds - Modes: `complex/raw`, `bipolar/raw`, `bipolar/majority` This report evaluates empirical retrieval behavior for a precomputed speculative superposition. It does not claim free planning from scratch: constructing `W_future` is proportional to the number of encoded branches and the vector dimension. Retrieval is algebraic over the precomputed world vector, followed by action dictionary scoring. ## Nominal Conditions Nominal rows use `distractor_similarity=0.0` and `target_corruption=0.0`. | mode | D | N | median SNR | top1 acc | target mean | bg mean | | --- | --- | --- | --- | --- | --- | --- | | bipolar/majority | 1000 | 10 | 5.07 | 1.00 | 0.257 | 0.052 | | bipolar/raw | 1000 | 10 | 5.71 | 1.00 | 1.040 | 0.172 | | complex/raw | 1000 | 10 | 15.03 | 1.00 | 1.031 | 0.079 | | bipolar/majority | 1000 | 100 | 0.67 | 0.40 | 0.059 | 0.076 | | bipolar/raw | 1000 | 100 | 1.57 | 0.60 | 1.025 | 0.803 | | complex/raw | 1000 | 100 | 1.26 | 0.80 | 0.811 | 0.558 | | bipolar/majority | 1000 | 1000 | 0.43 | 0.00 | 0.041 | 0.099 | | bip", "tokens": 147, "vector": { "430": -0.05277798139692595, "493": 0.05277798139692595, "133": 0.05277798139692595, "329": 0.0, "132": -0.1055559627938519, "480": 0.05277798139692595, "167": -0.1055559627938519, "194": -0.05277798139692595, "366": 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0.05277798139692595, "51": 0.05277798139692595, "310": -0.05277798139692595, "456": 0.05277798139692595, "57": 0.05277798139692595, "79": 0.05277798139692595, "159": 0.05277798139692595, "221": -0.05277798139692595, "324": 0.05277798139692595, "135": 0.05277798139692595, "73": 0.05277798139692595, "394": 0.05277798139692595 } }, { "id": "results/fast/report.md#1", "path": "results/fast/report.md", "chunk": 1, "text": " 1.57 | 0.60 | 1.025 | 0.803 | | complex/raw | 1000 | 100 | 1.26 | 0.80 | 0.811 | 0.558 | | bipolar/majority | 1000 | 1000 | 0.43 | 0.00 | 0.041 | 0.099 | | bipolar/raw | 1000 | 1000 | 0.40 | 0.20 | 1.386 | 3.115 | | complex/raw | 1000 | 1000 | 0.28 | 0.00 | 0.477 | 2.451 | | bipolar/majority | 5000 | 10 | 11.31 | 1.00 | 0.239 | 0.022 | | bipolar/raw | 5000 | 10 | 12.70 | 1.00 | 0.976 | 0.078 | | complex/raw | 5000 | 10 | 21.61 | 1.00 | 1.012 | 0.044 | | bipolar/majority | 5000 | 100 | 1.64 | 1.00 | 0.082 | 0.040 | | bipolar/raw | 5000 | 100 | 3.15 | 1.00 | 1.096 | 0.328 | | complex/raw | 5000 | 100 | 4.07 | 1.00 | 0.989 | 0.232 | | bipolar/majority | 5000 | 1000 | 0.80 | 0.00 | 0.032 | 0.045 | | bipolar/raw | 5000 | 1000 | 0.45 | 0.00 | 0.774 | 1.476 | | complex/raw | 5000 | 1000 | 0.76 | 0.20 | 0.848 | 1.055 | ## Capacity Indicators | mode | first top1 < 95% | first median SNR < 2 | best condition | worst condition | | --- | --- | --- | --- | --- | | bipolar/majority | D=1000, N=100, sim=0, corr=0, top1=0.40, median SNR=0.67 | D=1000, N=100, sim=0, corr=0, top1=0.40, median SNR=0.67 | D=5000, N=10, sim=0, corr=0.05, top1=1.00, median SNR=19.39 | D=1000, N=1000, sim=0.1, corr=0.05", "tokens": 160, "vector": { "456": 0.034836650714580884, "57": 0.034836650714580884, "79": 0.06967330142916177, "159": 0.034836650714580884, "490": 0.17418325357290443, "471": 0.31352985643122794, "320": 0.48771311000413237, "429": -0.2090199042874853, "95": 0.034836650714580884, "72": 0.06967330142916177, "444": 0.034836650714580884, "221": -0.034836650714580884, "457": 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0.034836650714580884, "370": -0.034836650714580884, "43": 0.034836650714580884, "385": 0.034836650714580884, "8": 0.034836650714580884, "231": 0.034836650714580884, "247": -0.034836650714580884, "478": -0.034836650714580884, "365": -0.034836650714580884, "492": -0.034836650714580884, "68": 0.034836650714580884, "230": -0.034836650714580884, "423": 0.06967330142916177, "173": -0.034836650714580884, "120": -0.13934660285832354, "24": -0.13934660285832354, "399": -0.034836650714580884, "321": 0.034836650714580884, "80": -0.13934660285832354, "132": -0.13934660285832354, "329": -0.06967330142916177, "148": 0.034836650714580884, "267": -0.034836650714580884 } }, { "id": "results/fast/report.md#2", "path": "results/fast/report.md", "chunk": 2, "text": "67 | D=1000, N=100, sim=0, corr=0, top1=0.40, median SNR=0.67 | D=5000, N=10, sim=0, corr=0.05, top1=1.00, median SNR=19.39 | D=1000, N=1000, sim=0.1, corr=0.05, top1=0.00, median SNR=0.08 | | bipolar/raw | D=1000, N=100, sim=0, corr=0, top1=0.60, median SNR=1.57 | D=1000, N=100, sim=0, corr=0, top1=0.60, median SNR=1.57 | D=10000, N=10, sim=0, corr=0, top1=1.00, median SNR=23.90 | D=1000, N=1000, sim=0.25, corr=0, top1=0.00, median SNR=0.09 | | complex/raw | D=1000, N=100, sim=0, corr=0, top1=0.80, median SNR=1.26 | D=1000, N=100, sim=0, corr=0, top1=0.80, median SNR=1.26 | D=10000, N=10, sim=0, corr=0.05, top1=1.00, median SNR=35.56 | D=1000, N=1000, sim=0.25, corr=0, top1=0.00, median SNR=0.06 | ## Outputs - Raw trials: `raw_trials.csv`, `raw_trials.json` - Aggregate summaries: `aggregate.csv`, `aggregate.json` - Report: `report.md` ## Plots - `results/fast/plots/snr_vs_branches.png` - `results/fast/plots/accuracy_vs_branches.png` - `results/fast/plots/corruption_sensitivity.png`", "tokens": 144, "vector": { "329": -0.06415002990995841, "320": 0.3528251645047713, "429": -0.16037507477489604, "80": -0.3528251645047713, "132": -0.4169751944147297, "21": -0.3528251645047713, "213": 0.032075014954979206, "120": 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"249": -0.032075014954979206, "56": 0.032075014954979206 } }, { "id": "results/hops/hops_results.csv#0", "path": "results/hops/hops_results.csv", "chunk": 0, "text": "baseline,domain,hops_solution,metric,note,problem,proved 1 exact brute-force solution,SAT,\"(True, False, True)\",4/4 clauses,Validated against exhaustive enumeration.,3-variable clause system,True \"exact brute force [5, 10, 13, 14, 17]\",Portfolio,\"[5, 10, 13, 14, 17]\",HOPS score 0.8021 vs exact 0.8021,\"Exact baseline searched all C(20,5)=15504 portfolios.\",20 assets choose exactly 5,True \"nearest-depot exact [0, 0, 0, 1, 1, 1, 2, 2, 2]\",Routing,\"[0, 0, 0, 1, 1, 1, 2, 2, 2]\",HOPS cost 4.2909 vs exact 4.2909,\"This benchmark validates assignment constraints, not route ordering.\",9 cities assigned to 3 fleets,True initial random conformation,HP Folding,8 continuous fold angles,energy -1.5858 -> -3.2124,Energy descent demonstration; no global optimum guarantee claimed.,2D HP chain HPPHHPHPPH,True", "tokens": 101, 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"results/hops/hops_results.json", "chunk": 0, "text": "{ \"rows\": [ { \"domain\": \"SAT\", \"problem\": \"3-variable clause system\", \"hops_solution\": \"(True, False, True)\", \"baseline\": \"1 exact brute-force solution\", \"metric\": \"4/4 clauses\", \"proved\": true, \"note\": \"Validated against exhaustive enumeration.\" }, { \"domain\": \"Portfolio\", \"problem\": \"20 assets choose exactly 5\", \"hops_solution\": \"[5, 10, 13, 14, 17]\", \"baseline\": \"exact brute force [5, 10, 13, 14, 17]\", \"metric\": \"HOPS score 0.8021 vs exact 0.8021\", \"proved\": true, \"note\": \"Exact baseline searched all C(20,5)=15504 portfolios.\" }, { \"domain\": \"Routing\", \"problem\": \"9 cities assigned to 3 fleets\", \"hops_solution\": \"[0, 0, 0, 1, 1, 1, 2, 2, 2]\", \"baseline\": \"nearest-depot exact [0, 0, 0, 1, 1, 1, 2, 2, 2]\", \"metric\": \"HOPS cost 4.2909 vs exact 4.2909\", \"proved\": true, \"note\": \"This benchmark validates assignment constraints, not route ordering.\" }, { 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-0.24253562503633297, "301": 0.0, "380": -0.24253562503633297, "505": -0.24253562503633297, "185": 0.24253562503633297, "128": 0.24253562503633297, "134": 0.24253562503633297, "80": 0.24253562503633297, "377": -0.24253562503633297, "217": -0.24253562503633297, "97": -0.24253562503633297, "382": -0.24253562503633297, "181": 0.24253562503633297 } }, { "id": "results/hops/site/index.html#0", "path": "results/hops/site/index.html", "chunk": 0, "text": " HOPS Proof Dashboard

HOPS Proof Dashboard

One continuous optimization pattern is exercised across SAT, portfolio selection, fleet assignment, and HP folding. The dashboard reports reproducible benchmark evidence with exact baselines where this toy suite makes exact validation tractable.

PASS

SAT

3-variable clause system

HOPS solution
(True, False, True)
Baseline
1 exact brute-force solution
Metric
4/4 clauses

Validated against exhaustive enumeration.

PASS

Portfolio

20 assets choose exactly 5

HOPS solution
[5, 10, 13, 14, 17]
Baseline
exact brute force [5, 10, 13, 14, 17]
Metric
HOPS score 0.8021 vs exact 0.8021

Exact basel", "tokens": 175, "vector": { "155": -0.5033620102305382, "308": -0.07744030926623663, "53": -0.038720154633118314, "312": -0.07744030926623663, "57": 0.07744030926623663, "159": 0.0, "148": 0.038720154633118314, "240": 0.038720154633118314, "476": -0.038720154633118314, "237": 0.0, "40": -0.11616046389935496, "456": 0.038720154633118314, "116": -0.07744030926623663, "250": 0.038720154633118314, "79": 0.038720154633118314, "147": -0.07744030926623663, "331": -0.038720154633118314, "123": 0.038720154633118314, "176": 0.038720154633118314, "171": 0.038720154633118314, "86": 0.038720154633118314, "98": 0.038720154633118314, "203": -0.038720154633118314, "291": 0.038720154633118314, "13": -0.038720154633118314, "307": 0.07744030926623663, "152": 0.1936007731655916, "449": 0.038720154633118314, "358": 0.07744030926623663, "264": 0.3097612370649465, "118": 0.0, "503": -0.038720154633118314, "190": 0.038720154633118314, "287": -0.038720154633118314, "506": 0.1936007731655916, "460": 0.038720154633118314, "478": 0.038720154633118314, "51": 0.07744030926623663, "14": 0.07744030926623663, "101": 0.038720154633118314, "94": 0.038720154633118314, "206": 0.038720154633118314, "354": -0.038720154633118314, "87": 0.038720154633118314, "441": -0.038720154633118314, "127": -0.038720154633118314, "9": 0.038720154633118314, "270": 0.038720154633118314, "238": 0.038720154633118314, "262": 0.038720154633118314, "436": 0.07744030926623663, "286": -0.038720154633118314, "271": -0.038720154633118314, "504": 0.038720154633118314, "495": -0.038720154633118314, "47": -0.038720154633118314, "461": 0.1936007731655916, "15": -0.038720154633118314, "63": -0.11616046389935496, "387": -0.07744030926623663, "7": 0.07744030926623663, "471": -0.07744030926623663, "249": -0.15488061853247326, "71": 0.07744030926623663, "59": 0.038720154633118314, "157": 0.038720154633118314, "443": -0.038720154633118314, "44": 0.15488061853247326, "510": -0.46464185559741983, "130": -0.15488061853247326, "181": 0.07744030926623663, "252": 0.038720154633118314, "139": -0.07744030926623663, "12": -0.07744030926623663, "50": 0.07744030926623663, "184": -0.07744030926623663, "227": -0.038720154633118314, "167": 0.038720154633118314, "324": -0.038720154633118314, "245": 0.038720154633118314, "395": 0.038720154633118314, "370": 0.038720154633118314, "36": -0.038720154633118314, "74": 0.07744030926623663, "319": 0.07744030926623663, "389": 0.07744030926623663, "325": -0.07744030926623663, "421": 0.038720154633118314, "474": -0.07744030926623663, "241": 0.038720154633118314, "19": -0.038720154633118314 } }, { "id": "results/hops/site/index.html#2", "path": "results/hops/site/index.html", "chunk": 2, "text": " 13, 14, 17]

Baseline
exact brute force [5, 10, 13, 14, 17]
Metric
HOPS score 0.8021 vs exact 0.8021

Exact baseline searched all C(20,5)=15504 portfolios.

PASS

Routing

9 cities assigned to 3 fleets

HOPS solution
[0, 0, 0, 1, 1, 1, 2, 2, 2]
Baseline
nearest-depot exact [0, 0, 0, 1, 1, 1, 2, 2, 2]
Metric
HOPS cost 4.2909 vs exact 4.2909

This benchmark validates assignment constraints, not route ordering.

PASS

HP Folding

2D HP chain HPPHHPHPPH

HOPS solution
8 continuous fold angles
Baseline
initial random conformation
Metric
energy -1.5858 -> -3.2124

Energy descent demonstration; no global optimum guarantee claimed.

Careful claim: this proves the local benchmark suite ran and validated successfully. It does not prove that NP-hardness is eliminated or that arbitrary instances are solve", "tokens": 173, "vector": { "319": 0.06738171205292091, "389": 0.06738171205292091, "325": -0.06738171205292091, "155": -0.5727445524498277, "510": -0.5390536964233673, "139": -0.13476342410584183, "506": 0.13476342410584183, "12": -0.03369085602646046, "50": 0.03369085602646046, "74": 0.03369085602646046, "184": -0.10107256807938136, "152": 0.13476342410584183, "421": 0.03369085602646046, "474": -0.06738171205292091, "241": 0.06738171205292091, "44": 0.16845428013230226, "370": 0.03369085602646046, "350": 0.03369085602646046, "245": 0.03369085602646046, "232": -0.03369085602646046, "106": 0.03369085602646046, "63": -0.16845428013230226, "264": 0.2358359921852232, "387": -0.06738171205292091, "461": 0.16845428013230226, "7": 0.06738171205292091, "471": -0.10107256807938136, "249": -0.13476342410584183, "252": 0.03369085602646046, "71": 0.06738171205292091, "83": 0.03369085602646046, "302": 0.03369085602646046, "102": -0.03369085602646046, "14": -0.03369085602646046, "130": -0.06738171205292091, "330": 0.03369085602646046, "186": 0.06738171205292091, "159": -0.03369085602646046, "105": 0.10107256807938136, "286": -0.06738171205292091, "270": 0.06738171205292091, "206": 0.06738171205292091, "482": 0.03369085602646046, "293": -0.03369085602646046, "138": 0.03369085602646046, "494": 0.03369085602646046, "147": -0.10107256807938136, "87": 0.03369085602646046, "504": 0.0, "148": 0.03369085602646046, "311": -0.03369085602646046, "503": -0.03369085602646046, "110": -0.03369085602646046, "428": -0.03369085602646046, "420": -0.03369085602646046, "89": 0.03369085602646046, "56": 0.06738171205292091, "194": -0.03369085602646046, "112": -0.03369085602646046, "190": 0.03369085602646046, "384": -0.03369085602646046, "96": 0.03369085602646046, "327": 0.03369085602646046, "237": 0.03369085602646046, "25": -0.03369085602646046, "477": 0.03369085602646046, "280": -0.03369085602646046, "441": -0.03369085602646046, "200": 0.03369085602646046, "354": -0.03369085602646046, "227": -0.03369085602646046, "183": -0.03369085602646046, "42": -0.03369085602646046, "150": -0.03369085602646046, "82": 0.03369085602646046, "398": 0.06738171205292091, "266": -0.03369085602646046, "429": 0.03369085602646046, "369": -0.03369085602646046, "135": -0.03369085602646046, "32": -0.03369085602646046, "193": 0.03369085602646046 } }, { "id": "results/hops/site/index.html#3", "path": "results/hops/site/index.html", "chunk": 3, "text": "im: this proves the local benchmark suite ran and validated successfully. It does not prove that NP-hardness is eliminated or that arbitrary instances are solved in constant time. Use hops_results.csv and hops_results.json for audit.

", "tokens": 44, "vector": { "168": -0.1270001270001905, "286": -0.1270001270001905, "280": -0.1270001270001905, "441": -0.1270001270001905, "293": -0.1270001270001905, "270": 0.1270001270001905, "504": 0.1270001270001905, "200": 0.1270001270001905, "354": -0.254000254000381, "227": -0.1270001270001905, "183": -0.1270001270001905, "42": -0.1270001270001905, "150": -0.254000254000381, "82": 0.1270001270001905, "398": 0.254000254000381, "266": -0.1270001270001905, "506": -0.1270001270001905, "429": 0.1270001270001905, "369": -0.1270001270001905, "135": -0.1270001270001905, "32": -0.1270001270001905, "186": 0.1270001270001905, "458": -0.1270001270001905, "344": -0.1270001270001905, "51": -0.1270001270001905, "419": 0.1270001270001905, "86": 0.508000508000762, "128": -0.254000254000381, "339": 0.1270001270001905, "258": 0.1270001270001905, "16": 0.1270001270001905, "440": -0.1270001270001905, "13": -0.1270001270001905, "291": 0.1270001270001905, "229": -0.1270001270001905 } }, { "id": "results/memory_fabric/fabric_report.md#0", "path": "results/memory_fabric/fabric_report.md", "chunk": 0, "text": "# Memory-Native Compute Fabric Report - Cases: `9` - Passed: `9` - Success rate: `1.00` This proof harness demonstrates shared complex-hypervector operations for tiered retrieval, provenance scoring, and semantic execution routing. The provenance score is a vector threshold, not a cryptographic signature. | domain | case/query/task | result | success | | --- | --- | --- | --- | | fractal_memory_pyramid | policy_limit | 250000 | True | | fractal_memory_pyramid | preferred_region | midwest | True | | fractal_memory_pyramid | risk_model | covariance_v3 | True | | fractal_memory_pyramid | folding_model | hp2d | True | | provenance_hmk | trusted_user_memory | True | True | | provenance_hmk | untrusted_agent_memory | False | True | | resonant_execution_mesh | last-mile-route | edge-router-01 | True | | resonant_execution_mesh | sat-batch-anneal | gpu-annealer-01 | True | | resonant_execution_mesh | hmk-audit | audit-ledger-01 | True |", "tokens": 89, "vector": { "192": -0.06984302957695782, "446": -0.06984302957695782, "176": 0.06984302957695782, "173": 0.06984302957695782, "132": -0.06984302957695782, "309": 0.0, "296": -0.06984302957695782, "174": -0.13968605915391563, "453": -0.06984302957695782, "497": -0.06984302957695782, "286": -0.06984302957695782, "449": 0.06984302957695782, "329": 0.06984302957695782, "114": -0.06984302957695782, "164": 0.0, "490": 0.06984302957695782, "189": -0.06984302957695782, "116": -0.06984302957695782, "16": 0.06984302957695782, "366": 0.0, "150": 0.13968605915391563, "364": 0.06984302957695782, "354": -0.06984302957695782, "55": -0.06984302957695782, "252": 0.13968605915391563, "441": -0.06984302957695782, "421": 0.06984302957695782, "506": -0.06984302957695782, "432": 0.06984302957695782, "73": 0.0, "293": -0.06984302957695782, "61": 0.06984302957695782, "282": -0.06984302957695782, "498": 0.06984302957695782, "187": 0.06984302957695782, "424": -0.06984302957695782, "270": 0.06984302957695782, "246": -0.06984302957695782, "320": 0.27937211830783126, "370": 0.06984302957695782, "501": 0.06984302957695782, "181": 0.6984302957695782, "434": -0.06984302957695782, "385": -0.06984302957695782, "397": 0.06984302957695782, "488": -0.06984302957695782, "65": -0.06984302957695782, "345": 0.06984302957695782, "284": -0.13968605915391563, "43": -0.06984302957695782, "399": -0.20952908873087345, "415": 0.06984302957695782, "138": 0.06984302957695782, "417": -0.06984302957695782, "372": 0.06984302957695782, "403": 0.20952908873087345, "51": 0.06984302957695782, "75": 0.06984302957695782, "348": 0.06984302957695782, "483": -0.06984302957695782, "103": 0.06984302957695782, "31": 0.06984302957695782, "440": -0.13968605915391563, "254": -0.06984302957695782 } }, { "id": "results/memory_fabric/fabric_results.csv#0", "path": "results/memory_fabric/fabric_results.csv", "chunk": 0, "text": "accepted,actor,actor_similarity,authorized_actor,case,confidence,domain,expected,expected_node,query,repaired,resonance,selected_node,success,task,tier,trust_similarity,value ,,,,,0.7075493385848546,fractal_memory_pyramid,250000,,policy_limit,False,,,True,,tier3_rsm,,250000 ,,,,,0.7079062054751789,fractal_memory_pyramid,midwest,,preferred_region,False,,,True,,tier2_hkvc,,midwest ,,,,,1.0,fractal_memory_pyramid,covariance_v3,,risk_model,False,,,True,,tier1_sparse,,covariance_v3 ,,,,,1.0,fractal_memory_pyramid,hp2d,,folding_model,False,,,True,,tier0_exact,,hp2d True,user,0.5057229799923029,user,trusted_user_memory,,provenance_hmk,True,,,,,,True,,,0.4961552769629266, False,agent,0.017941463254672014,user,untrusted_agent_memory,,provenance_hmk,False,,,,,,True,,,-0.014170222339404862, ,,,,,,resonant_execution_mesh,,edge-router-01,,,0.9999999999999999,edge-router-01,True,last-mile-route,,, ,,,,,,resonant_execution_mesh,,gpu-annealer-01,,,0.9999999999999999,gpu-annealer-01,True,sat-batch-anneal,,, ,,,,,,resonant_execution_mesh,,audit-ledger-01,,,1.0000000000000002,audit-ledger-01,True,hmk-audit,,,", "tokens": 101, "vector": { "7": 0.055641488407465724, "336": 0.055641488407465724, "242": 0.055641488407465724, "505": 0.055641488407465724, "187": 0.055641488407465724, "511": 0.055641488407465724, "498": 0.055641488407465724, "206": 0.055641488407465724, "355": -0.055641488407465724, "424": -0.055641488407465724, "315": 0.055641488407465724, "41": 0.055641488407465724, "145": 0.055641488407465724, "174": -0.055641488407465724, "270": 0.055641488407465724, "226": 0.055641488407465724, "244": 0.055641488407465724, "205": 0.055641488407465724, "268": -0.055641488407465724, "320": 0.2225659536298629, "501": 0.11128297681493145, "370": 0.055641488407465724, "252": 0.33384893044479436, "181": 0.612056372482123, "259": 0.055641488407465724, "101": -0.055641488407465724, "385": -0.11128297681493145, "434": -0.055641488407465724, "239": 0.055641488407465724, "488": -0.11128297681493145, "397": 0.055641488407465724, "89": -0.055641488407465724, "345": 0.11128297681493145, "65": -0.055641488407465724, "170": 0.055641488407465724, "4": -0.16692446522239718, "85": -0.055641488407465724, "73": 0.055641488407465724, "284": -0.11128297681493145, "324": 0.055641488407465724, "212": -0.055641488407465724, "414": 0.055641488407465724, "43": -0.055641488407465724, "468": -0.055641488407465724, "399": -0.16692446522239718, "417": -0.11128297681493145, "372": 0.11128297681493145, "403": 0.33384893044479436, "8": 0.11128297681493145, "309": 0.055641488407465724, "415": 0.055641488407465724, "138": 0.055641488407465724, "483": -0.11128297681493145, "103": 0.11128297681493145, "51": 0.055641488407465724, "75": 0.055641488407465724, "348": 0.055641488407465724, "440": -0.16692446522239718, "254": -0.11128297681493145, "256": 0.055641488407465724, "31": 0.055641488407465724 } }, { "id": "results/memory_fabric/fabric_results.csv#1", "path": "results/memory_fabric/fabric_results.csv", "chunk": 1, "text": "it-ledger-01,,,1.0000000000000002,audit-ledger-01,True,hmk-audit,,,", "tokens": 10, "vector": { "42": -0.25, "254": -0.5, "403": 0.5, "256": 0.25, "440": -0.5, "181": 0.25, "31": 0.25 } }, { "id": "results/memory_fabric/fabric_results.json#0", "path": "results/memory_fabric/fabric_results.json", "chunk": 0, "text": "[ { \"query\": \"policy_limit\", \"value\": \"250000\", \"tier\": \"tier3_rsm\", \"confidence\": 0.7075493385848546, \"repaired\": false, \"success\": true, \"domain\": \"fractal_memory_pyramid\", \"expected\": \"250000\" }, { \"query\": \"preferred_region\", \"value\": \"midwest\", \"tier\": \"tier2_hkvc\", \"confidence\": 0.7079062054751789, \"repaired\": false, \"success\": true, \"domain\": \"fractal_memory_pyramid\", \"expected\": \"midwest\" }, { \"query\": \"risk_model\", \"value\": \"covariance_v3\", \"tier\": \"tier1_sparse\", \"confidence\": 1.0, \"repaired\": false, \"success\": true, \"domain\": \"fractal_memory_pyramid\", \"expected\": \"covariance_v3\" }, { \"query\": \"folding_model\", \"value\": \"hp2d\", \"tier\": \"tier0_exact\", \"confidence\": 1.0, \"repaired\": false, \"success\": true, \"domain\": \"fractal_memory_pyramid\", \"expected\": \"hp2d\" }, { \"actor\": \"user\", \"authorized_actor\": \"user\", \"actor_similarity\": 0.5057229799923029, \"trust_similarity\": 0.4961552769629266, \"accepted\": true, \"domain\": \"provenance_hmk\", \"case\": \"trusted_user_memory\", \"expected\": true, \"success\": true }, { \"actor\": \"agent\", \"authorized_actor\": \"user\", \"actor_similarity\": 0.017941463254672014, \"trust_similarity\": -0.014170222339404862, \"accepted\": false, \"domain\": \"provenance_hmk\"", "tokens": 92, "vector": { "424": -0.22291128503014113, "370": 0.05572782125753528, "205": 0.22291128503014113, "501": 0.11145564251507056, "226": 0.22291128503014113, "259": 0.05572782125753528, "511": 0.22291128503014113, "268": -0.05572782125753528, "315": 0.22291128503014113, "252": 0.2786391062876764, "174": -0.2786391062876764, "181": 0.39009474880274697, "498": 0.3343669275452117, "320": 0.22291128503014113, "206": 0.2786391062876764, "434": -0.05572782125753528, "385": -0.11145564251507056, "239": 0.05572782125753528, "101": -0.05572782125753528, "397": 0.05572782125753528, "488": -0.11145564251507056, "89": -0.05572782125753528, "65": -0.05572782125753528, "345": 0.11145564251507056, "170": 0.05572782125753528, "336": 0.11145564251507056, "4": -0.16718346377260584, "505": 0.11145564251507056, "242": 0.11145564251507056, "85": -0.05572782125753528, "244": 0.11145564251507056, "324": 0.05572782125753528, "7": 0.11145564251507056, "284": -0.11145564251507056, "187": 0.05572782125753528, "73": 0.05572782125753528, "212": -0.05572782125753528, "414": 0.05572782125753528, "468": -0.05572782125753528 } }, { "id": "results/memory_fabric/fabric_results.json#1", "path": "results/memory_fabric/fabric_results.json", "chunk": 1, "text": ", \"authorized_actor\": \"user\", \"actor_similarity\": 0.017941463254672014, \"trust_similarity\": -0.014170222339404862, \"accepted\": false, \"domain\": \"provenance_hmk\", \"case\": \"untrusted_agent_memory\", \"expected\": false, \"success\": true }, { \"task\": \"last-mile-route\", \"selected_node\": \"edge-router-01\", \"expected_node\": \"edge-router-01\", \"resonance\": 0.9999999999999999, \"success\": true, \"domain\": \"resonant_execution_mesh\" }, { \"task\": \"sat-batch-anneal\", \"selected_node\": \"gpu-annealer-01\", \"expected_node\": \"gpu-annealer-01\", \"resonance\": 0.9999999999999999, \"success\": true, \"domain\": \"resonant_execution_mesh\" }, { \"task\": \"hmk-audit\", \"selected_node\": \"audit-ledger-01\", \"expected_node\": \"audit-ledger-01\", \"resonance\": 1.0000000000000002, \"success\": true, \"domain\": \"resonant_execution_mesh\" } ]", "tokens": 69, "vector": { "505": 0.07352146220938077, "4": -0.07352146220938077, "242": 0.07352146220938077, "414": 0.07352146220938077, "244": 0.07352146220938077, "468": -0.07352146220938077, "7": 0.07352146220938077, "252": 0.14704292441876154, "498": 0.2940858488375231, "284": -0.07352146220938077, "187": 0.07352146220938077, "43": -0.07352146220938077, "206": 0.07352146220938077, "174": -0.2940858488375231, "181": 0.2940858488375231, "270": 0.22056438662814232, "309": 0.07352146220938077, "415": 0.07352146220938077, "138": 0.07352146220938077, "145": 0.22056438662814232, "417": -0.14704292441876154, "372": 0.14704292441876154, "403": 0.44112877325628463, "355": -0.22056438662814232, "41": 0.22056438662814232, "8": 0.14704292441876154, "399": -0.22056438662814232, "51": 0.07352146220938077, "75": 0.07352146220938077, "348": 0.07352146220938077, "483": -0.14704292441876154, "103": 0.14704292441876154, "31": 0.07352146220938077, "440": -0.22056438662814232, "254": -0.14704292441876154, "256": 0.07352146220938077 } }, { "id": "results/memory_fabric/live_memory.json#0", "path": "results/memory_fabric/live_memory.json", "chunk": 0, "text": "[ { \"key\": \"hops_platform_state\", \"value\": \"paid pilot suite plus HOPS memory fabric plugin and MCP server are implemented and tests pass\", \"actor\": \"user\", \"trust\": \"high\", \"tool\": \"codex\" } ]", "tokens": 25, "vector": { "44": 0.19245008972987526, "433": -0.19245008972987526, "205": 0.19245008972987526, "38": -0.19245008972987526, "7": -0.19245008972987526, "504": 0.19245008972987526, "56": 0.19245008972987526, "152": 0.19245008972987526, "192": -0.19245008972987526, "173": 0.19245008972987526, "94": -0.19245008972987526, "354": -0.3849001794597505, "16": -0.19245008972987526, "179": -0.19245008972987526, "186": 0.19245008972987526, "104": -0.19245008972987526, "345": 0.19245008972987526, "471": -0.19245008972987526, "336": 0.19245008972987526, "4": -0.19245008972987526, "247": 0.19245008972987526, "366": 0.19245008972987526, "380": -0.19245008972987526, "87": -0.19245008972987526 } }, { "id": "results/pilot_suite/hops/dashboard.html#0", "path": "results/pilot_suite/hops/dashboard.html", "chunk": 0, "text": " HOPS Proof Dashboard

HOPS Proof Dashboard

One continuous optimization pattern is exercised across SAT, portfolio selection, fleet assignment, and HP folding. The dashboard reports reproducible benchmark evidence with exact baselines where this toy suite makes exact validation tractable.

PASS

SAT

3-variable clause system

HOPS solution
(True, False, True)
Baseline
1 exact brute-force solution
Metric
4/4 clauses

Validated against exhaustive enumeration.

PASS

Portfolio

20 assets choose exactly 5

HOPS solution
[5, 10, 13, 14, 17]
Baseline
exact brute force [5, 10, 13, 14, 17]
Metric
HOPS score 0.8021 vs exact 0.8021

Exact basel", "tokens": 175, "vector": { "155": -0.5033620102305382, "308": -0.07744030926623663, "53": -0.038720154633118314, "312": -0.07744030926623663, "57": 0.07744030926623663, "159": 0.0, "148": 0.038720154633118314, "240": 0.038720154633118314, "476": -0.038720154633118314, "237": 0.0, "40": -0.11616046389935496, "456": 0.038720154633118314, "116": -0.07744030926623663, "250": 0.038720154633118314, "79": 0.038720154633118314, "147": -0.07744030926623663, "331": -0.038720154633118314, "123": 0.038720154633118314, "176": 0.038720154633118314, "171": 0.038720154633118314, "86": 0.038720154633118314, "98": 0.038720154633118314, "203": -0.038720154633118314, "291": 0.038720154633118314, "13": -0.038720154633118314, "307": 0.07744030926623663, "152": 0.1936007731655916, "449": 0.038720154633118314, "358": 0.07744030926623663, "264": 0.3097612370649465, "118": 0.0, "503": -0.038720154633118314, "190": 0.038720154633118314, "287": -0.038720154633118314, "506": 0.1936007731655916, "460": 0.038720154633118314, "478": 0.038720154633118314, "51": 0.07744030926623663, "14": 0.07744030926623663, "101": 0.038720154633118314, "94": 0.038720154633118314, "206": 0.038720154633118314, "354": -0.038720154633118314, "87": 0.038720154633118314, "441": -0.038720154633118314, "127": -0.038720154633118314, "9": 0.038720154633118314, "270": 0.038720154633118314, "238": 0.038720154633118314, "262": 0.038720154633118314, "436": 0.07744030926623663, "286": -0.038720154633118314, "271": -0.038720154633118314, "504": 0.038720154633118314, "495": -0.038720154633118314, "47": -0.038720154633118314, "461": 0.1936007731655916, "15": -0.038720154633118314, "63": -0.11616046389935496, "387": -0.07744030926623663, "7": 0.07744030926623663, "471": -0.07744030926623663, "249": -0.15488061853247326, "71": 0.07744030926623663, "59": 0.038720154633118314, "157": 0.038720154633118314, "443": -0.038720154633118314, "44": 0.15488061853247326, "510": -0.46464185559741983, "130": -0.15488061853247326, "181": 0.07744030926623663, "252": 0.038720154633118314, "139": -0.07744030926623663, "12": -0.07744030926623663, "50": 0.07744030926623663, "184": -0.07744030926623663, "227": -0.038720154633118314, "167": 0.038720154633118314, "324": -0.038720154633118314, "245": 0.038720154633118314, "395": 0.038720154633118314, "370": 0.038720154633118314, "36": -0.038720154633118314, "74": 0.07744030926623663, "319": 0.07744030926623663, "389": 0.07744030926623663, "325": -0.07744030926623663, "421": 0.038720154633118314, "474": -0.07744030926623663, "241": 0.038720154633118314, "19": -0.038720154633118314 } }, { "id": "results/pilot_suite/hops/dashboard.html#2", "path": "results/pilot_suite/hops/dashboard.html", "chunk": 2, "text": " 13, 14, 17]

Baseline
exact brute force [5, 10, 13, 14, 17]
Metric
HOPS score 0.8021 vs exact 0.8021

Exact baseline searched all C(20,5)=15504 portfolios.

PASS

Routing

9 cities assigned to 3 fleets

HOPS solution
[0, 0, 0, 1, 1, 1, 2, 2, 2]
Baseline
nearest-depot exact [0, 0, 0, 1, 1, 1, 2, 2, 2]
Metric
HOPS cost 4.2909 vs exact 4.2909

This benchmark validates assignment constraints, not route ordering.

PASS

HP Folding

2D HP chain HPPHHPHPPH

HOPS solution
8 continuous fold angles
Baseline
initial random conformation
Metric
energy -1.5858 -> -3.2124

Energy descent demonstration; no global optimum guarantee claimed.

Careful claim: this proves the local benchmark suite ran and validated successfully. It does not prove that NP-hardness is eliminated or that arbitrary instances are solve", "tokens": 173, "vector": { "319": 0.06738171205292091, "389": 0.06738171205292091, "325": -0.06738171205292091, "155": -0.5727445524498277, "510": -0.5390536964233673, "139": -0.13476342410584183, "506": 0.13476342410584183, "12": -0.03369085602646046, "50": 0.03369085602646046, "74": 0.03369085602646046, "184": -0.10107256807938136, "152": 0.13476342410584183, "421": 0.03369085602646046, "474": -0.06738171205292091, "241": 0.06738171205292091, "44": 0.16845428013230226, "370": 0.03369085602646046, "350": 0.03369085602646046, "245": 0.03369085602646046, "232": -0.03369085602646046, "106": 0.03369085602646046, "63": -0.16845428013230226, "264": 0.2358359921852232, "387": -0.06738171205292091, "461": 0.16845428013230226, "7": 0.06738171205292091, "471": -0.10107256807938136, "249": -0.13476342410584183, "252": 0.03369085602646046, "71": 0.06738171205292091, "83": 0.03369085602646046, "302": 0.03369085602646046, "102": -0.03369085602646046, "14": -0.03369085602646046, "130": -0.06738171205292091, "330": 0.03369085602646046, "186": 0.06738171205292091, "159": -0.03369085602646046, "105": 0.10107256807938136, "286": -0.06738171205292091, "270": 0.06738171205292091, "206": 0.06738171205292091, "482": 0.03369085602646046, "293": -0.03369085602646046, "138": 0.03369085602646046, "494": 0.03369085602646046, "147": -0.10107256807938136, "87": 0.03369085602646046, "504": 0.0, "148": 0.03369085602646046, "311": -0.03369085602646046, "503": -0.03369085602646046, "110": -0.03369085602646046, "428": -0.03369085602646046, "420": -0.03369085602646046, "89": 0.03369085602646046, "56": 0.06738171205292091, "194": -0.03369085602646046, "112": -0.03369085602646046, "190": 0.03369085602646046, "384": -0.03369085602646046, "96": 0.03369085602646046, "327": 0.03369085602646046, "237": 0.03369085602646046, "25": -0.03369085602646046, "477": 0.03369085602646046, "280": -0.03369085602646046, "441": -0.03369085602646046, "200": 0.03369085602646046, "354": -0.03369085602646046, "227": -0.03369085602646046, "183": -0.03369085602646046, "42": -0.03369085602646046, "150": -0.03369085602646046, "82": 0.03369085602646046, "398": 0.06738171205292091, "266": -0.03369085602646046, "429": 0.03369085602646046, "369": -0.03369085602646046, "135": -0.03369085602646046, "32": -0.03369085602646046, "193": 0.03369085602646046 } }, { "id": "results/pilot_suite/hops/dashboard.html#3", "path": "results/pilot_suite/hops/dashboard.html", "chunk": 3, "text": "im: this proves the local benchmark suite ran and validated successfully. It does not prove that NP-hardness is eliminated or that arbitrary instances are solved in constant time. Use hops_results.csv and hops_results.json for audit.

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no global optimum guarantee claimed.\" } ], \"payload\": { \"portfolio_probabilities\": [ 0.2496069", "tokens": 126, "vector": { "444": -0.05679618342470648, "498": 0.2271847336988259, "51": 0.05679618342470648, "71": 0.2271847336988259, "59": 0.05679618342470648, "157": 0.05679618342470648, "443": -0.05679618342470648, "505": 0.2271847336988259, "181": 0.3407771005482389, "252": 0.11359236684941296, "139": -0.2839809171235324, "506": 0.3407771005482389, "12": -0.11359236684941296, "50": 0.11359236684941296, "130": -0.11359236684941296, "184": -0.2271847336988259, "429": 0.2271847336988259, "237": 0.2271847336988259, "227": -0.05679618342470648, "167": 0.05679618342470648, "118": 0.05679618342470648, "324": -0.05679618342470648, "14": 0.0, "245": 0.11359236684941296, "395": 0.05679618342470648, "370": 0.11359236684941296, "36": -0.05679618342470648, "74": 0.11359236684941296, "319": 0.11359236684941296, "389": 0.11359236684941296, "325": -0.11359236684941296, "152": 0.11359236684941296, "421": 0.05679618342470648, "474": -0.11359236684941296, "241": 0.11359236684941296, "350": 0.05679618342470648, "232": -0.05679618342470648, "106": 0.05679618342470648, "83": 0.05679618342470648, "302": 0.05679618342470648, "102": -0.05679618342470648, "330": 0.05679618342470648, "186": 0.05679618342470648, "159": -0.05679618342470648, "105": 0.11359236684941296, "286": -0.05679618342470648, "270": 0.05679618342470648, "206": 0.11359236684941296, "482": 0.05679618342470648, "293": 0.0, "138": 0.05679618342470648, "494": 0.05679618342470648, "147": -0.17038855027411945, "87": 0.05679618342470648, "504": -0.05679618342470648, "148": 0.05679618342470648, "311": -0.05679618342470648, "503": -0.05679618342470648, "110": -0.05679618342470648, "428": -0.05679618342470648, "420": -0.05679618342470648, "89": 0.05679618342470648, "56": 0.11359236684941296, "194": -0.05679618342470648, "112": -0.05679618342470648, "190": 0.05679618342470648, "384": -0.05679618342470648, "96": 0.05679618342470648, "327": 0.05679618342470648, "471": -0.05679618342470648, "291": -0.05679618342470648, "85": -0.05679618342470648 } }, { "id": "results/pilot_suite/hops/hops_results.json#1", "path": "results/pilot_suite/hops/hops_results.json", "chunk": 1, "text": " -3.2124\", \"proved\": true, \"note\": \"Energy descent demonstration; no global optimum guarantee claimed.\" } ], \"payload\": { \"portfolio_probabilities\": [ 0.24960695302151892, 0.2669622612591107, 0.22815494707155626, 0.22727063473339315, 0.22499254530241905, 0.37149765376508953, 0.2515288043812409, 0.2757066217029927, 0.23553823777790883, 0.22376457546401582, 0.4414292326762489, 0.26163095122795943, 0.2535419893545981, 0.399997495420195, 0.3998492576257707, 0.2699860906403692, 0.20548717250621906, 0.42330512732716075, 0.23000041491277096, 0.2119810706371779 ], \"routing_distances\": [ [ 0.22360679774997896, 8.200609733428363, 8.947066558375433 ], [ 0.36055512754639896, 7.805767098754613, 9.128526715741156 ], [ 0.6403124237432848, 8.509406559801922, 8.832326986700616 ], [ 7.802563681252464, 0.28284271247461845, 8.676404785393544 ], [ 8.402380615040002, 0.4472135954999582, 9.305912099305473 ], [ 7.532595834106593, 0.8602325267042626, 8.095677859203638 ], [ 8.676404785393546, 8.858893836140041, 0.2828427124746186 ], [ 9.217917335276988, 8.863972021616494, 0.4123105625617663 ], [ 9.154780172128657, 9.664884893261792, 0.7810249675906652 ] ], \"hp_angles\": [ -1.4376064965747861, -1.395127943903", "tokens": 65, "vector": { "112": 0.0, "429": 0.13245323570650439, "181": 0.0, "237": 0.13245323570650439, "56": 0.13245323570650439, "190": 0.13245323570650439, "105": 0.0, "147": -0.13245323570650439, "384": -0.13245323570650439, "96": 0.13245323570650439, "327": 0.13245323570650439, "471": -0.13245323570650439, "291": -0.13245323570650439, "191": -0.13245323570650439, "205": -0.13245323570650439, "225": 0.0, "179": -0.13245323570650439, "428": 0.13245323570650439, "508": -0.13245323570650439, "89": -0.13245323570650439, "379": 0.13245323570650439, "197": 0.13245323570650439, "345": 0.13245323570650439, "153": 0.13245323570650439, "419": -0.13245323570650439, "491": -0.13245323570650439, "42": -0.13245323570650439, "416": -0.13245323570650439, "226": -0.13245323570650439, "372": -0.13245323570650439, "453": -0.13245323570650439, "309": -0.13245323570650439, "423": 0.13245323570650439, "458": 0.13245323570650439, "10": 0.13245323570650439, "235": -0.13245323570650439, "510": -0.13245323570650439, "481": 0.13245323570650439, "189": 0.13245323570650439, "67": 0.13245323570650439, "336": 0.13245323570650439, "394": 0.13245323570650439, "496": 0.13245323570650439, "484": 0.13245323570650439, "413": 0.13245323570650439, "462": -0.13245323570650439, "321": -0.13245323570650439, "273": -0.13245323570650439, "230": 0.13245323570650439, "121": 0.13245323570650439, "474": -0.13245323570650439, "434": -0.13245323570650439, "40": 0.13245323570650439, "139": -0.13245323570650439, "270": 0.13245323570650439, "188": -0.13245323570650439, "301": -0.13245323570650439, "380": -0.13245323570650439, "505": -0.13245323570650439, "185": 0.13245323570650439, "222": 0.13245323570650439 } }, { "id": "results/pilot_suite/hops/hops_results.json#2", "path": "results/pilot_suite/hops/hops_results.json", "chunk": 2, "text": "88, 8.863972021616494, 0.4123105625617663 ], [ 9.154780172128657, 9.664884893261792, 0.7810249675906652 ] ], \"hp_angles\": [ -1.4376064965747861, -1.3951279439035698, 0.33842234190269016, 0.1558626680208898, -1.8108775089679212, 0.29220064535150364, -1.3303544054628718, -1.3753944172233183 ], \"all_proved\": true } }", "tokens": 17, "vector": { "139": -0.48507125007266594, "270": 0.24253562503633297, "188": -0.24253562503633297, "301": 0.0, "380": -0.24253562503633297, "505": -0.24253562503633297, "185": 0.24253562503633297, "128": 0.24253562503633297, "134": 0.24253562503633297, "80": 0.24253562503633297, "377": -0.24253562503633297, "217": -0.24253562503633297, "97": -0.24253562503633297, "382": -0.24253562503633297, "181": 0.24253562503633297 } }, { "id": "results/pilot_suite/memory_fabric/fabric_report.md#0", "path": "results/pilot_suite/memory_fabric/fabric_report.md", "chunk": 0, "text": "# Memory-Native Compute Fabric Report - Cases: `9` - Passed: `9` - Success rate: `1.00` This proof harness demonstrates shared complex-hypervector operations for tiered retrieval, provenance scoring, and semantic execution routing. The provenance score is a vector threshold, not a cryptographic signature. | domain | case/query/task | result | success | | --- | --- | --- | --- | | fractal_memory_pyramid | policy_limit | 250000 | True | | fractal_memory_pyramid | preferred_region | midwest | True | | fractal_memory_pyramid | risk_model | covariance_v3 | True | | fractal_memory_pyramid | folding_model | hp2d | True | | provenance_hmk | trusted_user_memory | True | True | | provenance_hmk | untrusted_agent_memory | False | True | | resonant_execution_mesh | last-mile-route | edge-router-01 | True | | resonant_execution_mesh | sat-batch-anneal | gpu-annealer-01 | True | | resonant_execution_mesh | hmk-audit | audit-ledger-01 | True |", "tokens": 89, "vector": { "192": -0.06984302957695782, "446": -0.06984302957695782, "176": 0.06984302957695782, "173": 0.06984302957695782, "132": -0.06984302957695782, "309": 0.0, "296": -0.06984302957695782, "174": -0.13968605915391563, "453": -0.06984302957695782, "497": -0.06984302957695782, "286": -0.06984302957695782, "449": 0.06984302957695782, "329": 0.06984302957695782, "114": -0.06984302957695782, "164": 0.0, "490": 0.06984302957695782, "189": -0.06984302957695782, "116": -0.06984302957695782, "16": 0.06984302957695782, "366": 0.0, "150": 0.13968605915391563, "364": 0.06984302957695782, "354": -0.06984302957695782, "55": -0.06984302957695782, "252": 0.13968605915391563, "441": -0.06984302957695782, "421": 0.06984302957695782, "506": -0.06984302957695782, "432": 0.06984302957695782, "73": 0.0, "293": -0.06984302957695782, "61": 0.06984302957695782, "282": -0.06984302957695782, "498": 0.06984302957695782, "187": 0.06984302957695782, "424": -0.06984302957695782, "270": 0.06984302957695782, "246": -0.06984302957695782, "320": 0.27937211830783126, "370": 0.06984302957695782, "501": 0.06984302957695782, "181": 0.6984302957695782, "434": -0.06984302957695782, "385": -0.06984302957695782, "397": 0.06984302957695782, "488": -0.06984302957695782, "65": -0.06984302957695782, "345": 0.06984302957695782, "284": -0.13968605915391563, "43": -0.06984302957695782, "399": -0.20952908873087345, "415": 0.06984302957695782, "138": 0.06984302957695782, "417": -0.06984302957695782, "372": 0.06984302957695782, "403": 0.20952908873087345, "51": 0.06984302957695782, "75": 0.06984302957695782, "348": 0.06984302957695782, "483": -0.06984302957695782, "103": 0.06984302957695782, "31": 0.06984302957695782, "440": -0.13968605915391563, "254": -0.06984302957695782 } }, { "id": "results/pilot_suite/memory_fabric/fabric_results.csv#0", "path": "results/pilot_suite/memory_fabric/fabric_results.csv", "chunk": 0, "text": "accepted,actor,actor_similarity,authorized_actor,case,confidence,domain,expected,expected_node,query,repaired,resonance,selected_node,success,task,tier,trust_similarity,value ,,,,,0.7075493385848546,fractal_memory_pyramid,250000,,policy_limit,False,,,True,,tier3_rsm,,250000 ,,,,,0.7079062054751789,fractal_memory_pyramid,midwest,,preferred_region,False,,,True,,tier2_hkvc,,midwest ,,,,,1.0,fractal_memory_pyramid,covariance_v3,,risk_model,False,,,True,,tier1_sparse,,covariance_v3 ,,,,,1.0,fractal_memory_pyramid,hp2d,,folding_model,False,,,True,,tier0_exact,,hp2d True,user,0.5057229799923029,user,trusted_user_memory,,provenance_hmk,True,,,,,,True,,,0.4961552769629266, False,agent,0.017941463254672014,user,untrusted_agent_memory,,provenance_hmk,False,,,,,,True,,,-0.014170222339404862, ,,,,,,resonant_execution_mesh,,edge-router-01,,,0.9999999999999999,edge-router-01,True,last-mile-route,,, ,,,,,,resonant_execution_mesh,,gpu-annealer-01,,,0.9999999999999999,gpu-annealer-01,True,sat-batch-anneal,,, ,,,,,,resonant_execution_mesh,,audit-ledger-01,,,1.0000000000000002,audit-ledger-01,True,hmk-audit,,,", "tokens": 101, "vector": { "7": 0.055641488407465724, "336": 0.055641488407465724, "242": 0.055641488407465724, "505": 0.055641488407465724, "187": 0.055641488407465724, "511": 0.055641488407465724, "498": 0.055641488407465724, "206": 0.055641488407465724, "355": -0.055641488407465724, "424": -0.055641488407465724, "315": 0.055641488407465724, "41": 0.055641488407465724, "145": 0.055641488407465724, "174": -0.055641488407465724, "270": 0.055641488407465724, "226": 0.055641488407465724, "244": 0.055641488407465724, "205": 0.055641488407465724, "268": -0.055641488407465724, "320": 0.2225659536298629, "501": 0.11128297681493145, "370": 0.055641488407465724, "252": 0.33384893044479436, "181": 0.612056372482123, "259": 0.055641488407465724, "101": -0.055641488407465724, "385": -0.11128297681493145, "434": -0.055641488407465724, "239": 0.055641488407465724, "488": -0.11128297681493145, "397": 0.055641488407465724, "89": -0.055641488407465724, "345": 0.11128297681493145, "65": -0.055641488407465724, "170": 0.055641488407465724, "4": -0.16692446522239718, "85": -0.055641488407465724, "73": 0.055641488407465724, "284": -0.11128297681493145, "324": 0.055641488407465724, "212": -0.055641488407465724, "414": 0.055641488407465724, "43": -0.055641488407465724, "468": -0.055641488407465724, "399": -0.16692446522239718, "417": -0.11128297681493145, "372": 0.11128297681493145, "403": 0.33384893044479436, "8": 0.11128297681493145, "309": 0.055641488407465724, "415": 0.055641488407465724, "138": 0.055641488407465724, "483": -0.11128297681493145, "103": 0.11128297681493145, "51": 0.055641488407465724, "75": 0.055641488407465724, "348": 0.055641488407465724, "440": -0.16692446522239718, "254": -0.11128297681493145, "256": 0.055641488407465724, "31": 0.055641488407465724 } }, { "id": "results/pilot_suite/memory_fabric/fabric_results.csv#1", "path": "results/pilot_suite/memory_fabric/fabric_results.csv", "chunk": 1, "text": "it-ledger-01,,,1.0000000000000002,audit-ledger-01,True,hmk-audit,,,", "tokens": 10, "vector": { "42": -0.25, "254": -0.5, "403": 0.5, "256": 0.25, "440": -0.5, "181": 0.25, "31": 0.25 } }, { "id": "results/pilot_suite/memory_fabric/fabric_results.json#0", "path": "results/pilot_suite/memory_fabric/fabric_results.json", "chunk": 0, "text": "[ { \"query\": \"policy_limit\", \"value\": \"250000\", \"tier\": \"tier3_rsm\", \"confidence\": 0.7075493385848546, \"repaired\": false, \"success\": true, \"domain\": \"fractal_memory_pyramid\", \"expected\": \"250000\" }, { \"query\": \"preferred_region\", \"value\": \"midwest\", \"tier\": \"tier2_hkvc\", \"confidence\": 0.7079062054751789, \"repaired\": false, \"success\": true, \"domain\": \"fractal_memory_pyramid\", \"expected\": \"midwest\" }, { \"query\": \"risk_model\", \"value\": \"covariance_v3\", \"tier\": \"tier1_sparse\", \"confidence\": 1.0, \"repaired\": false, \"success\": true, \"domain\": \"fractal_memory_pyramid\", \"expected\": \"covariance_v3\" }, { \"query\": \"folding_model\", \"value\": \"hp2d\", \"tier\": \"tier0_exact\", \"confidence\": 1.0, \"repaired\": false, \"success\": true, \"domain\": \"fractal_memory_pyramid\", \"expected\": \"hp2d\" }, { \"actor\": \"user\", \"authorized_actor\": \"user\", \"actor_similarity\": 0.5057229799923029, \"trust_similarity\": 0.4961552769629266, \"accepted\": true, \"domain\": \"provenance_hmk\", \"case\": \"trusted_user_memory\", \"expected\": true, \"success\": true }, { \"actor\": \"agent\", \"authorized_actor\": \"user\", \"actor_similarity\": 0.017941463254672014, \"trust_similarity\": -0.014170222339404862, \"accepted\": false, \"domain\": \"provenance_hmk\"", "tokens": 92, "vector": { "424": -0.22291128503014113, "370": 0.05572782125753528, "205": 0.22291128503014113, "501": 0.11145564251507056, "226": 0.22291128503014113, "259": 0.05572782125753528, "511": 0.22291128503014113, "268": -0.05572782125753528, "315": 0.22291128503014113, "252": 0.2786391062876764, "174": -0.2786391062876764, "181": 0.39009474880274697, "498": 0.3343669275452117, "320": 0.22291128503014113, "206": 0.2786391062876764, "434": -0.05572782125753528, "385": -0.11145564251507056, "239": 0.05572782125753528, "101": -0.05572782125753528, "397": 0.05572782125753528, "488": -0.11145564251507056, "89": -0.05572782125753528, "65": -0.05572782125753528, "345": 0.11145564251507056, "170": 0.05572782125753528, "336": 0.11145564251507056, "4": -0.16718346377260584, "505": 0.11145564251507056, "242": 0.11145564251507056, "85": -0.05572782125753528, "244": 0.11145564251507056, "324": 0.05572782125753528, "7": 0.11145564251507056, "284": -0.11145564251507056, "187": 0.05572782125753528, "73": 0.05572782125753528, "212": -0.05572782125753528, "414": 0.05572782125753528, "468": -0.05572782125753528 } }, { "id": "results/pilot_suite/memory_fabric/fabric_results.json#1", "path": "results/pilot_suite/memory_fabric/fabric_results.json", "chunk": 1, "text": ", \"authorized_actor\": \"user\", \"actor_similarity\": 0.017941463254672014, \"trust_similarity\": -0.014170222339404862, \"accepted\": false, \"domain\": \"provenance_hmk\", \"case\": \"untrusted_agent_memory\", \"expected\": false, \"success\": true }, { \"task\": \"last-mile-route\", \"selected_node\": \"edge-router-01\", \"expected_node\": \"edge-router-01\", \"resonance\": 0.9999999999999999, \"success\": true, \"domain\": \"resonant_execution_mesh\" }, { \"task\": \"sat-batch-anneal\", \"selected_node\": \"gpu-annealer-01\", \"expected_node\": \"gpu-annealer-01\", \"resonance\": 0.9999999999999999, \"success\": true, \"domain\": \"resonant_execution_mesh\" }, { \"task\": \"hmk-audit\", \"selected_node\": \"audit-ledger-01\", \"expected_node\": \"audit-ledger-01\", \"resonance\": 1.0000000000000002, \"success\": true, \"domain\": \"resonant_execution_mesh\" } ]", "tokens": 69, "vector": { "505": 0.07352146220938077, "4": -0.07352146220938077, "242": 0.07352146220938077, "414": 0.07352146220938077, "244": 0.07352146220938077, "468": -0.07352146220938077, "7": 0.07352146220938077, "252": 0.14704292441876154, "498": 0.2940858488375231, "284": -0.07352146220938077, "187": 0.07352146220938077, "43": -0.07352146220938077, "206": 0.07352146220938077, "174": -0.2940858488375231, "181": 0.2940858488375231, "270": 0.22056438662814232, "309": 0.07352146220938077, "415": 0.07352146220938077, "138": 0.07352146220938077, "145": 0.22056438662814232, "417": -0.14704292441876154, "372": 0.14704292441876154, "403": 0.44112877325628463, "355": -0.22056438662814232, "41": 0.22056438662814232, "8": 0.14704292441876154, "399": -0.22056438662814232, "51": 0.07352146220938077, "75": 0.07352146220938077, "348": 0.07352146220938077, "483": -0.14704292441876154, "103": 0.14704292441876154, "31": 0.07352146220938077, "440": -0.22056438662814232, "254": -0.14704292441876154, "256": 0.07352146220938077 } }, { "id": "results/pilot_suite/pilot_brief.md#0", "path": "results/pilot_suite/pilot_brief.md", "chunk": 0, "text": "# HOPS Paid Pilot Brief ## Pilot Offer HOPS is packaged as a reproducible optimization and memory-fabric proof suite for paid discovery pilots. The current local package demonstrates four optimization domains plus a memory-native compute fabric under deterministic tests. ## What The Pilot Proves - SAT: exact brute-force validation on the demonstration formula. - Portfolio: exact brute-force validation over `C(20,5)=15504` portfolios. - Routing: exact nearest-depot assignment on the pilot assignment benchmark. - HP folding: continuous energy descent against the initial conformation. - Memory fabric: tiered retrieval, provenance filtering, and semantic routing. ## What It Does Not Claim This package does not prove that arbitrary NP-hard instances are solved in constant time. It proves that the supplied continuous-relaxation and hypervector-runtime harnesses reproduce their benchmark claims and emit auditable artifacts. ## Buyer-Facing Pilot Path 1. Replace toy portfolio/routing inputs with customer anonymized data. 2. Keep exact or trusted baselines for every tractable slice. 3. Define acceptance metrics before tuning. 4. Run HOPS against historical scenarios. 5. Deliver scorecards, ", "tokens": 160, "vector": { "152": 0.3396831102433787, "38": -0.11322770341445956, "7": -0.2830692585361489, "41": 0.05661385170722978, "408": 0.05661385170722978, "506": 0.11322770341445956, "419": 0.05661385170722978, "500": -0.05661385170722978, "9": 0.05661385170722978, "190": 0.16984155512168936, "354": -0.22645540682891913, "192": -0.16984155512168936, "173": 0.16984155512168936, "449": 0.11322770341445956, "504": 0.05661385170722978, "16": 0.11322770341445956, "250": -0.05661385170722978, "224": 0.05661385170722978, "441": -0.3396831102433787, "151": 0.05661385170722978, "293": -0.16984155512168936, "188": 0.11322770341445956, "114": -0.05661385170722978, "260": -0.05661385170722978, "27": 0.05661385170722978, "56": 0.11322770341445956, "446": -0.05661385170722978, "176": 0.05661385170722978, "471": 0.05661385170722978, "267": -0.05661385170722978, "345": 0.0, "116": 0.11322770341445956, "280": -0.11322770341445956, "51": 0.05661385170722978, "12": -0.11322770341445956, "50": 0.16984155512168936, "440": -0.11322770341445956, "105": 0.05661385170722978, "115": -0.05661385170722978, "14": 0.11322770341445956, "95": 0.05661385170722978, "245": 0.05661385170722978, "232": -0.05661385170722978, "106": 0.05661385170722978, "252": 0.16984155512168936, "330": 0.05661385170722978, "186": 0.11322770341445956, "206": 0.11322770341445956, "270": 0.11322770341445956, "147": -0.05661385170722978, "87": 0.05661385170722978, "503": -0.11322770341445956, "167": 0.11322770341445956, "428": -0.05661385170722978, "89": 0.05661385170722978, "366": 0.0, "150": -0.11322770341445956, "55": -0.05661385170722978, "42": -0.11322770341445956, "477": 0.05661385170722978, "286": -0.05661385170722978, "82": 0.05661385170722978, "398": 0.11322770341445956, "135": -0.05661385170722978, "266": -0.05661385170722978, "237": 0.05661385170722978, "32": -0.05661385170722978, "458": -0.05661385170722978, "344": -0.05661385170722978, "57": -0.05661385170722978, "189": -0.05661385170722978, "217": 0.05661385170722978, "149": 0.05661385170722978, "326": 0.05661385170722978, "253": 0.05661385170722978, "166": 0.05661385170722978, "406": -0.05661385170722978, "257": -0.05661385170722978, "15": 0.05661385170722978, "365": 0.0, "435": 0.05661385170722978, "416": -0.05661385170722978, "378": 0.05661385170722978, "271": -0.05661385170722978, "262": 0.05661385170722978, "182": -0.05661385170722978, "58": -0.05661385170722978, "364": 0.05661385170722978, "369": -0.05661385170722978, "169": 0.05661385170722978, "436": 0.05661385170722978, "102": -0.05661385170722978, "47": -0.05661385170722978, "259": -0.05661385170722978, "492": 0.05661385170722978, "23": -0.05661385170722978, "455": 0.05661385170722978, "172": -0.05661385170722978, "501": -0.05661385170722978, "465": -0.05661385170722978, "390": 0.05661385170722978 } }, { "id": "results/pilot_suite/pilot_brief.md#1", "path": "results/pilot_suite/pilot_brief.md", "chunk": 1, "text": "ct or trusted baselines for every tractable slice. 3. Define acceptance metrics before tuning. 4. Run HOPS against historical scenarios. 5. Deliver scorecards, runtime, failures, and next-step integration estimate. ## Build Status - Tests passed: `True` - HOPS proof passed: `4/4` - Memory fabric proof passed: `9/9` ## Artifacts - `site/index.html` - `hops/hops_results.csv` - `hops/hops_results.json` - `memory_fabric/fabric_results.csv` - `memory_fabric/fabric_results.json` - `pilot_manifest.json`", "tokens": 57, "vector": { "248": 0.10259783520851541, "369": -0.10259783520851541, "169": 0.10259783520851541, "436": 0.10259783520851541, "16": 0.10259783520851541, "102": -0.10259783520851541, "47": -0.10259783520851541, "259": -0.10259783520851541, "492": 0.10259783520851541, "449": 0.30779350562554625, "23": -0.10259783520851541, "365": -0.10259783520851541, "455": 0.10259783520851541, "172": -0.10259783520851541, "152": 0.41039134083406165, "167": 0.10259783520851541, "501": -0.10259783520851541, "465": -0.10259783520851541, "506": -0.10259783520851541, "390": 0.10259783520851541, "217": 0.10259783520851541, "61": 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\"stdout\": \"ran 48 trials across 24 aggregate conditions\\nruntime: 0.06s\\nwrote /var/folders/l9/qx26_pcd2nsgd8hv6k4_gl300000gn/T/tmp7qpfl7bf/bench/raw_trials.csv\\nwrote /var/folders/l9/qx26_pcd2nsgd8hv6k4_gl300000gn/T/tmp7qpfl7bf/bench/raw_trials.json\\nwrote /var/folders/l9/qx26_pcd2nsgd8hv6k4_gl300000gn/T/tmp7qpfl7bf/bench/aggregate.csv\\nwrote /var/folders/l9/qx26_pcd2nsgd8hv6k4_gl300000gn/T/tmp7qpfl7bf/bench/aggregate.json\\nwrote /var/folders/l9/qx26_pcd2nsgd8hv6k4_gl300000gn/T/tmp7qpfl7bf/bench/report.md\\n\", \"stderr\": \"..........\\n----------------------------------------------------------------------\\nRan 10 tests in 1.846s\\n\\nOK\\n\", \"success\": true }, \"hops\": { \"passed\": 4, \"total\": 4 }, \"memory_fabric\": { \"passed\": 9, \"total\": 9 }, \"scorecards\": [ { \"name\": \"Test Suite\", \"success\": true, \"summary\": \"/Library/Developer/CommandLineTools/usr/bin/python3 -m unittest discover -s tests completed in 1.96s.\" }, { \"name\": \"HOPS Optimization 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"205": -0.11094003924504582, "339": 0.11094003924504582, "258": 0.11094003924504582, "132": -0.05547001962252291, "33": -0.05547001962252291, "382": 0.05547001962252291, "228": 0.05547001962252291, "74": 0.05547001962252291, "344": -0.11094003924504582, "15": -0.05547001962252291, "508": -0.05547001962252291, "174": -0.16641005886756874, "152": 0.11094003924504582, "296": -0.11094003924504582, "273": -0.11094003924504582, "437": -0.05547001962252291, "390": 0.05547001962252291, "386": 0.11094003924504582, "159": 0.05547001962252291, "504": 0.05547001962252291, "374": -0.11094003924504582, "333": 0.05547001962252291, "198": 0.05547001962252291, "190": 0.05547001962252291, "449": 0.05547001962252291 } }, { "id": "results/pilot_suite/pilot_manifest.json#1", "path": "results/pilot_suite/pilot_manifest.json", "chunk": 1, "text": "Developer/CommandLineTools/usr/bin/python3 -m unittest discover -s tests completed in 1.96s.\" }, { \"name\": \"HOPS Optimization Proof\", \"success\": true, \"summary\": \"SAT, portfolio, routing, and HP folding proof cases generated with auditable outputs.\" }, { \"name\": \"Memory Fabric Proof\", \"success\": true, \"summary\": \"Tiered retrieval, provenance HMK filtering, and resonant routing passed.\" } ] }", "tokens": 46, "vector": { "136": -0.125, "274": 0.125, "126": -0.125, "337": 0.125, "193": 0.125, "223": -0.125, "91": 0.125, "345": 0.125, "333": 0.125, "344": -0.125, "198": 0.125, "386": 0.25, "152": 0.125, "190": 0.125, "449": 0.375, "174": -0.25, "181": 0.25, "374": -0.25, "51": 0.25, "14": 0.125, "252": 0.25, "354": -0.25, "147": -0.125, "87": 0.125, "309": -0.125, "480": 0.125, "262": 0.125, "257": -0.125, "123": 0.125, "192": -0.125, "173": 0.125, "366": 0.0, "150": 0.125, "31": 0.125, "487": 0.125, "296": -0.125 } }, { "id": "results/pilot_suite/site/index.html#0", "path": "results/pilot_suite/site/index.html", "chunk": 0, "text": " HOPS Pilot Suite ", "tokens": 165, "vector": { "440": 0.047836487323493986, "229": 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HOPS Pilot Suite

A reproducible, test-driven pilot package for continuous optimization, hypervector memory, provenance filtering, and resonant execution routing.

PASS
PASS

Test Suite

/Library/Developer/CommandLineTools/usr/bin/python3 -m unittest discover -s tests completed in 1.96s.

PASS

HOPS Optimization Proof

SAT, portfolio, routing, and HP folding proof cases generated with auditable outputs.

PASS

Memory Fabric Proof

Tiered retrieval, provenance HMK filtering, and resonant routing passed.

Commercial posture: ready for paid discovery pilots on bounded customer datasets with agreed baselines and acceptance metrics. This dashboard is an evidence package, not a claim of universal complexi", "tokens": 156, "vector": { "237": 0.05198752449100364, "308": 0.0, "40": -0.05198752449100364, "456": 0.05198752449100364, "176": 0.05198752449100364, "171": 0.05198752449100364, "79": 0.05198752449100364, "310": -0.05198752449100364, "53": -0.05198752449100364, "331": -0.05198752449100364, "429": 0.05198752449100364, "393": -0.05198752449100364, "316": 0.05198752449100364, "312": -0.05198752449100364, "57": 0.05198752449100364, "116": -0.05198752449100364, "367": 0.05198752449100364, "203": -0.05198752449100364, "159": 0.05198752449100364, "291": 0.05198752449100364, "286": -0.1559625734730109, "307": 0.10397504898200728, "152": 0.10397504898200728, "7": -0.10397504898200728, "504": 0.10397504898200728, "264": 0.3639126714370255, "9": 0.05198752449100364, "188": 0.10397504898200728, "16": 0.10397504898200728, "503": -0.05198752449100364, "190": 0.10397504898200728, "189": -0.05198752449100364, "192": -0.10397504898200728, "150": 0.10397504898200728, "51": 0.1559625734730109, "354": -0.20795009796401456, "487": 0.10397504898200728, "164": 0.05198752449100364, "252": 0.1559625734730109, "461": 0.10397504898200728, "392": -0.05198752449100364, "471": -0.20795009796401456, "13": -0.05198752449100364, "63": -0.1559625734730109, "15": -0.05198752449100364, "388": 0.3119251469460218, "387": -0.1559625734730109, "55": -0.3119251469460218, "249": -0.3119251469460218, "183": -0.05198752449100364, "136": -0.05198752449100364, "274": 0.05198752449100364, "126": -0.05198752449100364, "337": 0.05198752449100364, "193": 0.05198752449100364, "223": -0.05198752449100364, "91": 0.05198752449100364, "345": 0.05198752449100364, "333": 0.05198752449100364, "344": -0.05198752449100364, "198": 0.05198752449100364, "449": 0.20795009796401456, "14": 0.05198752449100364, "147": -0.05198752449100364, "87": 0.05198752449100364, "309": -0.05198752449100364, "480": 0.05198752449100364, "262": 0.10397504898200728, "257": -0.05198752449100364, "123": 0.05198752449100364, "173": 0.05198752449100364, "366": 0.0, "31": 0.05198752449100364, "296": 0.0, "493": -0.10397504898200728, "208": 0.05198752449100364, "405": -0.05198752449100364, "434": -0.05198752449100364, "38": -0.05198752449100364, "250": -0.05198752449100364, "224": 0.05198752449100364, "440": -0.05198752449100364, "115": -0.05198752449100364, "182": -0.05198752449100364, "113": 0.05198752449100364, "158": -0.05198752449100364, "436": 0.05198752449100364, "23": -0.05198752449100364, "358": 0.05198752449100364, "506": -0.05198752449100364, "234": -0.05198752449100364, "238": 0.05198752449100364, "293": -0.05198752449100364, "477": 0.05198752449100364, "425": 0.05198752449100364, "452": 0.05198752449100364 } }, { "id": "results/pilot_suite/site/index.html#2", "path": "results/pilot_suite/site/index.html", "chunk": 2, "text": "overy pilots on bounded customer datasets with agreed baselines and acceptance metrics. This dashboard is an evidence package, not a claim of universal complexity collapse.

Audit files: pilot_manifest.json, pilot_brief.md.

", "tokens": 40, "vector": { "368": -0.1386750490563073, "224": 0.1386750490563073, "440": -0.2773500981126146, "115": -0.1386750490563073, "182": -0.1386750490563073, "113": 0.1386750490563073, "262": 0.1386750490563073, "158": -0.1386750490563073, "436": 0.1386750490563073, "354": -0.1386750490563073, "449": 0.1386750490563073, "23": -0.1386750490563073, "286": -0.1386750490563073, "358": 0.1386750490563073, "506": -0.1386750490563073, "234": -0.1386750490563073, "238": 0.1386750490563073, "188": 0.1386750490563073, "293": -0.1386750490563073, "477": 0.1386750490563073, "296": 0.1386750490563073, "425": 0.1386750490563073, "415": 0.1386750490563073, "147": 0.1386750490563073, "63": -0.1386750490563073, "317": 0.1386750490563073, "254": 0.2773500981126146, "336": 0.2773500981126146, "258": 0.2773500981126146, "174": 0.2773500981126146, "33": -0.2773500981126146, "13": -0.1386750490563073, "291": 0.1386750490563073, "229": -0.1386750490563073 } }, { "id": "results/sat/sat_report.md#0", "path": "results/sat/sat_report.md", "chunk": 0, "text": "# Holographic SAT-Annealer Report ## Summary - Cases: `21` - Satisfying assignments found: `21` - Success rate: `1.00` This is a continuous phase relaxation for SAT, not a proof that NP-hardness has been removed. It searches a smooth energy landscape, collapses phases to Boolean assignments, and validates small cases against exact brute force. ## Example Case The bundled example is: `(x0 OR x1) AND (NOT x0 OR x2) AND (NOT x1 OR NOT x2) AND (x0 OR x2)` Expected assignment for the demonstration: `(True, False, True)`. ## Outputs - `sat_results.csv` - `sat_results.json` - `sat_report.md`", "tokens": 86, "vector": { "431": 0.0827605888602368, "51": 0.1655211777204736, "103": 0.0827605888602368, "132": 0.1655211777204736, "374": -0.0827605888602368, "309": -0.1655211777204736, "367": 0.1655211777204736, "68": -0.0827605888602368, "495": 0.1655211777204736, "188": -0.0827605888602368, "174": -0.0827605888602368, "453": -0.0827605888602368, "497": -0.0827605888602368, "286": -0.0827605888602368, "506": -0.0827605888602368, "503": -0.0827605888602368, "258": 0.1655211777204736, "57": -0.0827605888602368, "16": 0.1655211777204736, "293": -0.24828176658071038, "449": 0.0827605888602368, "398": 0.0827605888602368, "266": -0.0827605888602368, "145": -0.0827605888602368, "389": -0.0827605888602368, "481": 0.0827605888602368, "42": -0.0827605888602368, "455": -0.0827605888602368, "448": 0.0827605888602368, "56": 0.0827605888602368, "428": 0.0827605888602368, "40": -0.0827605888602368, "390": 0.0827605888602368, "102": -0.0827605888602368, "191": 0.0827605888602368, "354": -0.3310423554409472, "206": 0.24828176658071038, "385": -0.0827605888602368, "167": 0.0827605888602368, "12": -0.0827605888602368, "50": 0.0827605888602368, "80": 0.1655211777204736, "187": 0.0827605888602368, "441": -0.1655211777204736, "332": -0.0827605888602368, "183": 0.24828176658071038, "369": -0.3310423554409472, "492": 0.1655211777204736, "105": 0.0827605888602368, "181": 0.1655211777204736, "252": 0.0827605888602368, "123": 0.0827605888602368, "250": 0.1655211777204736, "339": 0.0827605888602368, "440": -0.0827605888602368, "33": -0.0827605888602368 } }, { "id": "results/sat/sat_results.csv#0", "path": "results/sat/sat_results.csv", "chunk": 0, "text": "assignment,assignment_bits,brute_force_contains_result,brute_force_solution_count,case,clauses,elapsed_seconds,energy,expected_assignment,formula,iterations,matches_expected,matches_planted,phases,planted_assignment,probabilities,restart,satisfied_clauses,success,total_clauses,variables \"(True, False, True)\",101,,1,example,4,,1.072222241447161,\"(True, False, True)\",(x0 OR x1) AND (NOT x0 OR x2) AND (NOT x1 OR NOT x2) AND (x0 OR x2),2,True,,\"[0.86508252392951, 2.4772653339741146, 1.2151083528683682]\",,\"[0.2753643197315887, 0.7885380465043507, 0.38678100150250366]\",0,4,True,4,3 \"(False, True, False, True, True, False, False, False, False, False, False, False)\",010110000000,True,10,planted_0,48,0.00950904099999994,0.8646130612158263,,,8,,False,,\"(True, True, True, True, True, True, True, False, False, False, False, True)\",,0,48,True,48,12 \"(True, True, False, True, False, True, False, False, True, True, False, False)\",110101001100,True,9,planted_1,48,0.02595345800000004,1.538436504450367,,,19,,True,,\"(True, True, False, True, False, True, False, False, True, True, False, False)\",,0,48,True,48,12 \"(False, False, True, True, False, True, False, False, False, True, True, False)\",00110100", "tokens": 141, "vector": { "206": 0.01861129564531997, "36": 0.0, "447": 0.01861129564531997, "261": -0.01861129564531997, "187": 0.01861129564531997, "130": -0.01861129564531997, "107": -0.01861129564531997, "56": 0.01861129564531997, "68": 0.01861129564531997, "115": -0.01861129564531997, "298": 0.01861129564531997, "308": 0.01861129564531997, "303": 0.01861129564531997, "390": 0.01861129564531997, "362": 0.01861129564531997, "508": -0.01861129564531997, "58": 0.01861129564531997, "167": 0.01861129564531997, "174": -0.01861129564531997, "141": 0.01861129564531997, "122": 0.01861129564531997, "181": 0.7258405301674788, "252": 0.6513953475861989, "22": -0.01861129564531997, "80": 0.01861129564531997, "422": -0.01861129564531997, "183": 0.055833886935959906, "369": -0.07444518258127988, "492": 0.055833886935959906, "354": -0.055833886935959906, "293": -0.055833886935959906, "132": 0.055833886935959906, "190": 0.01861129564531997, "299": 0.01861129564531997, "315": 0.01861129564531997, "405": 0.03722259129063994, "366": -0.01861129564531997, "74": 0.01861129564531997, "304": 0.01861129564531997, "408": -0.11166777387191981, "490": -0.01861129564531997, "86": -0.01861129564531997, "363": -0.03722259129063994, "45": 0.01861129564531997, "259": -0.01861129564531997, "311": -0.01861129564531997, "148": 0.01861129564531997, "462": 0.01861129564531997 } }, { "id": "results/sat/sat_results.csv#1", "path": "results/sat/sat_results.csv", "chunk": 1, "text": "alse, True, False, False, True, True, False, False)\",,0,48,True,48,12 \"(False, False, True, True, False, True, False, False, False, True, True, False)\",001101000110,True,17,planted_2,48,0.036414833000000035,0.5951415883950607,,,25,,False,,\"(False, True, True, True, False, False, True, False, False, False, True, False)\",,0,48,True,48,12 \"(False, True, False, True, True, False, True, False, False, True, False, True)\",010110100101,True,10,planted_3,48,0.6949388750000001,4.030695828038574,,,2,,False,,\"(True, False, False, False, True, False, False, False, True, False, True, True)\",,1,48,True,48,12 \"(False, False, True, False, True, False, True, True, True, False, False, True)\",001010111001,True,8,planted_4,48,1.821234541,0.7246603766667804,,,56,,False,,\"(False, False, True, True, True, True, False, True, False, False, False, False)\",,2,48,True,48,12 \"(False, True, False, True, True, False, False, True, True, False, True, False)\",010110011010,True,13,planted_5,48,0.0046390410000003435,3.4682941951152175,,,3,,True,,\"(False, True, False, True, True, False, False, True, True, False, True, False)\",,0,48,True,48,12 \"(True, True, False, True, True, True, False, True, True, False, False, False", "tokens": 169, "vector": { "39": 0.010888234270284593, "181": 0.696846993298214, "252": 0.696846993298214, "408": -0.1524352797839843, "363": -0.05444117135142297, "397": -0.010888234270284593, "325": -0.010888234270284593, "17": 0.010888234270284593, "505": -0.010888234270284593, "459": 0.010888234270284593, "439": -0.010888234270284593, "487": 0.010888234270284593, "74": 0.010888234270284593, "347": -0.010888234270284593, "89": -0.010888234270284593, "12": 0.010888234270284593, "139": -0.010888234270284593, "220": 0.010888234270284593, "244": 0.010888234270284593, "206": -0.010888234270284593, "118": 0.010888234270284593, "470": 0.010888234270284593, "319": 0.010888234270284593, "334": 0.010888234270284593, "202": -0.010888234270284593, "364": 0.010888234270284593 } }, { "id": "results/sat/sat_results.csv#2", "path": "results/sat/sat_results.csv", "chunk": 2, "text": "False, True, True, False, False, True, True, False, True, False)\",,0,48,True,48,12 \"(True, True, False, True, True, True, False, True, True, False, False, False)\",110111011000,True,4,planted_6,48,0.012360666000000187,0.8492372258501558,,,8,,False,,\"(True, True, False, True, True, True, False, False, True, False, False, False)\",,0,48,True,48,12 \"(True, True, False, True, True, True, False, True, True, False, True, True)\",110111011011,True,1,planted_7,48,10.145567708,0.667802729875055,,,7,,True,,\"(True, True, False, True, True, True, False, True, True, False, True, True)\",,13,48,True,48,12 \"(False, False, False, True, False, True, True, False, False, True, False, False)\",000101100100,True,16,planted_8,48,0.006092041000000492,2.6439809321527297,,,4,,False,,\"(False, True, False, True, False, True, False, False, False, False, False, False)\",,0,48,True,48,12 \"(True, True, True, False, False, True, True, False, False, False, False, False)\",111001100000,True,1,planted_9,48,0.010295125000000738,0.5433744451424438,,,7,,True,,\"(True, True, True, False, False, True, True, False, False, False, False, False)\",,0,48,True,48,12 \"(False, False, True, False, True, False, True, True, False, False, Tr", "tokens": 168, "vector": { "252": 0.658315973375479, "181": 0.7338604293365996, "408": -0.15108891192224108, "363": -0.053960325686514675, "331": 0.010792065137302935, "327": 0.010792065137302935, "43": -0.010792065137302935, "40": 0.010792065137302935, "63": 0.010792065137302935, "299": -0.010792065137302935, "74": 0.010792065137302935, "214": -0.010792065137302935, "384": -0.010792065137302935, "319": 0.010792065137302935, "225": -0.010792065137302935, "177": 0.010792065137302935, "235": -0.010792065137302935, "362": 0.010792065137302935, "505": -0.010792065137302935, "502": -0.010792065137302935, "380": 0.010792065137302935, "234": 0.010792065137302935, "69": 0.010792065137302935, "376": 0.010792065137302935 } }, { "id": "results/sat/sat_results.csv#3", "path": "results/sat/sat_results.csv", "chunk": 3, "text": "rue, True, False, False, True, True, False, False, False, False, False)\",,0,48,True,48,12 \"(False, False, True, False, True, False, True, True, False, False, True, False)\",001010110010,True,1,planted_10,48,2.349363875,0.5810626727929704,,,34,,True,,\"(False, False, True, False, True, False, True, True, False, False, True, False)\",,3,48,True,48,12 \"(False, False, True, True, True, True, False, False, True, False, False, False)\",001111001000,True,16,planted_11,48,0.0072904170000001045,3.9015331654806844,,,2,,False,,\"(False, True, True, False, True, False, True, True, False, False, True, False)\",,0,48,True,48,12 \"(False, False, False, True, False, False, False, True, True, True, False, True)\",000100011101,True,16,planted_12,48,0.7961484580000011,0.7567313198174641,,,15,,False,,\"(True, True, False, True, False, False, False, False, True, False, False, True)\",,1,48,True,48,12 \"(False, False, True, False, False, True, False, True, True, False, True, True)\",001001011011,True,12,planted_13,48,0.01478670899999912,0.5029759971855111,,,7,,False,,\"(False, False, False, False, True, True, False, True, True, False, True, True)\",,0,48,True,48,12 \"(False, True, False, True, True, False, False, True", "tokens": 168, "vector": { "27": 0.010927819720786573, "181": 0.6556691832471944, "252": 0.7321639212927004, "408": -0.1639172958117986, "363": -0.06556691832471943, "115": -0.010927819720786573, "141": -0.010927819720786573, "60": -0.010927819720786573, "70": -0.010927819720786573, "390": -0.010927819720786573, "413": 0.010927819720786573, "177": 0.021855639441573146, "465": 0.010927819720786573, "356": -0.010927819720786573, "486": 0.021855639441573146, "330": 0.010927819720786573, "31": -0.010927819720786573, "319": 0.010927819720786573, "297": -0.010927819720786573, "272": 0.010927819720786573, "88": -0.010927819720786573, "212": 0.010927819720786573 } }, { "id": "results/sat/sat_results.csv#4", "path": "results/sat/sat_results.csv", "chunk": 4, "text": "7,,False,,\"(False, False, False, False, True, True, False, True, True, False, True, True)\",,0,48,True,48,12 \"(False, True, False, True, True, False, False, True, False, True, False, True)\",010110010101,True,13,planted_14,48,0.005656749999999988,1.6185777078103198,,,5,,False,,\"(False, False, False, True, False, False, True, True, False, True, True, True)\",,0,48,True,48,12 \"(False, True, True, False, True, False, False, False, False, False, True, False)\",011010000010,True,16,planted_15,48,0.01247012500000011,2.7189586793729394,,,7,,False,,\"(False, False, False, True, False, False, True, False, True, False, True, True)\",,0,48,True,48,12 \"(True, False, True, True, True, False, True, False, False, False, False, False)\",101110100000,True,9,planted_16,48,0.014116957999998903,0.6652149051573676,,,8,,False,,\"(True, False, False, False, True, False, True, False, False, False, True, False)\",,0,48,True,48,12 \"(True, False, True, True, True, False, True, False, True, True, False, False)\",101110101100,True,6,planted_17,48,0.018447208000001325,2.157989881475664,,,3,,False,,\"(True, False, False, True, False, False, True, False, True, False, False, True)\",,0,48,True,48,12 \"(True, False, True, True,", "tokens": 163, "vector": { "252": 0.7289814345553078, "181": 0.6627103950502798, "408": -0.15463242551173195, "363": -0.05522586625418999, "42": 0.011045173250837998, "319": 0.011045173250837998, "415": 0.011045173250837998, "266": 0.011045173250837998, "19": 0.011045173250837998, "460": 0.011045173250837998, "177": 0.011045173250837998, "488": -0.022090346501675996, "54": -0.011045173250837998, "471": 0.011045173250837998, "86": 0.011045173250837998, "152": -0.011045173250837998, "384": 0.011045173250837998, "246": -0.011045173250837998, "492": 0.011045173250837998, "73": 0.011045173250837998, "428": -0.011045173250837998 } }, { "id": "results/sat/sat_results.csv#5", "path": "results/sat/sat_results.csv", "chunk": 5, "text": "1325,2.157989881475664,,,3,,False,,\"(True, False, False, True, False, False, True, False, True, False, False, True)\",,0,48,True,48,12 \"(True, False, True, True, False, False, True, True, True, False, False, True)\",101100111001,True,14,planted_18,48,0.014928208000000609,0.6519051168222463,,,11,,False,,\"(True, False, True, True, False, True, False, True, True, False, True, True)\",,0,48,True,48,12 \"(False, False, False, True, False, True, False, True, True, False, True, True)\",000101011011,True,28,planted_19,48,0.8147108749999994,4.800672115520305,,,1,,False,,\"(False, False, False, True, False, False, False, True, False, True, True, True)\",,1,48,True,48,12", "tokens": 92, "vector": { "89": 0.02066362704109797, "428": -0.02066362704109797, "252": 0.640572438274037, "181": 0.7438905734795269, "408": -0.16530901632878375, "363": -0.06199088112329391, "152": 0.02066362704109797, "389": 0.02066362704109797, "82": -0.02066362704109797, "162": -0.02066362704109797, "228": -0.02066362704109797, "79": 0.02066362704109797, "90": -0.02066362704109797, "345": -0.02066362704109797, "483": -0.02066362704109797, "416": -0.02066362704109797 } }, { "id": "results/sat/sat_results.json#0", "path": "results/sat/sat_results.json", "chunk": 0, "text": "[ { \"case\": \"example\", \"variables\": 3, \"clauses\": 4, \"formula\": \"(x0 OR x1) AND (NOT x0 OR x2) AND (NOT x1 OR NOT x2) AND (x0 OR x2)\", \"assignment\": \"(True, False, True)\", \"assignment_bits\": \"101\", \"expected_assignment\": \"(True, False, True)\", \"matches_expected\": true, \"brute_force_solution_count\": 1, \"success\": true, \"satisfied_clauses\": 4, \"total_clauses\": 4, \"energy\": 1.072222241447161, \"iterations\": 2, \"restart\": 0, \"probabilities\": [ 0.2753643197315887, 0.7885380465043507, 0.38678100150250366 ], \"phases\": [ 0.86508252392951, 2.4772653339741146, 1.2151083528683682 ] }, { \"case\": \"planted_0\", \"variables\": 12, \"clauses\": 48, \"planted_assignment\": \"(True, True, True, True, True, True, True, False, False, False, False, True)\", \"assignment\": \"(False, True, False, True, True, False, False, False, False, False, False, False)\", \"assignment_bits\": \"010110000000\", \"matches_planted\": false, \"success\": true, \"satisfied_clauses\": 48, \"total_clauses\": 48, \"energy\": 0.8646130612158263, \"iterations\": 8, \"restart\": 0, \"elapsed_seconds\": 0.00950904099999994, \"brute_force_solution_count\": 10, \"brute_force_contains_result\": true }, { \"case\": \"planted_1\", \"variables\": 12, \"clauses\": 48, \"planted_as", "tokens": 111, "vector": { "187": 0.10693845086992786, "80": 0.035646150289975954, "122": 0.10693845086992786, "130": -0.10693845086992786, "115": -0.035646150289975954, "183": 0.10693845086992786, "369": -0.14258460115990382, "492": 0.10693845086992786, "354": -0.10693845086992786, "293": -0.10693845086992786, "132": 0.10693845086992786, "206": 0.07129230057995191, 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0.07401205672371726, "181": 0.6414378249388829, "187": 0.07401205672371726, "86": 0.02467068557457242, "122": 0.04934137114914484, "363": -0.04934137114914484, "130": -0.04934137114914484, "408": -0.14802411344743452, "362": 0.04934137114914484, "252": 0.7154498816626002, "206": 0.04934137114914484, "36": 0.04934137114914484, "471": 0.02467068557457242, "303": 0.04934137114914484, "174": -0.04934137114914484, "167": 0.04934137114914484, "141": 0.04934137114914484, "56": 0.04934137114914484, "384": 0.02467068557457242, "298": 0.04934137114914484, "58": 0.04934137114914484, "107": -0.04934137114914484, "152": -0.02467068557457242, "261": -0.04934137114914484, "492": 0.02467068557457242, "246": -0.02467068557457242, "428": -0.02467068557457242, "73": 0.02467068557457242, "82": -0.02467068557457242, "281": 0.02467068557457242 } }, { "id": "results/sat/sat_results.json#10", "path": "results/sat/sat_results.json", "chunk": 10, "text": "\": 3, \"restart\": 0, \"elapsed_seconds\": 0.018447208000001325, \"brute_force_solution_count\": 6, \"brute_force_contains_result\": true }, { \"case\": \"planted_18\", \"variables\": 12, \"clauses\": 48, \"planted_assignment\": \"(True, False, True, True, False, True, False, True, True, False, True, True)\", \"assignment\": \"(True, False, True, True, False, False, True, True, True, False, False, True)\", \"assignment_bits\": \"101100111001\", \"matches_planted\": false, \"success\": true, \"satisfied_clauses\": 48, \"total_clauses\": 48, \"energy\": 0.6519051168222463, \"iterations\": 11, \"restart\": 0, \"elapsed_seconds\": 0.014928208000000609, \"brute_force_solution_count\": 14, \"brute_force_contains_result\": true }, { \"case\": \"planted_19\", \"variables\": 12, \"clauses\": 48, \"planted_assignment\": \"(False, False, False, True, False, False, False, True, False, True, True, True)\", \"assignment\": \"(False, False, False, True, False, True, False, True, True, False, True, True)\", \"assignment_bits\": \"000101011011\", \"matches_planted\": false, \"success\": true, \"satisfied_clauses\": 48, \"total_clauses\": 48, \"energy\": 4.800672115520305, \"iterations\": 1, \"restart\": 1, \"elapsed_seconds\": 0.8147108749999994, \"brute_force_solution_count\": 28, ", "tokens": 109, "vector": { "58": 0.07597980893803578, "107": -0.07597980893803578, "73": 0.02532660297934526, "261": -0.07597980893803578, "447": 0.05065320595869052, "181": 0.7597980893803579, "187": 0.05065320595869052, "82": -0.02532660297934526, "122": 0.05065320595869052, "363": -0.05065320595869052, "130": -0.05065320595869052, "408": -0.15195961787607157, "362": 0.05065320595869052, "252": 0.5825118685249411, "206": 0.05065320595869052, "36": 0.05065320595869052, "152": 0.02532660297934526, "303": 0.05065320595869052, "174": -0.05065320595869052, "167": 0.05065320595869052, "141": 0.05065320595869052, "56": 0.05065320595869052, "228": -0.02532660297934526, "298": 0.05065320595869052, "79": 0.02532660297934526, "162": -0.02532660297934526, "389": 0.02532660297934526, "483": -0.02532660297934526, "90": -0.02532660297934526, "416": -0.02532660297934526, "345": -0.02532660297934526 } }, { "id": "results/sat/sat_results.json#11", "path": "results/sat/sat_results.json", "chunk": 11, "text": ": 48, \"total_clauses\": 48, \"energy\": 4.800672115520305, \"iterations\": 1, \"restart\": 1, \"elapsed_seconds\": 0.8147108749999994, \"brute_force_solution_count\": 28, \"brute_force_contains_result\": true } ]", "tokens": 13, "vector": { "408": -0.5163977794943222, "141": 0.2581988897471611, "56": 0.2581988897471611, "252": -0.2581988897471611, "298": 0.2581988897471611, "58": 0.2581988897471611, "107": -0.2581988897471611, "416": -0.2581988897471611, "261": -0.2581988897471611, "345": -0.2581988897471611, "447": 0.2581988897471611, "181": 0.2581988897471611 } }, { "id": "results/sat_example/sat_report.md#0", "path": "results/sat_example/sat_report.md", "chunk": 0, "text": "# Holographic SAT-Annealer Report ## Summary - Cases: `1` - Satisfying assignments found: `1` - Success rate: `1.00` This is a continuous phase relaxation for SAT, not a proof that NP-hardness has been removed. It searches a smooth energy landscape, collapses phases to Boolean assignments, and validates small cases against exact brute force. ## Example Case The bundled example is: `(x0 OR x1) AND (NOT x0 OR x2) AND (NOT x1 OR NOT x2) AND (x0 OR x2)` Expected assignment for the demonstration: `(True, False, True)`. ## Outputs - `sat_results.csv` - `sat_results.json` - `sat_report.md`", "tokens": 84, "vector": { "431": 0.08391813582966891, "51": 0.16783627165933782, "103": 0.08391813582966891, "132": 0.16783627165933782, "374": -0.08391813582966891, "309": -0.16783627165933782, "68": -0.08391813582966891, "495": 0.16783627165933782, "188": -0.08391813582966891, "174": -0.08391813582966891, "453": -0.08391813582966891, "497": -0.08391813582966891, "286": -0.08391813582966891, "506": -0.08391813582966891, "503": -0.08391813582966891, "258": 0.16783627165933782, "57": -0.08391813582966891, "16": 0.16783627165933782, "293": -0.25175440748900674, "449": 0.08391813582966891, "398": 0.08391813582966891, "266": -0.08391813582966891, "145": -0.08391813582966891, "389": -0.08391813582966891, "481": 0.08391813582966891, "42": -0.08391813582966891, "455": -0.08391813582966891, "448": 0.08391813582966891, "56": 0.08391813582966891, "428": 0.08391813582966891, "40": -0.08391813582966891, "390": 0.08391813582966891, "102": -0.08391813582966891, "191": 0.08391813582966891, "354": -0.33567254331867563, "206": 0.25175440748900674, "385": -0.08391813582966891, "167": 0.08391813582966891, "12": -0.08391813582966891, "50": 0.08391813582966891, "80": 0.16783627165933782, "187": 0.08391813582966891, "441": -0.16783627165933782, "332": -0.08391813582966891, "183": 0.25175440748900674, "369": -0.33567254331867563, "492": 0.16783627165933782, "105": 0.08391813582966891, "181": 0.16783627165933782, "252": 0.08391813582966891, "123": 0.08391813582966891, "250": 0.16783627165933782, "339": 0.08391813582966891, "440": -0.08391813582966891, "33": -0.08391813582966891 } }, { "id": "results/sat_example/sat_results.csv#0", "path": "results/sat_example/sat_results.csv", "chunk": 0, "text": "assignment,assignment_bits,brute_force_solution_count,case,clauses,energy,expected_assignment,formula,iterations,matches_expected,phases,probabilities,restart,satisfied_clauses,success,total_clauses,variables \"(True, False, True)\",101,1,example,4,0.0,\"(True, False, True)\",(x0 OR x1) AND (NOT x0 OR x2) AND (NOT x1 OR NOT x2) AND (x0 OR x2),2,True,\"[0.0, 3.141592653589793, 0.0]\",\"[0.0, 1.0, 0.0]\",0,4,True,4,3", "tokens": 46, "vector": { "206": 0.09284766908852593, "36": 0.09284766908852593, "261": -0.09284766908852593, "187": 0.09284766908852593, "130": -0.09284766908852593, "56": 0.09284766908852593, "68": 0.09284766908852593, "115": -0.09284766908852593, "298": 0.09284766908852593, "308": 0.09284766908852593, "390": 0.09284766908852593, "508": -0.09284766908852593, "58": 0.09284766908852593, "167": 0.09284766908852593, "174": -0.09284766908852593, "141": 0.09284766908852593, "122": 0.09284766908852593, "181": 0.5570860145311556, "252": 0.18569533817705186, "22": -0.09284766908852593, "80": 0.09284766908852593, "183": 0.2785430072655778, "369": -0.3713906763541037, "492": 0.18569533817705186, "354": -0.2785430072655778, "293": -0.2785430072655778, "132": 0.2785430072655778, "505": 0.09284766908852593 } }, { "id": "results/sat_example/sat_results.json#0", "path": "results/sat_example/sat_results.json", "chunk": 0, "text": "[ { \"case\": \"example\", \"variables\": 3, \"clauses\": 4, \"formula\": \"(x0 OR x1) AND (NOT x0 OR x2) AND (NOT x1 OR NOT x2) AND (x0 OR x2)\", \"assignment\": \"(True, False, True)\", \"assignment_bits\": \"101\", \"expected_assignment\": \"(True, False, True)\", \"matches_expected\": true, \"brute_force_solution_count\": 1, \"success\": true, \"satisfied_clauses\": 4, \"total_clauses\": 4, \"energy\": 0.0, \"iterations\": 2, \"restart\": 0, \"probabilities\": [ 0.0, 1.0, 0.0 ], \"phases\": [ 0.0, 3.141592653589793, 0.0 ] } ]", "tokens": 46, "vector": { "187": 0.09284766908852593, "80": 0.09284766908852593, "122": 0.09284766908852593, "130": -0.09284766908852593, "115": -0.09284766908852593, "183": 0.2785430072655778, "369": -0.3713906763541037, "492": 0.18569533817705186, "354": -0.2785430072655778, "293": -0.2785430072655778, "132": 0.2785430072655778, "206": 0.09284766908852593, "181": 0.5570860145311556, "252": 0.18569533817705186, "36": 0.09284766908852593, "22": -0.09284766908852593, "68": 0.09284766908852593, "308": 0.09284766908852593, "261": -0.09284766908852593, "174": -0.09284766908852593, "167": 0.09284766908852593, "141": 0.09284766908852593, "56": 0.09284766908852593, "298": 0.09284766908852593, "58": 0.09284766908852593, "508": -0.09284766908852593, "390": 0.09284766908852593, "505": 0.09284766908852593 } }, { "id": "results/smoke.csv#0", "path": "results/smoke.csv", "chunk": 0, "text": "domain,bundle_mode,dimension,branches,target_index,distractor_similarity,target_corruption,target_resonance,max_background_resonance,snr,top1_index,top1_correct,top5_correct complex,raw,1000,10,0,0.0,0.0,1.0875034880834307,0.12291312564405077,8.847740893293832,0,True,True bipolar,raw,1000,10,0,0.0,0.0,0.99,0.094,10.53191489361702,0,True,True bipolar,majority,1000,10,0,0.0,0.0,0.24,0.05,4.8,0,True,True complex,raw,1000,100,0,0.0,0.0,0.8329357632683463,0.6698951031922399,1.2433823732987022,0,True,True bipolar,raw,1000,100,0,0.0,0.0,1.084,0.668,1.622754491017964,0,True,True bipolar,majority,1000,100,0,0.0,0.0,0.07,0.066,1.0606060606060606,0,True,True", "tokens": 67, "vector": { "498": 0.05986843400892496, "320": 0.4190790380624747, "328": -0.05986843400892496, "476": -0.05986843400892496, "363": -0.05986843400892496, "175": 0.05986843400892496, "5": -0.05986843400892496, "187": 0.05986843400892496, "317": -0.05986843400892496, "24": -0.05986843400892496, "383": 0.05986843400892496, "45": 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1.0875034880834307, \"max_background_resonance\": 0.12291312564405077, \"snr\": 8.847740893293832, \"top1_index\": 0, \"top1_correct\": true, \"top5_correct\": true }, { \"domain\": \"bipolar\", \"bundle_mode\": \"raw\", \"dimension\": 1000, \"branches\": 10, \"target_index\": 0, \"distractor_similarity\": 0.0, \"target_corruption\": 0.0, \"target_resonance\": 0.99, \"max_background_resonance\": 0.094, \"snr\": 10.53191489361702, \"top1_index\": 0, \"top1_correct\": true, \"top5_correct\": true }, { \"domain\": \"bipolar\", \"bundle_mode\": \"majority\", \"dimension\": 1000, \"branches\": 10, \"target_index\": 0, \"distractor_similarity\": 0.0, \"target_corruption\": 0.0, \"target_resonance\": 0.24, \"max_background_resonance\": 0.05, \"snr\": 4.8, \"top1_index\": 0, \"top1_correct\": true, \"top5_correct\": true }, { \"domain\": \"complex\", \"bundle_mode\": \"raw\", \"dimension\": 1000, \"branches\": 100, \"target_index\": 0, \"distractor_similarity\": 0.0, \"target_corruption\": 0.0, \"target_resonance\": 0.8329357632683463, \"max_background_resonance\": 0.6698951031922399, \"snr\": 1.243382373298", "tokens": 83, "vector": { "498": 0.22325822887437904, "490": 0.11162911443718952, "320": 0.4465164577487581, "471": 0.16744367165578428, "328": -0.22325822887437904, "476": -0.22325822887437904, "74": 0.22325822887437904, "363": -0.2790727860929738, "175": 0.22325822887437904, "5": -0.22325822887437904, "187": 0.22325822887437904, "449": -0.05581455721859476, "317": -0.22325822887437904, "52": -0.05581455721859476, "24": -0.22325822887437904, "279": 0.05581455721859476, "383": 0.16744367165578428, "45": -0.16744367165578428, "181": 0.33488734331156855, "182": 0.16744367165578428, "457": -0.05581455721859476, "396": -0.05581455721859476, "338": 0.05581455721859476, "96": 0.05581455721859476, "290": 0.05581455721859476, "450": -0.05581455721859476, "429": -0.05581455721859476, "56": -0.05581455721859476, "391": 0.05581455721859476 } }, { "id": "results/smoke.json#1", "path": "results/smoke.json", "chunk": 1, "text": "ractor_similarity\": 0.0, \"target_corruption\": 0.0, \"target_resonance\": 0.8329357632683463, \"max_background_resonance\": 0.6698951031922399, \"snr\": 1.2433823732987022, \"top1_index\": 0, \"top1_correct\": true, \"top5_correct\": true }, { \"domain\": \"bipolar\", \"bundle_mode\": \"raw\", \"dimension\": 1000, \"branches\": 100, \"target_index\": 0, \"distractor_similarity\": 0.0, \"target_corruption\": 0.0, \"target_resonance\": 1.084, \"max_background_resonance\": 0.668, \"snr\": 1.622754491017964, \"top1_index\": 0, \"top1_correct\": true, \"top5_correct\": true }, { \"domain\": \"bipolar\", \"bundle_mode\": \"majority\", \"dimension\": 1000, \"branches\": 100, \"target_index\": 0, \"distractor_similarity\": 0.0, \"target_corruption\": 0.0, \"target_resonance\": 0.07, \"max_background_resonance\": 0.066, \"snr\": 1.0606060606060606, \"top1_index\": 0, \"top1_correct\": true, \"top5_correct\": true } ]", "tokens": 57, "vector": { "159": 0.08032193289024989, "5": -0.24096579867074966, "187": 0.24096579867074966, "56": -0.08032193289024989, "317": -0.24096579867074966, "391": 0.08032193289024989, "24": -0.24096579867074966, "405": -0.08032193289024989, "383": 0.24096579867074966, "45": -0.24096579867074966, "181": 0.4819315973414993, "182": 0.24096579867074966, "498": 0.16064386578049977, "457": -0.16064386578049977, "320": 0.32128773156099955, "471": 0.08032193289024989, "328": -0.16064386578049977, "476": -0.16064386578049977, "429": -0.16064386578049977, "363": -0.16064386578049977, "175": 0.16064386578049977, "294": -0.08032193289024989, "367": -0.08032193289024989, "222": 0.08032193289024989, "290": 0.08032193289024989, "281": -0.08032193289024989, "220": -0.08032193289024989, "333": -0.08032193289024989 } }, { "id": "results/tiny/aggregate.csv#0", "path": "results/tiny/aggregate.csv", "chunk": 0, "text": 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\"max_background_resonance\": 1.326, \"snr\": 1.0331825037707392, \"top1_index\": 0, \"top1_correct\": true, \"top5_correct\": true, \"trial_number\": 47 }, { \"domain\": \"bipolar\", \"bundle_mode\": \"majority\", \"dimension\": 1000, \"branches\": 100, \"target_index\": 0, \"distractor_similarity\": 0.25, \"target_corruption\": 0.1, \"target_resonance\": 0.116, \"max_background_resonance\": 0.1, \"snr\": 1.16, \"top1_index\": 0, \"top1_correct\": true, \"top5_correct\": true, \"trial_number\": 48 } ]", "tokens": 45, "vector": { "328": -0.20965696734438366, "320": 0.3144854510165755, "476": -0.20965696734438366, "429": -0.20965696734438366, "363": -0.20965696734438366, "175": 0.20965696734438366, "439": -0.20965696734438366, "5": -0.20965696734438366, "187": 0.20965696734438366, "378": -0.10482848367219183, "317": -0.20965696734438366, "420": -0.10482848367219183, "24": -0.20965696734438366, "134": -0.10482848367219183, "383": 0.20965696734438366, "45": -0.20965696734438366, "181": 0.4193139346887673, "182": 0.20965696734438366, "151": 0.20965696734438366, "49": 0.10482848367219183, "498": 0.10482848367219183, "457": -0.10482848367219183, "290": 0.10482848367219183, "229": 0.10482848367219183, "177": 0.10482848367219183, "408": -0.10482848367219183 } }, { "id": "results/tiny/report.md#0", "path": "results/tiny/report.md", "chunk": 0, "text": "# Everett Engine Scientific Harness Report Generated by `run_benchmark.py`. ## Summary - Raw trials: `48` - Aggregate conditions: `24` - Runtime: `0.14` seconds - Modes: `complex/raw`, `bipolar/raw`, `bipolar/majority` This report evaluates empirical retrieval behavior for a precomputed speculative superposition. It does not claim free planning from scratch: constructing `W_future` is proportional to the number of encoded branches and the vector dimension. Retrieval is algebraic over the precomputed world vector, followed by action dictionary scoring. ## Nominal Conditions Nominal rows use `distractor_similarity=0.0` and `target_corruption=0.0`. | mode | D | N | median SNR | top1 acc | target mean | bg mean | | --- | --- | --- | --- | --- | --- | --- | | bipolar/majority | 1000 | 10 | 6.27 | 1.00 | 0.272 | 0.047 | | bipolar/raw | 1000 | 10 | 7.72 | 1.00 | 1.163 | 0.171 | | complex/raw | 1000 | 10 | 72.37 | 1.00 | 1.058 | 0.052 | | bipolar/majority | 1000 | 100 | 0.90 | 0.50 | 0.064 | 0.070 | | bipolar/raw | 1000 | 100 | 0.66 | 0.00 | 0.557 | 0.843 | | complex/raw | 1000 | 100 | 1.51 | 1.00 | 0.913 | 0.648 | ## Capacity Indicators | mode | first top1 < 95% | first median SNR < 2 | b", "tokens": 146, "vector": { "430": -0.0545544725589981, "493": 0.0545544725589981, "133": 0.0545544725589981, "329": 0.0545544725589981, "132": -0.1091089451179962, "480": 0.0545544725589981, "167": -0.1091089451179962, "194": -0.0545544725589981, "366": 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"85": -0.0545544725589981, "259": -0.0545544725589981, "27": 0.0545544725589981, "200": -0.0545544725589981, "492": -0.0545544725589981, "68": 0.0545544725589981, "423": 0.1091089451179962, "173": -0.0545544725589981 } }, { "id": "results/tiny/report.md#1", "path": "results/tiny/report.md", "chunk": 1, "text": " | 0.00 | 0.557 | 0.843 | | complex/raw | 1000 | 100 | 1.51 | 1.00 | 0.913 | 0.648 | ## Capacity Indicators | mode | first top1 < 95% | first median SNR < 2 | best condition | worst condition | | --- | --- | --- | --- | --- | | bipolar/majority | D=1000, N=100, sim=0, corr=0, top1=0.50, median SNR=0.90 | D=1000, N=100, sim=0, corr=0, top1=0.50, median SNR=0.90 | D=1000, N=10, sim=0, corr=0, top1=1.00, median SNR=6.27 | D=1000, N=100, sim=0.25, corr=0, top1=0.00, median SNR=0.94 | | bipolar/raw | D=1000, N=100, sim=0, corr=0, top1=0.00, median SNR=0.66 | D=1000, N=100, sim=0, corr=0, top1=0.00, median SNR=0.66 | D=1000, N=10, sim=0, corr=0, top1=1.00, median SNR=7.72 | D=1000, N=100, sim=0, corr=0, top1=0.00, median SNR=0.66 | | complex/raw | not observed | D=1000, N=100, sim=0, corr=0, top1=1.00, median SNR=1.51 | D=1000, N=10, sim=0, corr=0, top1=1.00, median SNR=72.37 | D=1000, N=100, sim=0.25, corr=0.1, top1=1.00, median SNR=1.46 | ## Outputs - Raw trials: `raw_trials.csv`, `raw_trials.json` - Aggregate summaries: `aggregate.csv`, `aggregate.json` - Report: `report.md` ## Plots - `results/tiny/plots/snr_vs_branches.png` - `results/tiny/plots/accuracy_vs_branches.png` - `results/", "tokens": 162, "vector": { "497": -0.31622776601683794, "95": 0.02874797872880345, "85": -0.02874797872880345, "490": 0.0574959574576069, "471": 0.1149919149152138, "320": 0.34497574474564136, "429": -0.258731808559231, "259": -0.0574959574576069, "27": 0.02874797872880345, "200": -0.02874797872880345, "492": -0.02874797872880345, "68": 0.02874797872880345, "230": -0.02874797872880345, "423": 0.0574959574576069, "21": -0.34497574474564136, "173": -0.0574959574576069, "120": 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Report Generated by `run_benchmark.py`. ## Summary - Raw trials: `48` - Aggregate conditions: `24` - Runtime: `0.17` seconds - Modes: `complex/raw`, `bipolar/raw`, `bipolar/majority` This report evaluates empirical retrieval behavior for a precomputed speculative superposition. It does not claim free planning from scratch: constructing `W_future` is proportional to the number of encoded branches and the vector dimension. Retrieval is algebraic over the precomputed world vector, followed by action dictionary scoring. ## Nominal Conditions Nominal rows use `distractor_similarity=0.0` and `target_corruption=0.0`. | mode | D | N | median SNR | top1 acc | target mean | bg mean | | --- | --- | --- | --- | --- | --- | --- | | bipolar/majority | 1000 | 10 | 6.27 | 1.00 | 0.272 | 0.047 | | bipolar/raw | 1000 | 10 | 7.72 | 1.00 | 1.163 | 0.171 | | complex/raw | 1000 | 10 | 72.37 | 1.00 | 1.058 | 0.052 | | bipolar/majority | 1000 | 100 | 0.90 | 0.50 | 0.064 | 0.070 | | bipolar/raw | 1000 | 100 | 0.66 | 0.00 | 0.557 | 0.843 | | complex/raw | 1000 | 100 | 1.51 | 1.00 | 0.913 | 0.648 | ## Capacity Indicators | mode | first top1 < 95% | first median SNR < 2 | b", "tokens": 146, "vector": { "430": -0.0545544725589981, "493": 0.0545544725589981, "133": 0.0545544725589981, "329": 0.0545544725589981, "132": -0.1091089451179962, "480": 0.0545544725589981, "167": -0.1091089451179962, "194": -0.0545544725589981, "366": -0.0545544725589981, "374": -0.0545544725589981, "471": 0.3818813079129867, "149": 0.0545544725589981, "408": -0.0545544725589981, "238": -0.1091089451179962, "86": 0.1091089451179962, "450": -0.0545544725589981, "217": 0.0545544725589981, "325": -0.0545544725589981, "89": -0.0545544725589981, "0": -0.0545544725589981, "490": 0.1636634176769943, "457": -0.3273268353539886, "290": 0.1636634176769943, "286": -0.0545544725589981, "109": 0.0545544725589981, "66": 0.0, "264": 0.0545544725589981, "16": 0.0545544725589981, "148": -0.1091089451179962, "207": 0.0, "498": 0.0545544725589981, "42": -0.0545544725589981, "150": -0.0545544725589981, "293": -0.0545544725589981, "477": 0.0545544725589981, "429": -0.2182178902359924, "373": -0.0545544725589981, "90": 0.0545544725589981, "81": -0.0545544725589981, "1": 0.0545544725589981, "506": -0.1091089451179962, "102": -0.0545544725589981, "441": -0.1636634176769943, "18": 0.0545544725589981, "296": 0.0, "118": 0.0545544725589981, "476": -0.0545544725589981, "354": -0.1091089451179962, "432": 0.1091089451179962, "328": -0.0545544725589981, "53": -0.0545544725589981, "95": 0.1091089451179962, "72": 0.0545544725589981, "23": 0.1091089451179962, "445": -0.0545544725589981, "364": 0.0545544725589981, "469": 0.1091089451179962, "444": -0.0545544725589981, "419": 0.0545544725589981, "175": 0.0545544725589981, "5": -0.0545544725589981, "230": -0.1636634176769943, "120": -0.1091089451179962, "24": -0.1091089451179962, "21": -0.1091089451179962, "321": -0.0545544725589981, "52": 0.0545544725589981, "130": -0.1091089451179962, "63": 0.0545544725589981, "320": 0.3273268353539886, "74": 0.1636634176769943, "103": -0.0545544725589981, "497": -0.2727723627949905, "28": -0.0545544725589981, "135": 0.1091089451179962, "61": -0.0545544725589981, "378": -0.0545544725589981, "280": 0.0545544725589981, "361": -0.0545544725589981, "282": 0.0545544725589981, "94": -0.0545544725589981, "69": 0.0545544725589981, "58": -0.0545544725589981, "85": -0.0545544725589981, "259": -0.0545544725589981, "27": 0.0545544725589981, "200": -0.0545544725589981, "492": -0.0545544725589981, "68": 0.0545544725589981, "423": 0.1091089451179962, "173": -0.0545544725589981 } }, { "id": "results/tiny_clean/report.md#1", "path": "results/tiny_clean/report.md", "chunk": 1, "text": " | 0.00 | 0.557 | 0.843 | | complex/raw | 1000 | 100 | 1.51 | 1.00 | 0.913 | 0.648 | ## Capacity Indicators | mode | first top1 < 95% | first median SNR < 2 | best condition | worst condition | | --- | --- | --- | --- | --- | | bipolar/majority | D=1000, N=100, sim=0, corr=0, top1=0.50, median SNR=0.90 | D=1000, N=100, sim=0, corr=0, top1=0.50, median SNR=0.90 | D=1000, N=10, sim=0, corr=0, top1=1.00, median SNR=6.27 | D=1000, N=100, sim=0.25, corr=0, top1=0.00, median SNR=0.94 | | bipolar/raw | D=1000, N=100, sim=0, corr=0, top1=0.00, median SNR=0.66 | D=1000, N=100, sim=0, corr=0, top1=0.00, median SNR=0.66 | D=1000, N=10, sim=0, corr=0, top1=1.00, median SNR=7.72 | D=1000, N=100, sim=0, corr=0, top1=0.00, median SNR=0.66 | | complex/raw | not observed | D=1000, N=100, sim=0, corr=0, top1=1.00, median SNR=1.51 | D=1000, N=10, sim=0, corr=0, top1=1.00, median SNR=72.37 | D=1000, N=100, sim=0.25, corr=0.1, top1=1.00, median SNR=1.46 | ## Outputs - Raw trials: `raw_trials.csv`, `raw_trials.json` - Aggregate summaries: `aggregate.csv`, `aggregate.json` - Report: `report.md` ## Plots - `results/tiny_clean/plots/snr_vs_branches.png` - `results/tiny_clean/plots/accuracy_vs_branches.png`", "tokens": 161, "vector": { "497": -0.3168831610536226, "95": 0.02880756009578387, "85": -0.02880756009578387, "490": 0.05761512019156774, "471": 0.11523024038313548, "320": 0.3456907211494064, "429": -0.2592680408620548, "259": -0.05761512019156774, "27": 0.02880756009578387, "200": -0.02880756009578387, "492": -0.02880756009578387, "68": 0.02880756009578387, "230": -0.02880756009578387, "423": 0.05761512019156774, "21": -0.3456907211494064, "173": -0.05761512019156774, "120": -0.3456907211494064, "24": -0.3456907211494064, "399": -0.02880756009578387, "345": -0.05761512019156774, "321": 0.02880756009578387, "457": -0.05761512019156774, "290": 0.02880756009578387, "80": -0.3168831610536226, "132": -0.3744982812451903, "282": 0.05761512019156774, "361": -0.05761512019156774, "74": 0.0864226802873516, "103": -0.02880756009578387, "439": -0.05761512019156774, "227": -0.02880756009578387, "58": -0.0864226802873516, "135": 0.05761512019156774, "293": -0.02880756009578387, "96": -0.02880756009578387, "378": -0.02880756009578387, "37": 0.02880756009578387, "123": 0.02880756009578387, "149": 0.02880756009578387, "205": -0.05761512019156774, "339": 0.05761512019156774, "258": 0.05761512019156774, "238": -0.0864226802873516, "198": -0.02880756009578387, "33": -0.02880756009578387, "406": -0.0864226802873516, "448": -0.05761512019156774, "364": -0.05761512019156774, "143": -0.05761512019156774, "249": -0.02880756009578387 } }, { "id": "results/tiny_clean/report.md#2", "path": "results/tiny_clean/report.md", "chunk": 2, "text": "ate.csv`, `aggregate.json` - Report: `report.md` ## Plots - `results/tiny_clean/plots/snr_vs_branches.png` - `results/tiny_clean/plots/accuracy_vs_branches.png` - `results/tiny_clean/plots/corruption_sensitivity.png`", "tokens": 23, "vector": { "361": 0.13483997249264842, "339": 0.13483997249264842, "238": -0.13483997249264842, "258": 0.13483997249264842, "132": -0.26967994498529685, "33": -0.13483997249264842, "406": -0.5393598899705937, "448": -0.40451991747794525, "364": -0.40451991747794525, "173": -0.13483997249264842, "143": -0.40451991747794525, "249": -0.13483997249264842, "56": 0.13483997249264842 } }, { "id": "run_benchmark.py#0", "path": "run_benchmark.py", "chunk": 0, "text": "\"\"\"CLI runner for the Everett Engine scientific benchmark harness.\"\"\" from __future__ import annotations import argparse import contextlib import csv import json import os import statistics import time from collections import defaultdict from pathlib import Path from typing import Iterable, List, Optional, Sequence from everett_engine import TrialConfig, TrialResult, run_trial, summarize_result MODES = ( (\"complex\", \"raw\"), (\"bipolar\", \"raw\"), (\"bipolar\", \"majority\"), ) PROFILES = { \"tiny\": { \"dimensions\": \"1000\", \"branches\": \"10,100\", \"distractor_similarity\": \"0.0,0.25\", \"target_corruption\": \"0.0,0.1\", \"seeds_per_condition\": 2, }, \"fast\": { \"dimensions\": \"1000,5000,10000\", \"branches\": \"10,100,1000\", \"distractor_similarity\": \"0.0,0.1,0.25\", \"target_corruption\": \"0.0,0.05,0.1\", \"seeds_per_condition\": 5, }, \"deep\": { \"dimensions\": \"1000,5000,10000,25000\", \"branches\": \"10,100,1000,5000\", \"distractor_similarity\": \"0.0,0.1,0.25,0.5\", \"target_corruption\": \"0.0,0.05,0.1,0.2\", \"seeds_per_condition\": 10, }, } def parse_int_list(value: str) -> List[int]: return [int(item.strip()) for item in value.split(\",\") if item.strip()] def parse_float_list(value: str) -> List[float]: return [float(item", "tokens": 125, "vector": { "409": -0.05018856132284956, "82": -0.05018856132284956, "16": 0.05018856132284956, "441": -0.05018856132284956, "430": -0.05018856132284956, "493": 0.05018856132284956, "133": 0.05018856132284956, "270": 0.05018856132284956, "329": 0.05018856132284956, "373": -0.2509428066142478, "201": -0.05018856132284956, "217": 0.6022627358741947, "297": 0.05018856132284956, "427": 0.05018856132284956, "460": 0.05018856132284956, "339": 0.05018856132284956, "258": 0.05018856132284956, "132": 0.05018856132284956, "73": -0.05018856132284956, "51": -0.05018856132284956, "256": 0.05018856132284956, "156": 0.05018856132284956, "64": 0.05018856132284956, "416": -0.05018856132284956, "504": -0.05018856132284956, "163": 0.15056568396854866, "492": -0.05018856132284956, "447": 0.05018856132284956, "13": 0.05018856132284956, "506": -0.05018856132284956, "424": -0.05018856132284956, "417": 0.05018856132284956, "467": -0.05018856132284956, "0": -0.05018856132284956, "490": 0.05018856132284956, "471": 0.10037712264569912, "457": 0.0, "290": 0.05018856132284956, "469": 0.05018856132284956, "137": -0.05018856132284956, "112": -0.15056568396854866, "320": 0.2509428066142478, "476": -0.15056568396854866, "74": 0.20075424529139824, "429": -0.15056568396854866, "175": 0.20075424529139824, "439": -0.15056568396854866, "5": -0.15056568396854866, "185": -0.15056568396854866, "273": -0.05018856132284956, "15": -0.15056568396854866, "313": -0.10037712264569912, "372": 0.05018856132284956, "8": 0.05018856132284956, "459": 0.10037712264569912, "205": 0.15056568396854866, "396": 0.10037712264569912, "109": 0.10037712264569912, "369": 0.10037712264569912, "330": -0.20075424529139824, "470": -0.10037712264569912, "344": -0.05018856132284956, "173": -0.05018856132284956, "403": -0.05018856132284956, "259": 0.05018856132284956, "374": 0.10037712264569912 } }, { "id": "run_benchmark.py#1", "path": "run_benchmark.py", "chunk": 1, "text": ": str) -> List[int]: return [int(item.strip()) for item in value.split(\",\") if item.strip()] def parse_float_list(value: str) -> List[float]: return [float(item.strip()) for item in value.split(\",\") if item.strip()] def mean(values: Sequence[float]) -> float: return float(statistics.fmean(values)) if values else 0.0 def stdev(values: Sequence[float]) -> float: return float(statistics.stdev(values)) if len(values) > 1 else 0.0 def median(values: Sequence[float]) -> float: return float(statistics.median(values)) if values else 0.0 def pct(values: Sequence[bool]) -> float: return float(sum(1 for value in values if value) / len(values)) if values else 0.0 def apply_profile_defaults(args: argparse.Namespace) -> argparse.Namespace: profile = PROFILES[args.profile] for key, value in profile.items(): if getattr(args, key) is None: setattr(args, key, value) return args def iter_configs(args: argparse.Namespace) -> Iterable[TrialConfig]: base_seed = args.seed for dimension in parse_int_list(args.dimensions): for branches in parse_int_list(args.branches): for similarity in parse_float_list(args.distractor_similarity): for corruption in parse_float_list(args.target_corruption): target_index = ", "tokens": 160, "vector": { "396": 0.0647523908238176, "163": 0.0647523908238176, "109": 0.0647523908238176, "369": 0.22663336788336164, "330": -0.19425717247145283, "470": -0.1295047816476352, "16": 0.22663336788336164, "344": -0.2590095632952704, "205": 0.22663336788336164, "173": -0.0647523908238176, "403": -0.16188097705954402, "459": 0.22663336788336164, "259": 0.09712858623572641, "374": 0.42089054035481444, "130": -0.0323761954119088, "137": -0.42089054035481444, "447": 0.1295047816476352, "73": -0.09712858623572641, "131": -0.0323761954119088, "381": -0.1295047816476352, "114": -0.0647523908238176, "113": 0.0647523908238176, "120": -0.0647523908238176, "2": 0.0323761954119088, "183": -0.0323761954119088, "265": 0.38851434494290565, "55": 0.0323761954119088, "427": 0.09712858623572641, "25": 0.0647523908238176, "469": 0.0323761954119088, "44": 0.09712858623572641, "95": 0.0323761954119088, "124": -0.0323761954119088, "506": -0.0647523908238176, "276": 0.0323761954119088, "168": 0.0323761954119088, "312": 0.0323761954119088, "121": 0.0323761954119088, "328": -0.0323761954119088, "175": 0.09712858623572641, "112": -0.0323761954119088, "476": -0.0647523908238176, "140": 0.0323761954119088, "225": 0.0323761954119088, "5": -0.0323761954119088, "363": -0.0323761954119088 } }, { "id": "run_benchmark.py#2", "path": "run_benchmark.py", "chunk": 2, "text": "list(args.branches): for similarity in parse_float_list(args.distractor_similarity): for corruption in parse_float_list(args.target_corruption): target_index = min(args.target_index, branches - 1) for seed_offset in range(args.seeds_per_condition): for mode_offset, (domain, bundle_mode) in enumerate(MODES): seed = ( base_seed + seed_offset * 10_000 + mode_offset * 1_000_000 + dimension * 3 + branches * 5 + int(similarity * 10_000) * 7 + int(corruption * 10_000) * 11 ) yield TrialConfig( dimension=dimension, branches=branches, target_index=target_index, distractor_similarity=similarity, target_corruption=corruption, seed=seed, domain=domain, bundle_mode=bundle_mode, ) def result_row(result: TrialResult, trial_number: int) -> dict[str, object]: row = result.as_dict() row[\"trial_number\"] = trial_number return row def write_csv(path: Path, rows: list[dict[str, object]]) -> None: if not rows: return path.parent.mkdir(parents=True, exist_ok=True) with path.open(\"w\", newline=\"\", encoding=\"utf-8\") as handle: writer = csv.DictWriter(handle, fieldnames=list(rows[0].keys())) writer.writeheader() writer.writerows(rows) def write_json(path: Path, rows: list[dict[str, object]]) -> None: path.par", "tokens": 137, "vector": { "163": 0.20280805062535753, "265": 0.2535100632816969, "476": -0.2535100632816969, "16": 0.20280805062535753, "140": 0.15210603796901814, "344": -0.20280805062535753, "259": 0.10140402531267877, "175": 0.10140402531267877, "225": 0.15210603796901814, "5": -0.10140402531267877, "363": -0.20280805062535753, "287": -0.05070201265633938, "449": -0.10140402531267877, "290": 0.05070201265633938, "185": 0.10140402531267877, "493": 0.10140402531267877, "498": 0.15210603796901814, "320": 0.15210603796901814, "148": -0.05070201265633938, "0": -0.05070201265633938, "25": -0.15210603796901814, "121": 0.05070201265633938, "313": 0.15210603796901814, "284": -0.05070201265633938, "328": -0.10140402531267877, "109": 0.15210603796901814, "79": 0.05070201265633938, "139": 0.05070201265633938, "506": -0.05070201265633938, "459": 0.15210603796901814, "376": 0.05070201265633938, "246": -0.10140402531267877, "424": 0.0, "151": 0.20280805062535753, "106": -0.15210603796901814, "396": 0.15210603796901814, "41": 0.15210603796901814, "355": -0.15210603796901814, "369": 0.10140402531267877, "401": -0.05070201265633938, "416": -0.3549140885943757, "444": -0.2535100632816969, "276": 0.10140402531267877, "403": -0.05070201265633938, "293": -0.05070201265633938, "484": -0.05070201265633938, "405": 0.05070201265633938, "108": -0.05070201265633938, "181": 0.10140402531267877, "495": 0.05070201265633938, "262": 0.05070201265633938, "35": 0.05070201265633938, "234": 0.05070201265633938, "232": -0.05070201265633938, "153": -0.05070201265633938, "500": -0.05070201265633938, "450": -0.10140402531267877, "339": 0.05070201265633938, "141": 0.05070201265633938, "343": -0.05070201265633938, "150": -0.05070201265633938, "251": 0.05070201265633938, "457": 0.05070201265633938 } }, { "id": "run_benchmark.py#3", "path": "run_benchmark.py", "chunk": 3, "text": "handle, fieldnames=list(rows[0].keys())) writer.writeheader() writer.writerows(rows) def write_json(path: Path, rows: list[dict[str, object]]) -> None: path.parent.mkdir(parents=True, exist_ok=True) path.write_text(json.dumps(rows, indent=2), encoding=\"utf-8\") def aggregate_rows(raw_rows: list[dict[str, object]]) -> list[dict[str, object]]: groups: dict[tuple[object, ...], list[dict[str, object]]] = defaultdict(list) key_fields = ( \"domain\", \"bundle_mode\", \"dimension\", \"branches\", \"distractor_similarity\", \"target_corruption\", ) for row in raw_rows: groups[tuple(row[field] for field in key_fields)].append(row) summaries: list[dict[str, object]] = [] for key, rows in sorted(groups.items()): target = [float(row[\"target_resonance\"]) for row in rows] background = [float(row[\"max_background_resonance\"]) for row in rows] snr = [float(row[\"snr\"]) for row in rows] top1 = [bool(row[\"top1_correct\"]) for row in rows] top5 = [bool(row[\"top5_correct\"]) for row in rows] summary = dict(zip(key_fields, key)) summary.update( { \"trials\": len(rows), \"target_mean\": mean(target), \"target_std\": stdev(target), \"target_min\": min(target), \"target_max\": max(target), \"background_mean\": mean(background), \"back", "tokens": 155, "vector": { "450": -0.037139067635410375, "343": -0.037139067635410375, "163": 0.25997347344787264, "444": -0.4085297439895141, "328": 0.0, "185": 0.07427813527082075, "424": 0.037139067635410375, "150": -0.037139067635410375, "459": 0.07427813527082075, "251": -0.037139067635410375, "416": -0.1485562705416415, "106": -0.25997347344787264, "396": 0.18569533817705186, "41": 0.22283440581246225, "276": 0.037139067635410375, "484": -0.037139067635410375, "405": 0.037139067635410375, "108": -0.037139067635410375, "181": 0.07427813527082075, "495": 0.037139067635410375, "230": -0.037139067635410375, "258": 0.037139067635410375, "336": 0.037139067635410375, "383": 0.037139067635410375, "232": -0.037139067635410375, "153": -0.037139067635410375, "469": -0.037139067635410375, "488": 0.07427813527082075, "334": 0.11141720290623112, "156": 0.037139067635410375, "133": -0.11141720290623112, "498": 0.037139067635410375, "320": 0.037139067635410375, "476": -0.037139067635410375, "175": 0.037139067635410375, "5": -0.037139067635410375, "16": 0.297112541083283, "355": -0.48280787926033486, "344": -0.297112541083283, "192": 0.07427813527082075, "174": -0.037139067635410375, "198": -0.037139067635410375, "44": 0.07427813527082075, "219": 0.037139067635410375, "95": 0.037139067635410375, "52": 0.18569533817705186, "374": 0.037139067635410375, "187": 0.037139067635410375, "393": -0.07427813527082075, "317": -0.07427813527082075, "24": -0.07427813527082075, "21": -0.037139067635410375, "183": -0.07427813527082075, "45": -0.037139067635410375, "363": -0.037139067635410375, "182": 0.037139067635410375, "74": 0.037139067635410375, "297": -0.037139067635410375, "149": 0.037139067635410375, "113": 0.037139067635410375, "130": -0.07427813527082075, "313": -0.037139067635410375, "114": -0.037139067635410375, "425": -0.037139067635410375, "287": -0.037139067635410375, "246": -0.037139067635410375, "411": -0.037139067635410375, "415": -0.037139067635410375, "60": -0.037139067635410375 } }, { "id": "run_benchmark.py#4", "path": "run_benchmark.py", "chunk": 4, "text": "ows), \"target_mean\": mean(target), \"target_std\": stdev(target), \"target_min\": min(target), \"target_max\": max(target), \"background_mean\": mean(background), \"background_std\": stdev(background), \"background_min\": min(background), \"background_max\": max(background), \"snr_mean\": mean(snr), \"snr_std\": stdev(snr), \"snr_min\": min(snr), \"snr_max\": max(snr), \"snr_median\": median(snr), \"top1_accuracy\": pct(top1), \"top5_accuracy\": pct(top5), \"failure_count\": sum(1 for value in top1 if not value), } ) summaries.append(summary) return summaries def mode_label(row: dict[str, object]) -> str: return f\"{row['domain']}/{row['bundle_mode']}\" def capacity_indicators(aggregate: list[dict[str, object]]) -> dict[str, dict[str, object]]: indicators: dict[str, dict[str, object]] = {} for row in aggregate: label = mode_label(row) indicators.setdefault(label, {\"mode\": label, \"accuracy_drop\": None, \"snr_drop\": None}) for label, bucket in indicators.items(): rows = [row for row in aggregate if mode_label(row) == label] ordered = sorted( rows, key=lambda row: ( float(row[\"distractor_similarity\"]), float(row[\"target_corruption\"]), int(row[\"dimension\"]), int(row[\"branches\"]), ), ) accuracy_drop = next( (row for ro", "tokens": 145, "vector": { "320": 0.0838443616300637, "317": -0.04192218081503185, "130": -0.12576654244509555, "52": 0.1676887232601274, "313": -0.04192218081503185, "114": -0.12576654244509555, "425": -0.04192218081503185, "287": -0.12576654244509555, "246": -0.04192218081503185, "411": -0.12576654244509555, "415": -0.04192218081503185, "393": -0.1676887232601274, "3": -0.04192218081503185, "353": -0.04192218081503185, "432": -0.04192218081503185, "273": 0.04192218081503185, "24": -0.20961090407515925, "458": 0.04192218081503185, "274": 0.04192218081503185, "94": -0.04192218081503185, "316": 0.04192218081503185, "120": -0.04192218081503185, "44": 0.0, "2": 0.0838443616300637, "21": -0.0838443616300637, "488": 0.04192218081503185, "363": -0.04192218081503185, "20": 0.04192218081503185, "265": 0.04192218081503185, "16": 0.20961090407515925, "205": 0.0838443616300637, "344": -0.1676887232601274, "403": -0.0838443616300637, "293": -0.04192218081503185, "198": -0.0838443616300637, "174": -0.04192218081503185, "374": 0.04192218081503185, "369": 0.0838443616300637, "459": 0.0838443616300637, "259": -0.12576654244509555, "355": -0.5869105314104459, "106": -0.2515330848901911, "396": 0.29345526570522296, "41": 0.1676887232601274, "498": 0.04192218081503185, "123": 0.04192218081503185, "238": -0.12576654244509555, "163": 0.04192218081503185, "68": 0.12576654244509555, "26": -0.20961090407515925, "30": 0.04192218081503185, "230": -0.04192218081503185, "447": 0.0838443616300637, "276": 0.0838443616300637, "511": 0.04192218081503185, "453": -0.04192218081503185, "95": 0.04192218081503185, "444": -0.0838443616300637, "383": -0.04192218081503185, "219": 0.04192218081503185, "85": 0.04192218081503185, "175": 0.04192218081503185, "5": -0.04192218081503185, "109": 0.0838443616300637, "328": -0.04192218081503185, "476": -0.04192218081503185, "454": 0.04192218081503185, "382": 0.04192218081503185 } }, { "id": "run_benchmark.py#5", "path": "run_benchmark.py", "chunk": 5, "text": "row: ( float(row[\"distractor_similarity\"]), float(row[\"target_corruption\"]), int(row[\"dimension\"]), int(row[\"branches\"]), ), ) accuracy_drop = next( (row for row in ordered if float(row[\"top1_accuracy\"]) < 0.95), None, ) snr_drop = next((row for row in ordered if float(row[\"snr_median\"]) < 2.0), None) best = max(rows, key=lambda row: (float(row[\"top1_accuracy\"]), float(row[\"snr_median\"]))) worst = min(rows, key=lambda row: (float(row[\"top1_accuracy\"]), float(row[\"snr_median\"]))) bucket[\"accuracy_drop\"] = compact_condition(accuracy_drop) if accuracy_drop else \"not observed\" bucket[\"snr_drop\"] = compact_condition(snr_drop) if snr_drop else \"not observed\" bucket[\"best_condition\"] = compact_condition(best) bucket[\"worst_condition\"] = compact_condition(worst) return indicators def compact_condition(row: Optional[dict[str, object]]) -> str: if row is None: return \"not observed\" return ( f\"D={int(row['dimension'])}, N={int(row['branches'])}, \" f\"sim={float(row['distractor_similarity']):g}, \" f\"corr={float(row['target_corruption']):g}, \" f\"top1={float(row['top1_accuracy']):.2f}, \" f\"median SNR={float(row['snr_median']):.2f}\" ) def select_nominal_rows(aggregate: list[dict[str, object]]) -> ", "tokens": 138, "vector": { "355": -0.778216007955863, "374": 0.3735436838188142, "175": 0.062257280636469035, "5": -0.062257280636469035, "109": 0.12451456127293807, "328": -0.062257280636469035, "476": -0.062257280636469035, "447": 0.12451456127293807, "454": 0.062257280636469035, "16": 0.062257280636469035, "344": -0.062257280636469035, "383": -0.062257280636469035, "403": -0.1556432015911726, "44": -0.062257280636469035, "173": -0.031128640318234518, "276": 0.09338592095470355, "511": 0.12451456127293807, "316": 0.12451456127293807, "399": -0.062257280636469035, "411": -0.031128640318234518, "444": -0.062257280636469035, "85": 0.062257280636469035, "321": 0.062257280636469035, "287": -0.031128640318234518, "453": -0.12451456127293807, "123": -0.1556432015911726, "381": -0.062257280636469035, "293": -0.09338592095470355, "96": -0.09338592095470355, "307": 0.031128640318234518, "448": -0.031128640318234518, "369": 0.09338592095470355, "68": 0.031128640318234518, "459": 0.062257280636469035, "492": -0.031128640318234518, "106": -0.062257280636469035, "396": 0.09338592095470355, "41": 0.062257280636469035, "506": -0.031128640318234518, "80": -0.031128640318234518, "132": -0.031128640318234518, "21": -0.031128640318234518, "312": -0.062257280636469035, "120": -0.031128640318234518, "24": -0.031128640318234518, "474": -0.031128640318234518, "238": -0.031128640318234518, "163": 0.031128640318234518 } }, { "id": "run_benchmark.py#6", "path": "run_benchmark.py", "chunk": 6, "text": "g}, \" f\"top1={float(row['top1_accuracy']):.2f}, \" f\"median SNR={float(row['snr_median']):.2f}\" ) def select_nominal_rows(aggregate: list[dict[str, object]]) -> list[dict[str, object]]: zeroish = [ row for row in aggregate if float(row[\"distractor_similarity\"]) == 0.0 and float(row[\"target_corruption\"]) == 0.0 ] return sorted(zeroish, key=lambda row: (mode_label(row), int(row[\"dimension\"]), int(row[\"branches\"]))) def markdown_table(rows: list[list[object]], headers: list[str]) -> str: lines = [ \"| \" + \" | \".join(headers) + \" |\", \"| \" + \" | \".join(\"---\" for _ in headers) + \" |\", ] for row in rows: lines.append(\"| \" + \" | \".join(str(item) for item in row) + \" |\") return \"\\n\".join(lines) def write_report( path: Path, raw_rows: list[dict[str, object]], aggregate: list[dict[str, object]], elapsed_seconds: float, plot_paths: list[Path], ) -> None: indicators = capacity_indicators(aggregate) nominal = select_nominal_rows(aggregate) headline = sorted( nominal, key=lambda row: (int(row[\"dimension\"]), int(row[\"branches\"]), mode_label(row)), )[:18] capacity_rows = [ [ label, bucket[\"accuracy_drop\"], bucket[\"snr_drop\"], bucket[\"best_condition\"], bucket[\"worst_condition\"], ] for label, bucket in", "tokens": 138, "vector": { "21": -0.037267799624996496, "374": 0.18633899812498247, "355": -0.5962847939999439, "44": 0.037267799624996496, "312": -0.07453559924999299, "120": -0.037267799624996496, "24": -0.037267799624996496, "316": 0.037267799624996496, "459": 0.11180339887498948, "474": -0.07453559924999299, "238": -0.18633899812498247, "163": 0.29814239699997197, "106": -0.14907119849998599, "396": 0.2608745973749754, "41": 0.18633899812498247, "287": 0.07453559924999299, "16": 0.18633899812498247, "344": -0.33541019662496846, "403": -0.037267799624996496, "175": 0.037267799624996496, "354": -0.037267799624996496, "5": -0.037267799624996496, "369": 0.07453559924999299, "219": 0.07453559924999299, "85": 0.07453559924999299, "259": -0.07453559924999299, "109": 0.14907119849998599, "328": -0.07453559924999299, "476": -0.07453559924999299, "340": -0.037267799624996496, "444": -0.07453559924999299, "311": -0.11180339887498948, "94": -0.11180339887498948, "174": -0.037267799624996496, "330": -0.07453559924999299, "173": 0.037267799624996496, "416": -0.11180339887498948, "488": 0.037267799624996496, "107": -0.037267799624996496, "321": 0.037267799624996496, "276": 0.037267799624996496, "68": 0.037267799624996496, "123": 0.037267799624996496, "469": 0.07453559924999299, "293": -0.037267799624996496, "334": 0.037267799624996496, "462": 0.037267799624996496, "26": -0.07453559924999299, "453": -0.18633899812498247, "447": 0.037267799624996496, "511": 0.037267799624996496, "307": 0.037267799624996496, "448": -0.037267799624996496 } }, { "id": "run_benchmark.py#7", "path": "run_benchmark.py", "chunk": 7, "text": "ow)), )[:18] capacity_rows = [ [ label, bucket[\"accuracy_drop\"], bucket[\"snr_drop\"], bucket[\"best_condition\"], bucket[\"worst_condition\"], ] for label, bucket in sorted(indicators.items()) ] headline_rows = [ [ mode_label(row), int(row[\"dimension\"]), int(row[\"branches\"]), f\"{float(row['snr_median']):.2f}\", f\"{float(row['top1_accuracy']):.2f}\", f\"{float(row['target_mean']):.3f}\", f\"{float(row['background_mean']):.3f}\", ] for row in headline ] plot_lines = \"\\n\".join(f\"- `{plot}`\" for plot in plot_paths) if plot_paths else \"- plots disabled\" content = f\"\"\"# Everett Engine Scientific Harness Report Generated by `run_benchmark.py`. ## Summary - Raw trials: `{len(raw_rows)}` - Aggregate conditions: `{len(aggregate)}` - Runtime: `{elapsed_seconds:.2f}` seconds - Modes: `complex/raw`, `bipolar/raw`, `bipolar/majority` This report evaluates empirical retrieval behavior for a precomputed speculative superposition. It does not claim free planning from scratch: constructing `W_future` is proportional to the number of encoded branches and the vector dimension. Retrieval is algebraic over the precomputed world vector, followed by action dictionary scoring. ## Nominal Conditions Nominal rows use `", "tokens": 140, "vector": { "21": -0.05716619504750295, "334": 0.05716619504750295, "462": 0.05716619504750295, "26": -0.1143323900950059, "453": -0.2858309752375147, "447": 0.05716619504750295, "511": 0.05716619504750295, "307": 0.05716619504750295, "448": -0.05716619504750295, "16": 0.2286647801900118, "344": -0.2286647801900118, "219": 0.05716619504750295, "68": 0.05716619504750295, "95": 0.1143323900950059, "449": 0.05716619504750295, "259": -0.05716619504750295, "355": -0.4573295603800236, "109": 0.17149858514250885, "328": -0.1143323900950059, "476": -0.1143323900950059, "374": 0.17149858514250885, "316": 0.05716619504750295, "312": -0.17149858514250885, "44": -0.05716619504750295, "317": -0.05716619504750295, "292": 0.1143323900950059, "415": -0.05716619504750295, "293": -0.1143323900950059, "63": 0.05716619504750295, "15": 0.1143323900950059, "321": 0.1143323900950059, "403": -0.05716619504750295, "381": -0.05716619504750295, "406": -0.05716619504750295, "279": 0.05716619504750295, "237": -0.05716619504750295, "430": -0.05716619504750295, "493": 0.05716619504750295, "133": 0.05716619504750295, "329": 0.05716619504750295, "132": -0.1143323900950059, "480": 0.05716619504750295, "167": -0.1143323900950059, "194": -0.05716619504750295, "366": -0.05716619504750295, "471": 0.17149858514250885, "149": 0.05716619504750295, "113": 0.1143323900950059, "488": 0.05716619504750295, "238": -0.17149858514250885, "86": 0.1143323900950059, "217": 0.05716619504750295, "107": -0.05716619504750295, "89": -0.05716619504750295, "0": -0.05716619504750295, "490": 0.05716619504750295, "457": -0.1143323900950059, "290": 0.05716619504750295, "286": -0.05716619504750295, "66": 0.0, "264": 0.05716619504750295, "148": -0.1143323900950059, "207": 0.05716619504750295, "498": 0.05716619504750295, "42": -0.05716619504750295, "150": -0.05716619504750295, "477": 0.05716619504750295, "429": -0.05716619504750295, "373": -0.05716619504750295, "90": 0.05716619504750295, "81": -0.05716619504750295, "1": 0.05716619504750295, "506": -0.1143323900950059, "102": -0.05716619504750295, "441": -0.17149858514250885, "18": 0.05716619504750295, "296": 0.05716619504750295, "118": 0.05716619504750295, "354": -0.05716619504750295, "432": 0.1143323900950059, "53": -0.05716619504750295, "72": 0.05716619504750295, "23": 0.1143323900950059, "445": -0.05716619504750295, "364": 0.05716619504750295, "469": 0.1143323900950059, "444": -0.05716619504750295, "419": 0.05716619504750295 } }, { "id": "run_benchmark.py#8", "path": "run_benchmark.py", "chunk": 8, "text": " the vector dimension. Retrieval is algebraic over the precomputed world vector, followed by action dictionary scoring. ## Nominal Conditions Nominal rows use `distractor_similarity=0.0` and `target_corruption=0.0`. {markdown_table(headline_rows, [\"mode\", \"D\", \"N\", \"median SNR\", \"top1 acc\", \"target mean\", \"bg mean\"])} ## Capacity Indicators {markdown_table(capacity_rows, [\"mode\", \"first top1 < 95%\", \"first median SNR < 2\", \"best condition\", \"worst condition\"])} ## Outputs - Raw trials: `raw_trials.csv`, `raw_trials.json` - Aggregate summaries: `aggregate.csv`, `aggregate.json` - Report: `report.md` ## Plots {plot_lines} \"\"\" path.parent.mkdir(parents=True, exist_ok=True) path.write_text(content, encoding=\"utf-8\") def import_pyplot(out_dir: Path): cache_dir = out_dir.parent / \".mplconfig\" cache_dir.mkdir(parents=True, exist_ok=True) os.environ.setdefault(\"MPLCONFIGDIR\", str(cache_dir)) with open(os.devnull, \"w\", encoding=\"utf-8\") as devnull: with contextlib.redirect_stderr(devnull): import matplotlib matplotlib.use(\"Agg\") import matplotlib.pyplot as plt return plt def filtered_plot_rows(aggregate: list[dict[str, object]]) -> list[dict[str, object]]: return [ row for row in aggregate ", "tokens": 141, "vector": { "441": -0.1214913478461574, "432": 0.1214913478461574, "328": -0.0607456739230787, "366": -0.0607456739230787, "506": -0.0607456739230787, "53": -0.0607456739230787, "95": 0.0607456739230787, "148": -0.0607456739230787, "72": 0.0607456739230787, "23": 0.1214913478461574, "167": -0.0607456739230787, "445": -0.0607456739230787, "364": 0.0607456739230787, "469": 0.1214913478461574, "86": 0.0607456739230787, "444": -0.0607456739230787, "419": 0.1214913478461574, "175": 0.0607456739230787, "354": -0.0607456739230787, "5": -0.0607456739230787, "340": -0.1214913478461574, "449": 0.0607456739230787, "230": -0.1822370217692361, "120": -0.1214913478461574, "24": -0.1214913478461574, "21": -0.1214913478461574, "321": 0.0, "52": 0.0607456739230787, "130": -0.1214913478461574, "63": 0.1214913478461574, "492": -0.0607456739230787, "68": 0.0607456739230787, "462": 0.0607456739230787, "423": 0.1214913478461574, "173": -0.0607456739230787, "399": -0.0607456739230787, "345": -0.1214913478461574, "123": 0.0607456739230787, "471": 0.0, "149": 0.0607456739230787, "205": -0.1214913478461574, "339": 0.1214913478461574, "258": 0.1214913478461574, "238": -0.30372836961539346, "198": -0.0607456739230787, "132": 0.0, "33": -0.0607456739230787, "406": -0.0607456739230787, "416": -0.1822370217692361, "484": -0.1214913478461574, "405": 0.1214913478461574, "108": -0.1214913478461574, "181": 0.2429826956923148, "495": 0.1214913478461574, "237": -0.0607456739230787, "232": -0.1214913478461574, "153": -0.2429826956923148, "459": 0.1214913478461574, "355": -0.0607456739230787, "42": -0.1822370217692361, "264": 0.0607456739230787, "76": -0.0607456739230787, "30": 0.0607456739230787, "396": 0.1822370217692361, "262": 0.1214913478461574, "35": 0.0607456739230787, "113": -0.1822370217692361, "500": -0.1214913478461574, "460": 0.0607456739230787, "333": 0.0607456739230787, "217": 0.1214913478461574, "299": -0.1822370217692361, "84": 0.0607456739230787, "146": 0.0607456739230787, "257": -0.1214913478461574, "369": 0.1214913478461574, "389": -0.0607456739230787, "163": 0.1214913478461574, "106": -0.1214913478461574, "41": 0.1214913478461574, "16": 0.0607456739230787, "344": -0.0607456739230787 } }, { "id": "run_benchmark.py#9", "path": "run_benchmark.py", "chunk": 9, "text": "rt matplotlib.pyplot as plt return plt def filtered_plot_rows(aggregate: list[dict[str, object]]) -> list[dict[str, object]]: return [ row for row in aggregate if float(row[\"distractor_similarity\"]) == 0.0 and float(row[\"target_corruption\"]) == 0.0 ] def plot_metric( plt, rows: list[dict[str, object]], out_path: Path, y_field: str, y_label: str, title: str, ) -> None: plt.figure(figsize=(9, 5.5)) dimensions = sorted({int(row[\"dimension\"]) for row in rows}) for label in sorted({mode_label(row) for row in rows}): for dimension in dimensions: series = [ row for row in rows if mode_label(row) == label and int(row[\"dimension\"]) == dimension ] series = sorted(series, key=lambda row: int(row[\"branches\"])) if not series: continue xs = [int(row[\"branches\"]) for row in series] ys = [float(row[y_field]) for row in series] plt.plot(xs, ys, marker=\"o\", label=f\"{label} D={dimension}\") plt.xscale(\"log\") plt.xlabel(\"branches\") plt.ylabel(y_label) plt.title(title) plt.grid(True, alpha=0.25) plt.legend(fontsize=\"small\", ncol=2) plt.tight_layout() plt.savefig(out_path, dpi=150) plt.close() def plot_corruption_sensitivity(plt, aggregate: list[dict[str, object]], out_path: Path) -> None: candidates = [", "tokens": 170, "vector": { "461": 0.03137279025690793, "299": -0.03137279025690793, "146": 0.03137279025690793, "500": -0.03137279025690793, "257": -0.4705918538536189, "369": 0.06274558051381586, "459": 0.09411837077072378, "389": -0.03137279025690793, "238": -0.09411837077072378, "163": 0.12549116102763172, "106": -0.12549116102763172, "396": 0.2196095317983555, "41": 0.12549116102763172, "355": -0.5647102246243427, "16": 0.25098232205526344, "344": -0.2196095317983555, "403": -0.09411837077072378, "374": 0.09411837077072378, "175": 0.03137279025690793, "354": -0.06274558051381586, "5": -0.03137279025690793, "99": -0.03137279025690793, "444": -0.12549116102763172, "475": 0.09411837077072378, "416": -0.06274558051381586, "235": -0.06274558051381586, "174": 0.06274558051381586, "170": 0.12549116102763172, "276": 0.06274558051381586, "392": -0.03137279025690793, "136": -0.03137279025690793, "112": -0.06274558051381586, "219": 0.09411837077072378, "109": 0.12549116102763172, "328": -0.15686395128453964, "26": -0.12549116102763172, "259": -0.06274558051381586, "156": 0.18823674154144757, "44": 0.03137279025690793, "85": 0.03137279025690793, "476": -0.09411837077072378, "293": -0.03137279025690793, "226": 0.03137279025690793, "417": -0.06274558051381586, "75": -0.06274558051381586, "15": 0.0, "493": 0.03137279025690793, "496": 0.03137279025690793, "387": -0.03137279025690793, "415": 0.03137279025690793, "181": 0.03137279025690793, "398": 0.03137279025690793, "439": -0.03137279025690793, "442": 0.0, "504": -0.03137279025690793, "385": -0.03137279025690793, "189": 0.03137279025690793, "413": -0.03137279025690793, "409": -0.03137279025690793, "168": 0.03137279025690793, "154": 0.03137279025690793, "305": 0.03137279025690793 } }, { "id": "run_benchmark.py#10", "path": "run_benchmark.py", "chunk": 10, "text": "ut() plt.savefig(out_path, dpi=150) plt.close() def plot_corruption_sensitivity(plt, aggregate: list[dict[str, object]], out_path: Path) -> None: candidates = [ row for row in aggregate if float(row[\"distractor_similarity\"]) == 0.0 ] if not candidates: return dimension = max(int(row[\"dimension\"]) for row in candidates) branches = max(int(row[\"branches\"]) for row in candidates if int(row[\"dimension\"]) == dimension) rows = [ row for row in candidates if int(row[\"dimension\"]) == dimension and int(row[\"branches\"]) == branches ] plt.figure(figsize=(8, 5)) for label in sorted({mode_label(row) for row in rows}): series = sorted( [row for row in rows if mode_label(row) == label], key=lambda row: float(row[\"target_corruption\"]), ) xs = [float(row[\"target_corruption\"]) for row in series] ys = [float(row[\"top1_accuracy\"]) for row in series] plt.plot(xs, ys, marker=\"o\", label=label) plt.xlabel(\"target-query corruption\") plt.ylabel(\"top-1 accuracy\") plt.title(f\"Corruption sensitivity at D={dimension}, N={branches}\") plt.ylim(-0.03, 1.03) plt.grid(True, alpha=0.25) plt.legend() plt.tight_layout() plt.savefig(out_path, dpi=150) plt.close() def write_plots(out_dir: Path, aggregate: list[dict[str, ", "tokens": 169, "vector": { "70": -0.029373674772302332, "257": -0.41123144681223267, "409": -0.058747349544604664, "475": 0.08812102431690699, "168": 0.058747349544604664, "154": 0.058747349544604664, "305": 0.058747349544604664, "459": 0.058747349544604664, "170": 0.058747349544604664, "238": -0.08812102431690699, "163": 0.058747349544604664, "106": -0.058747349544604664, "396": 0.058747349544604664, "41": 0.029373674772302332, "416": -0.058747349544604664, "276": 0.029373674772302332, "442": -0.11749469908920933, "355": -0.6755945197629537, "16": 0.264363072950721, "344": -0.20561572340611634, "403": -0.14686837386151166, "374": 0.11749469908920933, "175": 0.029373674772302332, "293": -0.029373674772302332, "369": 0.029373674772302332, "328": -0.20561572340611634, "411": -0.058747349544604664, "109": 0.14686837386151166, "476": -0.14686837386151166, "444": -0.08812102431690699, "354": -0.029373674772302332, "392": -0.029373674772302332, "136": -0.029373674772302332, "26": -0.11749469908920933, "219": 0.058747349544604664, "259": -0.058747349544604664, "156": 0.08812102431690699, "44": 0.0, "85": 0.029373674772302332, "5": -0.058747349544604664, "417": -0.058747349544604664, "75": -0.058747349544604664, "15": 0.0, "493": 0.029373674772302332, "52": 0.029373674772302332, "424": -0.029373674772302332, "225": 0.058747349544604664, "415": 0.029373674772302332, "40": -0.029373674772302332, "92": 0.029373674772302332, "177": -0.029373674772302332, "490": -0.029373674772302332, "11": -0.058747349544604664, "181": 0.029373674772302332, "398": 0.029373674772302332, "439": -0.029373674772302332, "413": -0.029373674772302332, "387": 0.029373674772302332, "153": -0.029373674772302332 } }, { "id": "run_benchmark.py#11", "path": "run_benchmark.py", "chunk": 11, "text": " plt.grid(True, alpha=0.25) plt.legend() plt.tight_layout() plt.savefig(out_path, dpi=150) plt.close() def write_plots(out_dir: Path, aggregate: list[dict[str, object]]) -> list[Path]: plt = import_pyplot(out_dir) plots_dir = out_dir / \"plots\" plots_dir.mkdir(parents=True, exist_ok=True) paths = [ plots_dir / \"snr_vs_branches.png\", plots_dir / \"accuracy_vs_branches.png\", plots_dir / \"corruption_sensitivity.png\", ] nominal = filtered_plot_rows(aggregate) plot_metric( plt, nominal, paths[0], \"snr_median\", \"median SNR\", \"Median SNR vs branch count\", ) plot_metric( plt, nominal, paths[1], \"top1_accuracy\", \"top-1 accuracy\", \"Top-1 accuracy vs branch count\", ) plot_corruption_sensitivity(plt, aggregate, paths[2]) return paths def run(args: argparse.Namespace) -> None: args = apply_profile_defaults(args) out_dir = args.out_dir out_dir.mkdir(parents=True, exist_ok=True) started = time.perf_counter() raw_rows: list[dict[str, object]] = [] for trial_number, config in enumerate(iter_configs(args), start=1): result = run_trial(config) raw_rows.append(result_row(result, trial_number)) if args.verbose: print(summarize_result(result), flush=True) elapsed = time.perf_counter() - started aggregate ", "tokens": 137, "vector": { "257": -0.4407318961869526, "15": -0.048970210687439175, "181": 0.29382126412463505, "398": 0.048970210687439175, "439": -0.048970210687439175, "442": 0.048970210687439175, "413": -0.048970210687439175, "409": -0.048970210687439175, "475": 0.048970210687439175, "168": 0.048970210687439175, "154": 0.048970210687439175, "305": 0.048970210687439175, "459": 0.09794042137487835, "387": 0.048970210687439175, "153": -0.29382126412463505, "416": -0.09794042137487835, "238": -0.1958808427497567, "163": 0.14691063206231753, "106": -0.09794042137487835, "396": 0.09794042137487835, "41": 0.09794042137487835, "355": 0.048970210687439175, "267": -0.24485105343719588, "406": -0.048970210687439175, "405": 0.09794042137487835, "108": -0.09794042137487835, "495": 0.09794042137487835, "336": -0.24485105343719588, "173": -0.048970210687439175, "143": -0.14691063206231753, "249": -0.048970210687439175, "56": 0.048970210687439175, "469": 0.14691063206231753, "389": -0.048970210687439175, "99": -0.09794042137487835, "316": 0.048970210687439175, "120": -0.09794042137487835, "24": -0.09794042137487835, "241": 0.09794042137487835, "243": 0.09794042137487835, "364": -0.09794042137487835, "44": -0.048970210687439175, "40": -0.09794042137487835, "344": 0.048970210687439175, "170": 0.048970210687439175, "369": 0.048970210687439175, "172": -0.048970210687439175, "265": 0.29382126412463505, "427": 0.048970210687439175, "403": 0.0, "276": 0.048970210687439175, "55": 0.048970210687439175, "259": 0.09794042137487835, "51": -0.09794042137487835, "211": -0.09794042137487835, "488": 0.09794042137487835, "16": 0.048970210687439175, "151": 0.09794042137487835, "183": 0.14691063206231753, "148": -0.048970210687439175, "312": 0.048970210687439175, "460": 0.048970210687439175, "246": -0.14691063206231753, "417": 0.048970210687439175, "174": -0.048970210687439175, "376": 0.048970210687439175, "253": 0.048970210687439175, "462": 0.048970210687439175, "467": -0.048970210687439175, "501": -0.048970210687439175 } }, { "id": "run_benchmark.py#12", "path": "run_benchmark.py", "chunk": 12, "text": "aw_rows.append(result_row(result, trial_number)) if args.verbose: print(summarize_result(result), flush=True) elapsed = time.perf_counter() - started aggregate = aggregate_rows(raw_rows) raw_csv = out_dir / \"raw_trials.csv\" raw_json = out_dir / \"raw_trials.json\" aggregate_csv = out_dir / \"aggregate.csv\" aggregate_json = out_dir / \"aggregate.json\" report = out_dir / \"report.md\" write_csv(raw_csv, raw_rows) write_json(raw_json, raw_rows) write_csv(aggregate_csv, aggregate) write_json(aggregate_json, aggregate) plot_paths = [] if args.no_plots else write_plots(out_dir, aggregate) write_report(report, raw_rows, aggregate, elapsed, plot_paths) print(f\"ran {len(raw_rows)} trials across {len(aggregate)} aggregate conditions\") print(f\"runtime: {elapsed:.2f}s\") print(f\"wrote {raw_csv}\") print(f\"wrote {raw_json}\") print(f\"wrote {aggregate_csv}\") print(f\"wrote {aggregate_json}\") print(f\"wrote {report}\") for path in plot_paths: print(f\"wrote {path}\") def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--profile\", choices=sorted(PROFILES), default=\"fast\") parser.add_argument(\"--dimensions\") parser.add_argument(\"--branches\") parser.add_argument(\"--distr", "tokens": 127, "vector": { "378": -0.047727395990334764, "174": -0.047727395990334764, "376": 0.047727395990334764, "246": -0.09545479198066953, "151": -0.09545479198066953, "403": -0.09545479198066953, "265": 0.09545479198066953, "253": 0.047727395990334764, "462": 0.4295465639130129, "467": -0.047727395990334764, "501": -0.047727395990334764, "181": 0.047727395990334764, "183": 0.1431821879710043, "51": -0.047727395990334764, "211": -0.047727395990334764, "259": 0.047727395990334764, "238": -0.4295465639130129, "469": 0.0, "488": 0.23863697995167382, "366": 0.1431821879710043, "153": -0.2863643759420086, "205": -0.09545479198066953, "339": 0.09545479198066953, "233": -0.1431821879710043, "258": 0.09545479198066953, "338": -0.1431821879710043, "132": -0.19090958396133906, "33": -0.047727395990334764, "401": -0.09545479198066953, "251": 0.09545479198066953, "321": 0.1431821879710043, "416": -0.047727395990334764, "381": -0.047727395990334764, "387": 0.047727395990334764, "173": 0.047727395990334764, "200": 0.047727395990334764, "113": 0.09545479198066953, "149": 0.047727395990334764, "478": 0.047727395990334764, "86": 0.047727395990334764, "217": 0.047727395990334764, "312": -0.047727395990334764, "346": -0.2863643759420086, "16": 0.047727395990334764, "344": -0.047727395990334764, "459": 0.047727395990334764, "13": -0.047727395990334764, "276": 0.047727395990334764, "433": -0.23863697995167382, "427": 0.047727395990334764, "271": 0.047727395990334764, "201": -0.047727395990334764, "208": 0.047727395990334764, "475": -0.19090958396133906, "25": 0.047727395990334764, "313": 0.047727395990334764, "219": 0.047727395990334764, "55": 0.047727395990334764, "273": -0.047727395990334764, "112": -0.047727395990334764, "476": -0.047727395990334764, "439": 0.047727395990334764 } }, { "id": "run_benchmark.py#13", "path": "run_benchmark.py", "chunk": 13, "text": "gument(\"--profile\", choices=sorted(PROFILES), default=\"fast\") parser.add_argument(\"--dimensions\") parser.add_argument(\"--branches\") parser.add_argument(\"--distractor-similarity\") parser.add_argument(\"--target-corruption\") parser.add_argument(\"--seeds-per-condition\", type=int) parser.add_argument(\"--target-index\", type=int, default=0) parser.add_argument(\"--seed\", type=int, default=7) parser.add_argument(\"--out-dir\", type=Path, default=Path(\"results\")) parser.add_argument(\"--no-plots\", action=\"store_true\") parser.add_argument(\"--verbose\", action=\"store_true\") args = parser.parse_args() run(args) if __name__ == \"__main__\": main()", "tokens": 70, "vector": { "15": -0.05698028822981897, "25": 0.0, "313": 0.05698028822981897, "219": 0.05698028822981897, "469": 0.05698028822981897, "55": 0.2279211529192759, "273": -0.05698028822981897, "433": -0.6267831705280087, "475": -0.5698028822981898, "112": -0.05698028822981897, "476": -0.05698028822981897, "9": 0.05698028822981897, "140": 0.05698028822981897, "52": 0.11396057645963795, "225": 0.05698028822981897, "68": 0.05698028822981897, "436": -0.05698028822981897, "345": -0.05698028822981897, "275": -0.2279211529192759, "109": 0.1709408646894569, "27": -0.05698028822981897, "118": -0.05698028822981897, "43": -0.05698028822981897, "416": -0.11396057645963795, "448": -0.05698028822981897, "147": -0.05698028822981897, "406": -0.05698028822981897, "445": -0.11396057645963795, "99": -0.11396057645963795, "253": 0.05698028822981897, "265": 0.11396057645963795, "322": 0.05698028822981897, "172": -0.05698028822981897, "403": -0.05698028822981897, "34": -0.05698028822981897, "333": 0.05698028822981897, "13": -0.05698028822981897 } }, { "id": "run_hops_proof.py#0", "path": "run_hops_proof.py", "chunk": 0, "text": "\"\"\"Generate a local HOPS proof dashboard across multiple optimization domains.\"\"\" from __future__ import annotations import argparse import csv import itertools import json import math from pathlib import Path from typing import Dict, List, Sequence, Tuple import numpy as np from sat_annealer import AnnealerConfig, anneal_sat, brute_force_solutions, example_formula, format_assignment def write_csv(path: Path, rows: list[dict[str, object]]) -> None: if not rows: return fields = sorted({field for row in rows for field in row}) path.parent.mkdir(parents=True, exist_ok=True) with path.open(\"w\", newline=\"\", encoding=\"utf-8\") as handle: writer = csv.DictWriter(handle, fieldnames=fields) writer.writeheader() writer.writerows(rows) def write_json(path: Path, payload: object) -> None: path.parent.mkdir(parents=True, exist_ok=True) path.write_text(json.dumps(payload, indent=2), encoding=\"utf-8\") def portfolio_problem(seed: int = 7) -> Tuple[np.ndarray, np.ndarray, int]: rng = np.random.default_rng(seed) n = 20 k = 5 returns = rng.normal(0.08, 0.015, size=n) target = np.array([5, 10, 13, 14, 17]) returns[target] += np.array([0.10, 0.12, 0.11, 0.10, 0.13]) features = rng.normal(0.0, 0.05, size", "tokens": 162, "vector": { "36": 0.045360921162651446, "293": -0.09072184232530289, "152": 0.045360921162651446, "449": 0.045360921162651446, "358": 0.045360921162651446, "478": 0.045360921162651446, "63": 0.045360921162651446, "190": 0.045360921162651446, "27": 0.045360921162651446, "373": -0.18144368465060579, "201": -0.045360921162651446, "217": 0.45360921162651446, "297": 0.045360921162651446, "427": 0.045360921162651446, "339": 0.09072184232530289, "74": 0.18144368465060579, "258": 0.09072184232530289, "88": 0.045360921162651446, "64": 0.045360921162651446, "416": -0.408248290463863, "504": -0.045360921162651446, "106": -0.09072184232530289, "163": 0.09072184232530289, "447": 0.13608276348795434, "251": -0.045360921162651446, "371": 0.045360921162651446, "500": -0.09072184232530289, "266": -0.2721655269759087, "359": 0.045360921162651446, "143": 0.045360921162651446, "315": 0.045360921162651446, "57": 0.045360921162651446, "423": -0.045360921162651446, "421": -0.045360921162651446, "459": 0.13608276348795434, "401": -0.045360921162651446, "444": -0.18144368465060579, "396": 0.045360921162651446, "41": 0.09072184232530289, "276": 0.09072184232530289, "403": -0.045360921162651446, "369": 0.045360921162651446, "219": 0.13608276348795434, "192": 0.09072184232530289, "16": 0.09072184232530289, "355": -0.09072184232530289, "344": -0.09072184232530289, "484": -0.09072184232530289, "405": 0.09072184232530289, "108": -0.09072184232530289, "181": 0.18144368465060579, "495": 0.09072184232530289, "262": 0.045360921162651446, "35": 0.045360921162651446, "234": 0.045360921162651446, "232": -0.09072184232530289, "153": -0.09072184232530289, "450": -0.09072184232530289, "185": 0.13608276348795434, "141": 0.045360921162651446, "343": -0.045360921162651446, "424": 0.045360921162651446, "150": -0.045360921162651446, "291": -0.09072184232530289, "230": -0.045360921162651446, "336": 0.045360921162651446, "383": 0.045360921162651446, "197": 0.045360921162651446, "25": -0.09072184232530289, "109": 0.09072184232530289, "376": 0.09072184232530289, "177": 0.13608276348795434, "420": -0.045360921162651446, "279": -0.045360921162651446, "245": 0.045360921162651446, "277": -0.09072184232530289, "305": -0.09072184232530289, "306": 0.045360921162651446, "45": 0.045360921162651446, "204": -0.09072184232530289, "52": 0.09072184232530289, "319": 0.09072184232530289, "389": 0.045360921162651446, "325": -0.045360921162651446, "363": -0.045360921162651446, "79": 0.045360921162651446, "91": 0.045360921162651446, "457": 0.045360921162651446 } }, { "id": "run_hops_proof.py#1", "path": "run_hops_proof.py", "chunk": 1, "text": "al(0.08, 0.015, size=n) target = np.array([5, 10, 13, 14, 17]) returns[target] += np.array([0.10, 0.12, 0.11, 0.10, 0.13]) features = rng.normal(0.0, 0.05, size=(n, 3)) covariance = features @ features.T covariance += np.eye(n) * 0.04 return returns, covariance, k def portfolio_score(selection: Sequence[int], returns: np.ndarray, covariance: np.ndarray, risk: float) -> float: p = np.zeros(len(returns), dtype=np.float64) p[list(selection)] = 1.0 return float(returns @ p - risk * (p @ covariance @ p)) def brute_force_portfolio(returns: np.ndarray, covariance: np.ndarray, k: int, risk: float) -> Tuple[List[int], float]: best_selection: List[int] = [] best_score = -math.inf for selection in itertools.combinations(range(len(returns)), k): score = portfolio_score(selection, returns, covariance, risk) if score > best_score: best_score = score best_selection = list(selection) return best_selection, best_score def anneal_portfolio( returns: np.ndarray, covariance: np.ndarray, k: int, seed: int = 7, restarts: int = 64, iterations: int = 900, ) -> Tuple[List[int], float, np.ndarray]: rng = np.random.default_rng(seed) risk = 0.55 penalty = 2.5 binary = 0.02 best_selection: List[int] = [] best_", "tokens": 146, "vector": { "355": 0.036760731104690386, "306": 0.036760731104690386, "45": 0.036760731104690386, "204": -0.07352146220938077, "52": 0.07352146220938077, "266": -0.477889504360975, "219": 0.07352146220938077, "74": 0.14704292441876154, "319": 0.07352146220938077, "389": 0.036760731104690386, "325": -0.036760731104690386, "277": -0.33084657994221345, "363": -0.036760731104690386, "79": 0.036760731104690386, "91": 0.11028219331407116, "177": 0.07352146220938077, "305": -0.036760731104690386, "457": 0.036760731104690386, "40": -0.2940858488375231, "71": -0.036760731104690386, "364": -0.036760731104690386, "369": 0.11028219331407116, "459": 0.11028219331407116, "261": -0.07352146220938077, "101": 0.18380365552345193, "447": 0.036760731104690386, "109": 0.36760731104690386, "376": 0.2573251177328327, "44": 0.18380365552345193, "374": 0.22056438662814232, "46": 0.036760731104690386, "113": 0.07352146220938077, "182": -0.036760731104690386, "107": 0.036760731104690386, "163": 0.22056438662814232, "281": 0.036760731104690386, "251": -0.07352146220938077, "296": -0.14704292441876154, "63": 0.14704292441876154, "88": 0.036760731104690386, "233": 0.036760731104690386, "16": 0.036760731104690386, "344": -0.036760731104690386, "317": -0.036760731104690386, "290": 0.036760731104690386, "421": 0.11028219331407116, "403": -0.036760731104690386, "175": 0.036760731104690386, "25": -0.07352146220938077, "270": 0.036760731104690386, "422": 0.036760731104690386, "298": 0.036760731104690386, "445": 0.036760731104690386, "420": -0.036760731104690386, "279": -0.036760731104690386, "2": 0.036760731104690386, "478": -0.036760731104690386, "154": -0.036760731104690386, "425": -0.036760731104690386, "179": -0.036760731104690386 } }, { "id": "run_hops_proof.py#2", "path": "run_hops_proof.py", "chunk": 2, "text": " = 900, ) -> Tuple[List[int], float, np.ndarray]: rng = np.random.default_rng(seed) risk = 0.55 penalty = 2.5 binary = 0.02 best_selection: List[int] = [] best_score = -math.inf best_p = np.zeros(len(returns), dtype=np.float64) for _ in range(restarts): p = rng.uniform(0.0, 1.0, size=len(returns)) velocity = np.zeros_like(p) noise = 0.015 for _step in range(iterations): grad = -returns + 2.0 * risk * (covariance @ p) grad += 2.0 * penalty * (np.sum(p) - k) grad += binary * (1.0 - 2.0 * p) velocity = 0.88 * velocity - 0.045 * grad + rng.normal(0.0, noise, size=len(p)) p = np.clip(p + velocity, 0.0, 1.0) noise *= 0.992 selection = sorted(np.argsort(p)[-k:].tolist()) score = portfolio_score(selection, returns, covariance, risk) if score > best_score: best_selection = selection best_score = score best_p = p.copy() return best_selection, best_score, best_p def routing_problem() -> Tuple[np.ndarray, np.ndarray]: cities = np.array( [ [0.8, 1.1], [1.2, 0.7], [0.5, 1.4], [8.8, 1.2], [9.4, 0.8], [8.5, 1.7], [4.8, 8.8], [5.4, 9.1], [4.4, 9.5], ], dtype=np.float64, ) depots = np.array([[1.0, 1.0], [9.0, 1.0], [5.0, 9.0]], dtype=np.float64) return cities, depots def anneal_routing(cities: np.nd", "tokens": 126, "vector": { "445": 0.045267873021259265, "251": -0.09053574604251853, "163": 0.09053574604251853, "109": 0.09053574604251853, "374": 0.045267873021259265, "266": -0.6790180953188889, "376": 0.1358036190637778, "177": 0.1358036190637778, "420": -0.045267873021259265, "279": -0.045267873021259265, "25": -0.045267873021259265, "44": 0.1358036190637778, "2": 0.045267873021259265, "478": -0.09053574604251853, "154": -0.09053574604251853, "425": -0.045267873021259265, "296": -0.1358036190637778, "63": 0.18107149208503706, "88": 0.045267873021259265, "233": 0.045267873021259265, "327": 0.1358036190637778, "46": 0.045267873021259265, "113": 0.1358036190637778, "277": -0.18107149208503706, "182": -0.1358036190637778, "107": 0.1358036190637778, "16": 0.09053574604251853, "344": -0.09053574604251853, "290": 0.09053574604251853, "270": 0.045267873021259265, "495": -0.045267873021259265, "204": -0.09053574604251853, "125": 0.18107149208503706, "333": -0.045267873021259265, "239": 0.1358036190637778, "45": 0.045267873021259265, "407": 0.045267873021259265, "298": 0.045267873021259265, "400": 0.18107149208503706, "40": -0.09053574604251853, "265": 0.045267873021259265, "139": -0.045267873021259265, "43": 0.045267873021259265, "305": -0.045267873021259265, "103": 0.045267873021259265, "192": 0.045267873021259265, "101": 0.1358036190637778, "219": 0.1358036190637778, "338": -0.045267873021259265, "158": -0.045267873021259265, "421": 0.1358036190637778, "261": -0.045267873021259265, "403": -0.045267873021259265, "111": -0.045267873021259265, "369": 0.09053574604251853, "459": 0.09053574604251853, "218": 0.045267873021259265, "83": 0.1358036190637778, "249": 0.09053574604251853, "128": -0.045267873021259265, "5": -0.045267873021259265 } }, { "id": "run_hops_proof.py#3", "path": "run_hops_proof.py", "chunk": 3, "text": "4, 9.5], ], dtype=np.float64, ) depots = np.array([[1.0, 1.0], [9.0, 1.0], [5.0, 9.0]], dtype=np.float64) return cities, depots def anneal_routing(cities: np.ndarray, depots: np.ndarray, seed: int = 7) -> Tuple[List[int], float, np.ndarray]: rng = np.random.default_rng(seed) distances = np.linalg.norm(cities[:, None, :] - depots[None, :, :], axis=2) p = rng.uniform(0.0, 1.0, size=distances.shape) velocity = np.zeros_like(p) penalty = 8.0 noise = 0.02 for _ in range(600): row_error = np.sum(p, axis=1, keepdims=True) - 1.0 grad = distances + 2.0 * penalty * row_error velocity = 0.85 * velocity - 0.055 * grad + rng.normal(0.0, noise, size=p.shape) p = np.clip(p + velocity, 0.0, 1.0) noise *= 0.99 assignment = np.argmax(p, axis=1).tolist() cost = float(sum(distances[city, fleet] for city, fleet in enumerate(assignment))) return assignment, cost, distances def exact_routing(cities: np.ndarray, depots: np.ndarray) -> Tuple[List[int], float]: distances = np.linalg.norm(cities[:, None, :] - depots[None, :, :], axis=2) assignment = np.argmin(distances, axis=1).tolist() cost = float(sum(distances[city, fleet] for city, fleet in enumerate(assignment))) return assignment, cost def hp_coordinat", "tokens": 147, "vector": { "182": -0.07500586006173493, "266": -0.6000468804938794, "107": 0.07500586006173493, "249": 0.2250175801852048, "219": 0.037502930030867465, "369": 0.1125087900926024, "83": 0.18751465015433733, "459": 0.1125087900926024, "128": -0.037502930030867465, "376": 0.18751465015433733, "25": -0.07500586006173493, "109": 0.1125087900926024, "251": -0.07500586006173493, "163": 0.07500586006173493, "374": 0.15001172012346986, "177": 0.1125087900926024, "420": -0.037502930030867465, "279": -0.037502930030867465, "294": -0.3000234402469397, "458": -0.07500586006173493, "115": 0.07500586006173493, "276": 0.15001172012346986, "211": -0.18751465015433733, "495": -0.037502930030867465, "204": -0.07500586006173493, "501": -0.07500586006173493, "125": 0.15001172012346986, "333": -0.037502930030867465, "478": -0.07500586006173493, "239": 0.1125087900926024, "425": -0.037502930030867465, "16": 0.1125087900926024, "344": -0.1125087900926024, "290": 0.037502930030867465, "296": 0.037502930030867465, "401": -0.07500586006173493, "265": 0.1125087900926024, "217": -0.037502930030867465, "181": 0.037502930030867465, "400": 0.07500586006173493, "180": 0.037502930030867465, "365": -0.037502930030867465, "305": -0.037502930030867465, "103": 0.037502930030867465, "396": -0.037502930030867465, "206": 0.2250175801852048, "370": 0.037502930030867465, "158": -0.07500586006173493, "159": -0.15001172012346986, "17": -0.15001172012346986, "94": 0.15001172012346986, "148": -0.07500586006173493, "300": 0.037502930030867465, "461": 0.037502930030867465, "37": -0.037502930030867465 } }, { "id": "run_hops_proof.py#4", "path": "run_hops_proof.py", "chunk": 4, "text": "p.argmin(distances, axis=1).tolist() cost = float(sum(distances[city, fleet] for city, fleet in enumerate(assignment))) return assignment, cost def hp_coordinates(angles: np.ndarray) -> np.ndarray: coords = [np.array([0.0, 0.0]), np.array([1.0, 0.0])] heading = 0.0 for angle in angles: heading += angle coords.append(coords[-1] + np.array([math.cos(heading), math.sin(heading)])) return np.array(coords) def hp_energy(sequence: str, angles: np.ndarray) -> float: coords = hp_coordinates(angles) energy = 0.0 for i in range(len(sequence)): for j in range(i + 2, len(sequence)): distance = float(np.linalg.norm(coords[i] - coords[j])) distance = max(distance, 0.25) attraction = 1.4 if sequence[i] == \"H\" and sequence[j] == \"H\" else 0.25 energy += (1.0 / distance**3) - (attraction / distance**1.5) return float(energy) def anneal_hp(sequence: str, seed: int = 7) -> Tuple[float, float, np.ndarray]: rng = np.random.default_rng(seed) dims = len(sequence) - 2 angles = rng.uniform(-0.4, 0.4, size=dims) start_energy = hp_energy(sequence, angles) best_angles = angles.copy() best_energy = start_energy temperature = 0.08 eps = 1e-4 for _ in range(700): gradient = np.zeros_like(angles) base = hp_energy(", "tokens": 147, "vector": { "461": 0.04025848411142373, "294": -0.08051696822284746, "211": -0.04025848411142373, "158": -0.04025848411142373, "159": -0.08051696822284746, "374": 0.24155090466854237, "265": 0.04025848411142373, "17": -0.08051696822284746, "94": 0.24155090466854237, "16": 0.20129242055711863, "344": -0.20129242055711863, "148": -0.04025848411142373, "206": 0.08051696822284746, "369": 0.12077545233427119, "459": 0.12077545233427119, "34": -0.08051696822284746, "110": -0.32206787289138983, "266": -0.442843325225661, "376": 0.16103393644569491, "214": 0.2818093887799661, "219": 0.16103393644569491, "73": 0.08051696822284746, "174": -0.04025848411142373, "88": 0.08051696822284746, "155": -0.04025848411142373, "204": 0.0, "121": -0.12077545233427119, "447": 0.32206787289138983, "396": 0.08051696822284746, "56": 0.12077545233427119, "290": 0.12077545233427119, "113": 0.12077545233427119, "99": -0.20129242055711863, "458": -0.04025848411142373, "115": 0.04025848411142373, "411": -0.04025848411142373, "439": -0.08051696822284746, "33": 0.08051696822284746, "403": -0.04025848411142373, "354": -0.04025848411142373, "381": -0.04025848411142373, "472": -0.04025848411142373, "25": -0.08051696822284746, "109": 0.04025848411142373, "251": -0.04025848411142373, "177": 0.08051696822284746, "420": -0.04025848411142373, "279": -0.04025848411142373, "70": 0.08051696822284746, "495": -0.04025848411142373, "466": 0.08051696822284746, "196": 0.04025848411142373, "111": -0.04025848411142373, "380": -0.04025848411142373, "179": -0.04025848411142373, "306": 0.04025848411142373, "455": -0.04025848411142373, "424": 0.04025848411142373, "153": 0.04025848411142373, "27": -0.04025848411142373, "333": -0.04025848411142373, "202": -0.04025848411142373 } }, { "id": "run_hops_proof.py#5", "path": "run_hops_proof.py", "chunk": 5, "text": "es) best_angles = angles.copy() best_energy = start_energy temperature = 0.08 eps = 1e-4 for _ in range(700): gradient = np.zeros_like(angles) base = hp_energy(sequence, angles) for idx in range(dims): shifted = angles.copy() shifted[idx] += eps gradient[idx] = (hp_energy(sequence, shifted) - base) / eps proposal = angles - 0.025 * gradient + rng.normal(0.0, temperature, size=dims) proposal = np.clip(proposal, -math.pi, math.pi) proposal_energy = hp_energy(sequence, proposal) if proposal_energy < base or rng.random() < math.exp((base - proposal_energy) / max(temperature, 1e-6)): angles = proposal base = proposal_energy if base < best_energy: best_energy = base best_angles = angles.copy() temperature *= 0.992 return start_energy, float(best_energy), best_angles def run_proof(seed: int) -> Tuple[List[dict[str, object]], Dict[str, object]]: rows: List[dict[str, object]] = [] sat_formula = example_formula() sat_result = anneal_sat(sat_formula, AnnealerConfig(variables=3, seed=seed)) sat_solutions = brute_force_solutions(3, sat_formula) rows.append( { \"domain\": \"SAT\", \"problem\": \"3-variable clause system\", \"hops_solution\": format_assignment(sat_result.assignment), \"baseline\": f\"{len(sat", "tokens": 137, "vector": { "192": 0.0, "196": 0.15289415743128767, "110": -0.3567530340063379, "111": -0.15289415743128767, "380": -0.20385887657505022, "466": 0.10192943828752511, "179": -0.20385887657505022, "306": 0.050964719143762556, "455": -0.15289415743128767, "424": 0.10192943828752511, "16": 0.10192943828752511, "344": -0.10192943828752511, "290": 0.10192943828752511, "153": 0.050964719143762556, "27": -0.15289415743128767, "266": -0.10192943828752511, "333": -0.050964719143762556, "202": -0.3567530340063379, "121": -0.15289415743128767, "447": 0.15289415743128767, "198": 0.15289415743128767, "70": 0.10192943828752511, "302": 0.15289415743128767, "492": -0.2548235957188128, "79": 0.050964719143762556, "177": 0.10192943828752511, "305": -0.050964719143762556, "204": -0.050964719143762556, "103": 0.050964719143762556, "88": 0.15289415743128767, "133": 0.10192943828752511, "258": -0.20385887657505022, "403": -0.10192943828752511, "369": 0.0, "420": -0.050964719143762556, "221": 0.050964719143762556, "411": -0.050964719143762556, "374": 0.050964719143762556, "459": 0.050964719143762556, "156": 0.050964719143762556, "25": -0.15289415743128767, "109": 0.050964719143762556, "251": -0.050964719143762556, "163": 0.10192943828752511, "106": -0.15289415743128767, "396": 0.15289415743128767, "41": 0.15289415743128767, "444": -0.10192943828752511, "285": -0.15289415743128767, "423": -0.050964719143762556, "125": 0.10192943828752511, "315": 0.050964719143762556, "143": 0.050964719143762556, "122": 0.050964719143762556, "78": -0.050964719143762556, "57": 0.050964719143762556, "174": -0.050964719143762556, "498": 0.050964719143762556, "51": 0.10192943828752511, "71": 0.050964719143762556, "59": 0.050964719143762556, "157": 0.050964719143762556, "443": -0.050964719143762556, "505": 0.050964719143762556, "421": -0.050964719143762556, "206": 0.050964719143762556, "139": -0.050964719143762556, "113": 0.050964719143762556 } }, { "id": "run_hops_proof.py#6", "path": "run_hops_proof.py", "chunk": 6, "text": "ormula) rows.append( { \"domain\": \"SAT\", \"problem\": \"3-variable clause system\", \"hops_solution\": format_assignment(sat_result.assignment), \"baseline\": f\"{len(sat_solutions)} exact brute-force solution\", \"metric\": f\"{sat_result.satisfied_clauses}/{sat_result.total_clauses} clauses\", \"proved\": sat_result.success and sat_result.assignment in sat_solutions, \"note\": \"Validated against exhaustive enumeration.\", } ) returns, covariance, k = portfolio_problem(seed) risk = 0.55 exact_selection, exact_score = brute_force_portfolio(returns, covariance, k, risk) hops_selection, hops_score, probabilities = anneal_portfolio(returns, covariance, k, seed) rows.append( { \"domain\": \"Portfolio\", \"problem\": \"20 assets choose exactly 5\", \"hops_solution\": str(hops_selection), \"baseline\": f\"exact brute force {exact_selection}\", \"metric\": f\"HOPS score {hops_score:.4f} vs exact {exact_score:.4f}\", \"proved\": hops_selection == exact_selection, \"note\": \"Exact baseline searched all C(20,5)=15504 portfolios.\", } ) cities, depots = routing_problem() exact_assignment, exact_cost = exact_routing(cities, depots) hops_assignment, hops_cost, distances = anneal_routing(cities, depots, seed) rows.append( { \"domain\": \"Ro", "tokens": 114, "vector": { "196": -0.0629940788348712, "444": -0.1889822365046136, "174": -0.2519763153394848, "498": 0.1889822365046136, "51": 0.0629940788348712, "71": 0.1259881576697424, "59": 0.0629940788348712, "157": 0.2519763153394848, "443": -0.0629940788348712, "505": 0.1259881576697424, "421": 0.0, "125": 0.314970394174356, "206": 0.1259881576697424, "139": -0.1889822365046136, "113": 0.0629940788348712, "78": -0.1259881576697424, "506": 0.2519763153394848, "12": -0.1259881576697424, "50": 0.1259881576697424, "130": -0.1259881576697424, "184": -0.1259881576697424, "167": 0.1259881576697424, "141": -0.0629940788348712, "429": 0.1259881576697424, "354": -0.0629940788348712, "344": -0.0629940788348712, "237": 0.1259881576697424, "227": -0.0629940788348712, "118": 0.0629940788348712, "324": -0.0629940788348712, "277": -0.1889822365046136, "40": -0.1889822365046136, "197": 0.0629940788348712, "25": -0.1889822365046136, "44": 0.1259881576697424, "2": 0.0629940788348712, "442": -0.1889822365046136, "281": 0.0629940788348712, "315": -0.1259881576697424, "508": -0.0629940788348712, "175": 0.0629940788348712, "14": 0.0629940788348712, "245": 0.1259881576697424, "395": 0.0629940788348712, "370": 0.1259881576697424, "36": -0.0629940788348712, "396": 0.0629940788348712, "152": 0.0629940788348712, "361": -0.1259881576697424, "241": 0.0629940788348712, "350": 0.0629940788348712, "232": -0.0629940788348712, "106": 0.0629940788348712, "83": 0.1889822365046136, "249": 0.1889822365046136, "218": 0.0629940788348712, "283": -0.0629940788348712, "205": -0.0629940788348712, "300": 0.0629940788348712, "308": -0.0629940788348712, "198": -0.0629940788348712, "294": -0.0629940788348712, "128": -0.0629940788348712, "382": 0.0629940788348712 } }, { "id": "run_hops_proof.py#7", "path": "run_hops_proof.py", "chunk": 7, "text": "assignment, exact_cost = exact_routing(cities, depots) hops_assignment, hops_cost, distances = anneal_routing(cities, depots, seed) rows.append( { \"domain\": \"Routing\", \"problem\": \"9 cities assigned to 3 fleets\", \"hops_solution\": str(hops_assignment), \"baseline\": f\"nearest-depot exact {exact_assignment}\", \"metric\": f\"HOPS cost {hops_cost:.4f} vs exact {exact_cost:.4f}\", \"proved\": hops_assignment == exact_assignment, \"note\": \"This benchmark validates assignment constraints, not route ordering.\", } ) sequence = \"HPPHHPHPPH\" hp_start, hp_final, hp_angles = anneal_hp(sequence, seed) rows.append( { \"domain\": \"HP Folding\", \"problem\": f\"2D HP chain {sequence}\", \"hops_solution\": f\"{len(hp_angles)} continuous fold angles\", \"baseline\": \"initial random conformation\", \"metric\": f\"energy {hp_start:.4f} -> {hp_final:.4f}\", \"proved\": hp_final < hp_start, \"note\": \"Energy descent demonstration; no global optimum guarantee claimed.\", } ) payload = { \"portfolio_probabilities\": probabilities.tolist(), \"routing_distances\": distances.tolist(), \"hp_angles\": hp_angles.tolist(), \"all_proved\": all(bool(row[\"proved\"]) for row in rows), } return rows, payload def render_dashboard(rows: list[dict[str, object]],", "tokens": 125, "vector": { "206": 0.19405386820594528, "205": -0.12936924547063017, "300": 0.06468462273531508, "83": 0.19405386820594528, "249": 0.12936924547063017, "308": -0.19405386820594528, "198": -0.12936924547063017, "294": -0.12936924547063017, "128": -0.06468462273531508, "25": -0.12936924547063017, "444": -0.32342311367657545, "174": -0.12936924547063017, "498": 0.12936924547063017, "252": 0.06468462273531508, "71": 0.12936924547063017, "302": 0.06468462273531508, "102": -0.06468462273531508, "14": -0.06468462273531508, "505": -0.12936924547063017, "396": 0.12936924547063017, "139": -0.12936924547063017, "330": 0.06468462273531508, "186": 0.06468462273531508, "506": 0.12936924547063017, "283": -0.12936924547063017, "184": -0.12936924547063017, "152": 0.06468462273531508, "159": -0.06468462273531508, "361": -0.25873849094126034, "241": 0.06468462273531508, "429": 0.19405386820594528, "237": 0.12936924547063017, "286": -0.06468462273531508, "270": 0.06468462273531508, "482": 0.06468462273531508, "293": 0.0, "138": 0.06468462273531508, "494": 0.06468462273531508, "447": 0.19405386820594528, "311": -0.06468462273531508, "212": -0.19405386820594528, "82": 0.19405386820594528, "472": -0.06468462273531508, "147": -0.19405386820594528, "87": 0.06468462273531508, "504": -0.06468462273531508, "148": 0.06468462273531508, "113": 0.06468462273531508, "503": -0.06468462273531508, "110": -0.06468462273531508, "428": -0.06468462273531508, "420": -0.06468462273531508, "89": 0.06468462273531508, "56": 0.12936924547063017, "190": 0.06468462273531508, "105": 0.0, "384": -0.06468462273531508, "96": 0.06468462273531508, "327": 0.06468462273531508, "471": -0.06468462273531508, "291": -0.12936924547063017, "508": -0.06468462273531508, "158": -0.19405386820594528, "309": -0.06468462273531508, "382": -0.06468462273531508, "350": 0.06468462273531508, "183": -0.06468462273531508, "355": -0.12936924547063017, "16": 0.06468462273531508, "344": -0.06468462273531508, "369": 0.06468462273531508, "459": 0.06468462273531508, "432": 0.06468462273531508, "163": 0.06468462273531508, "106": -0.06468462273531508, "41": 0.06468462273531508 } }, { "id": "run_hops_proof.py#8", "path": "run_hops_proof.py", "chunk": 8, "text": "_angles\": hp_angles.tolist(), \"all_proved\": all(bool(row[\"proved\"]) for row in rows), } return rows, payload def render_dashboard(rows: list[dict[str, object]], payload: Dict[str, object], path: Path) -> None: cards = [] for row in rows: status = \"PASS\" if row[\"proved\"] else \"CHECK\" cards.append( f\"\"\"
{status}

{row['domain']}

{row['problem']}

HOPS solution
{row['hops_solution']}
Baseline
{row['baseline']}
Metric
{row['metric']}

{row['note']}

\"\"\" ) content = f\"\"\" HOPS Proof Dashboard

HOPS Proof Dashboard

One continuous optimization pattern is exercised across SAT, portfolio selection, fleet assignment, and HP folding. The dashboard reports reproducible benchmark evidence with exact baselines where this toy suite makes exact validation tractable.

{''.join(cards)}

Careful claim: this proves the local benchmark suite ran and validated successfully. It does not prove that NP-hardness is eliminated or that arbitrary instances are solved in constant time. Use hops_results.csv and hops_results.json for audit.

\"\"\" path.parent.mkdir(parents=True, exist_ok=True) path.write_text(content, encoding=\"utf-8\") def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--seed\", type=int, default=7) parser.add_argument(\"--out-dir\", type=Path, default=Path(\"results/hops\")) args = parser.parse_args() rows, payload = run_proof(args.seed) write_csv(args.out_dir / \"hops_results.csv\", rows) write_json(args.out_dir / \"hops_results.json\", {\"rows\": rows, \"payload\": payload}) render_dashboard(rows, payload, args.out_dir / \"site\" / \"index.html\") passed = sum(1 for row in rows if row[\"proved\"]) print(f\"HOPS proof cases passed: {passed}/{len(rows)}\") print(f\"wrote {args.out_dir / '", "tokens": 159, "vector": { "87": 0.051639777949432225, "441": -0.10327955589886445, "358": 0.051639777949432225, "127": -0.051639777949432225, "9": 0.051639777949432225, "270": 0.10327955589886445, "238": 0.051639777949432225, "262": 0.051639777949432225, "506": 0.051639777949432225, "436": 0.10327955589886445, "286": -0.10327955589886445, "271": 0.0, "504": 0.10327955589886445, "495": 0.0, "152": 0.15491933384829668, "47": -0.051639777949432225, "461": 0.10327955589886445, "264": 0.10327955589886445, "15": -0.051639777949432225, "344": -0.15491933384829668, "362": 0.051639777949432225, "237": 0.0, "25": -0.15491933384829668, "477": 0.051639777949432225, "280": -0.051639777949432225, "293": -0.051639777949432225, "200": 0.051639777949432225, "354": -0.10327955589886445, "227": -0.051639777949432225, "183": -0.051639777949432225, "42": -0.051639777949432225, "150": -0.10327955589886445, "82": 0.051639777949432225, "398": 0.10327955589886445, "266": -0.051639777949432225, "429": 0.10327955589886445, "369": -0.051639777949432225, "135": -0.051639777949432225, "32": -0.051639777949432225, "186": 0.051639777949432225, "458": -0.051639777949432225, "51": -0.051639777949432225, "419": 0.051639777949432225, "86": 0.2065591117977289, "128": -0.2065591117977289, "339": 0.10327955589886445, "258": 0.10327955589886445, "16": 0.10327955589886445, "440": -0.051639777949432225, "13": -0.10327955589886445, "291": -0.15491933384829668, "229": -0.10327955589886445, "416": -0.2065591117977289, "484": -0.051639777949432225, "405": 0.051639777949432225, "108": -0.051639777949432225, "181": 0.10327955589886445, "230": -0.051639777949432225, "232": -0.051639777949432225, "153": -0.25819888974716115, "459": 0.051639777949432225, "276": 0.051639777949432225, "433": -0.2065591117977289, "427": 0.051639777949432225, "201": -0.051639777949432225, "208": 0.051639777949432225, "475": -0.10327955589886445, "275": -0.10327955589886445, "109": 0.051639777949432225, "55": 0.10327955589886445, "118": -0.051639777949432225, "43": -0.051639777949432225, "448": -0.051639777949432225, "265": 0.3614784456460256, "322": 0.051639777949432225, "444": -0.3614784456460256, "156": 0.051639777949432225, "401": -0.051639777949432225, "251": 0.10327955589886445, "432": 0.051639777949432225, "27": -0.051639777949432225, "296": -0.15491933384829668, "355": -0.10327955589886445, "403": -0.051639777949432225, "462": 0.10327955589886445, "449": 0.051639777949432225, "309": -0.051639777949432225, "113": 0.051639777949432225, "346": -0.051639777949432225 } }, { "id": "run_hops_proof.py#11", "path": "run_hops_proof.py", "chunk": 11, "text": " \"site\" / \"index.html\") passed = sum(1 for row in rows if row[\"proved\"]) print(f\"HOPS proof cases passed: {passed}/{len(rows)}\") print(f\"wrote {args.out_dir / 'hops_results.csv'}\") print(f\"wrote {args.out_dir / 'hops_results.json'}\") print(f\"wrote {args.out_dir / 'site' / 'index.html'}\") if __name__ == \"__main__\": main()", "tokens": 43, "vector": { "251": 0.20100756305184242, "27": -0.20100756305184242, "229": -0.20100756305184242, "296": -0.30151134457776363, "265": 0.40201512610368484, "16": 0.10050378152592121, "355": -0.20100756305184242, "344": -0.10050378152592121, "444": -0.20100756305184242, "403": -0.20100756305184242, "429": 0.10050378152592121, "462": 0.40201512610368484, "152": 0.10050378152592121, "449": 0.10050378152592121, "309": -0.10050378152592121, "113": 0.10050378152592121, "346": -0.30151134457776363, "153": -0.30151134457776363, "128": -0.20100756305184242, "339": 0.10050378152592121, "258": 0.10050378152592121, "34": -0.10050378152592121, "333": 0.10050378152592121, "13": -0.10050378152592121 } }, { "id": "run_memory_fabric.py#0", "path": "run_memory_fabric.py", "chunk": 0, "text": "\"\"\"Run the unified memory-native fabric proof harness.\"\"\" from __future__ import annotations import argparse import csv import json from pathlib import Path from memory_fabric import ( ComplexSpace, FractalMemoryPyramid, MemoryRecord, Node, ProvenanceHMK, ResonantExecutionMesh, ) def write_csv(path: Path, rows: list[dict[str, object]]) -> None: if not rows: return fields = sorted({field for row in rows for field in row}) path.parent.mkdir(parents=True, exist_ok=True) with path.open(\"w\", newline=\"\", encoding=\"utf-8\") as handle: writer = csv.DictWriter(handle, fieldnames=fields) writer.writeheader() writer.writerows(rows) def write_json(path: Path, payload: object) -> None: path.parent.mkdir(parents=True, exist_ok=True) path.write_text(json.dumps(payload, indent=2), encoding=\"utf-8\") def run_memory(space: ComplexSpace) -> list[dict[str, object]]: pyramid = FractalMemoryPyramid(space, tier0_capacity=2, tier1_capacity=2, tier2_capacity=2, threshold=0.18) records = [ MemoryRecord(\"contract_owner\", \"northwind\", \"user\", \"high\", \"editor\"), MemoryRecord(\"policy_limit\", \"250000\", \"user\", \"high\", \"calculator\"), MemoryRecord(\"preferred_region\", \"midwest\", \"user\", \"high\", \"crm\"), MemoryRecord(\"", "tokens": 140, "vector": { "172": -0.05314940034527339, "441": -0.05314940034527339, "9": 0.05314940034527339, "192": 0.05314940034527339, "446": -0.05314940034527339, "173": 0.05314940034527339, "449": 0.05314940034527339, "329": 0.05314940034527339, "373": -0.15944820103582016, "201": -0.05314940034527339, "217": 0.31889640207164033, "297": 0.05314940034527339, "427": 0.05314940034527339, "339": 0.10629880069054679, "258": 0.10629880069054679, "64": 0.05314940034527339, "416": -0.4783446031074605, "437": -0.05314940034527339, "355": 0.0, "356": 0.10629880069054679, "485": 0.26574700172636695, "84": 0.05314940034527339, "252": 0.05314940034527339, "471": -0.05314940034527339, "459": 0.15944820103582016, "401": -0.05314940034527339, "444": -0.21259760138109357, "163": 0.10629880069054679, "106": -0.10629880069054679, "396": 0.10629880069054679, "41": 0.15944820103582016, "276": 0.10629880069054679, "403": -0.05314940034527339, "293": -0.05314940034527339, "369": 0.05314940034527339, "447": 0.10629880069054679, "219": 0.05314940034527339, "16": 0.10629880069054679, "344": -0.10629880069054679, "484": -0.10629880069054679, "405": 0.10629880069054679, "108": -0.10629880069054679, "181": 0.21259760138109357, "495": 0.10629880069054679, "262": 0.05314940034527339, "35": 0.05314940034527339, "234": 0.05314940034527339, "232": -0.10629880069054679, "153": -0.10629880069054679, "500": -0.05314940034527339, "450": -0.10629880069054679, "185": 0.15944820103582016, "141": 0.05314940034527339, "343": -0.05314940034527339, "424": 0.05314940034527339, "150": -0.05314940034527339, "251": 0.05314940034527339, "291": -0.10629880069054679, "230": -0.05314940034527339, "336": 0.05314940034527339, "383": 0.05314940034527339, "89": -0.05314940034527339, "319": 0.10629880069054679, "34": 0.05314940034527339, "243": -0.05314940034527339, "398": -0.05314940034527339, "237": 0.05314940034527339, "73": -0.05314940034527339, "334": 0.05314940034527339, "169": -0.05314940034527339, "1": -0.05314940034527339, "132": -0.05314940034527339, "4": -0.15944820103582016, "366": 0.15944820103582016, "277": -0.05314940034527339, "370": 0.05314940034527339, "501": 0.05314940034527339, "210": 0.05314940034527339, "434": -0.05314940034527339, "385": -0.05314940034527339, "402": -0.05314940034527339 } }, { "id": "run_memory_fabric.py#1", "path": "run_memory_fabric.py", "chunk": 1, "text": "itor\"), MemoryRecord(\"policy_limit\", \"250000\", \"user\", \"high\", \"calculator\"), MemoryRecord(\"preferred_region\", \"midwest\", \"user\", \"high\", \"crm\"), MemoryRecord(\"carrier_mode\", \"rail\", \"agent\", \"medium\", \"planner\"), MemoryRecord(\"risk_model\", \"covariance_v3\", \"user\", \"high\", \"optimizer\"), MemoryRecord(\"delivery_sla\", \"36h\", \"dispatcher\", \"high\", \"router\"), MemoryRecord(\"gpu_budget\", \"edge\", \"ops\", \"medium\", \"scheduler\"), MemoryRecord(\"folding_model\", \"hp2d\", \"researcher\", \"high\", \"annealer\"), ] for record in records: pyramid.add(record) queries = { \"policy_limit\": \"250000\", \"preferred_region\": \"midwest\", \"risk_model\": \"covariance_v3\", \"folding_model\": \"hp2d\", } rows = [] for query, expected in queries.items(): result = pyramid.retrieve(query) row = result.as_dict() row[\"domain\"] = \"fractal_memory_pyramid\" row[\"expected\"] = expected row[\"success\"] = result.success and result.value == expected rows.append(row) return rows def run_provenance(space: ComplexSpace) -> list[dict[str, object]]: hmk = ProvenanceHMK(space) trusted = hmk.encode(\"set_policy_limit\", \"user\", \"high\", \"editor\") untrusted = hmk.encode(\"rewrite_policy_limit\", \"agent\", \"low\", \"autofill\") rows = [] for label, cv, actor,", "tokens": 122, "vector": { "455": 0.05698028822981897, "485": 0.3988620176087328, "370": 0.11396057645963795, "501": 0.11396057645963795, "4": -0.2279211529192759, "366": 0.3418817293789138, "210": 0.05698028822981897, "434": -0.11396057645963795, "385": -0.11396057645963795, "402": -0.05698028822981897, "291": 0.0, "253": 0.05698028822981897, "212": -0.11396057645963795, "192": -0.11396057645963795, "292": 0.05698028822981897, "397": 0.11396057645963795, "488": -0.11396057645963795, "491": -0.05698028822981897, "438": 0.05698028822981897, "511": -0.05698028822981897, "372": 0.05698028822981897, "254": 0.05698028822981897, "417": -0.05698028822981897, "169": 0.05698028822981897, "416": -0.05698028822981897, "65": -0.11396057645963795, "345": 0.11396057645963795, "53": -0.05698028822981897, "103": 0.05698028822981897, "16": 0.1709408646894569, "112": 0.11396057645963795, "344": -0.11396057645963795, "34": 0.11396057645963795, "126": -0.05698028822981897, "263": 0.11396057645963795, "444": -0.2279211529192759, "424": -0.11396057645963795, "206": 0.2279211529192759, "95": 0.05698028822981897, "246": -0.2279211529192759, "80": -0.05698028822981897, "355": -0.2849014411490949, "151": 0.05698028822981897, "498": 0.05698028822981897, "320": 0.05698028822981897, "174": -0.1709408646894569, "354": -0.05698028822981897, "205": 0.05698028822981897, "369": 0.05698028822981897, "459": 0.05698028822981897, "59": 0.05698028822981897, "319": 0.11396057645963795, "163": 0.05698028822981897, "106": -0.05698028822981897, "396": 0.05698028822981897, "41": 0.05698028822981897, "31": 0.1709408646894569, "252": 0.05698028822981897, "439": -0.11396057645963795, "213": -0.05698028822981897, "277": -0.05698028822981897, "418": -0.05698028822981897, "364": -0.05698028822981897, "64": 0.05698028822981897, "26": -0.05698028822981897, "297": 0.05698028822981897, "336": 0.05698028822981897 } }, { "id": "run_memory_fabric.py#2", "path": "run_memory_fabric.py", "chunk": 2, "text": ".encode(\"set_policy_limit\", \"user\", \"high\", \"editor\") untrusted = hmk.encode(\"rewrite_policy_limit\", \"agent\", \"low\", \"autofill\") rows = [] for label, cv, actor, expected in [ (\"trusted_user_memory\", trusted, \"user\", True), (\"untrusted_agent_memory\", untrusted, \"agent\", False), ]: result = hmk.verify(cv, actual_actor=actor, authorized_actor=\"user\", required_trust=\"high\") row = result.as_dict() row[\"domain\"] = \"provenance_hmk\" row[\"case\"] = label row[\"expected\"] = expected row[\"success\"] = result.accepted == expected rows.append(row) return rows def run_mesh(space: ComplexSpace) -> list[dict[str, object]]: nodes = [ Node(\"edge-router-01\", \"edge\", \"low\", \"router\"), Node(\"gpu-annealer-01\", \"server\", \"medium\", \"annealer\"), Node(\"audit-ledger-01\", \"server\", \"high\", \"provenance\"), Node(\"mobile-cache-01\", \"edge\", \"low\", \"memory\"), ] mesh = ResonantExecutionMesh(space, nodes) tasks = [ (\"last-mile-route\", \"edge\", \"low\", \"router\", \"edge-router-01\"), (\"sat-batch-anneal\", \"server\", \"medium\", \"annealer\", \"gpu-annealer-01\"), (\"hmk-audit\", \"server\", \"high\", \"provenance\", \"audit-ledger-01\"), ] rows = [] for task in tasks: result = mesh.route(*task) row = result.as_dict() row[\"domain\"] = \"resonant_", "tokens": 142, "vector": { "439": -0.0936585811581694, "213": -0.0936585811581694, "4": -0.1404878717372541, "366": 0.1404878717372541, "277": -0.0468292905790847, "355": -0.4214636152117623, "31": 0.1404878717372541, "418": -0.0468292905790847, "212": -0.0936585811581694, "364": -0.1873171623163388, "64": 0.0468292905790847, "444": -0.1873171623163388, "16": 0.0936585811581694, "26": -0.0936585811581694, "297": 0.0936585811581694, "336": 0.0936585811581694, "206": 0.1873171623163388, "344": -0.0936585811581694, "73": 0.0468292905790847, "169": 0.0468292905790847, "181": 0.0468292905790847, "43": -0.0468292905790847, "252": 0.0468292905790847, "246": -0.2341464528954235, "417": -0.2809757434745082, "117": -0.0468292905790847, "505": 0.0468292905790847, "14": -0.0468292905790847, "151": 0.0936585811581694, "498": 0.0936585811581694, "284": -0.0468292905790847, "187": 0.0468292905790847, "174": -0.0936585811581694, "7": 0.0468292905790847, "369": 0.0468292905790847, "459": 0.0468292905790847, "116": 0.0468292905790847, "319": 0.0936585811581694, "163": 0.0468292905790847, "106": -0.0468292905790847, "396": 0.0468292905790847, "41": 0.0468292905790847, "325": -0.0936585811581694, "84": 0.1873171623163388, "372": 0.1873171623163388, "403": 0.3278050340535929, "483": -0.0936585811581694, "103": 0.1873171623163388, "179": -0.1873171623163388, "192": -0.1404878717372541, "440": -0.1404878717372541, "254": -0.0936585811581694, "150": 0.0936585811581694, "94": 0.0468292905790847, "211": -0.0936585811581694, "471": -0.0468292905790847, "264": 0.0936585811581694, "309": 0.0468292905790847, "415": 0.0468292905790847, "138": 0.0936585811581694, "51": 0.0468292905790847, "75": 0.0468292905790847, "348": 0.0468292905790847, "270": 0.0936585811581694 } }, { "id": "run_memory_fabric.py#3", "path": "run_memory_fabric.py", "chunk": 3, "text": " \"server\", \"high\", \"provenance\", \"audit-ledger-01\"), ] rows = [] for task in tasks: result = mesh.route(*task) row = result.as_dict() row[\"domain\"] = \"resonant_execution_mesh\" rows.append(row) return rows def report(rows: list[dict[str, object]]) -> str: passed = sum(1 for row in rows if row[\"success\"]) lines = [ \"# Memory-Native Compute Fabric Report\", \"\", f\"- Cases: `{len(rows)}`\", f\"- Passed: `{passed}`\", f\"- Success rate: `{passed / len(rows):.2f}`\", \"\", \"This proof harness demonstrates shared complex-hypervector operations for tiered retrieval, provenance scoring, and semantic execution routing. The provenance score is a vector threshold, not a cryptographic signature.\", \"\", \"| domain | case/query/task | result | success |\", \"| --- | --- | --- | --- |\", ] for row in rows: label = row.get(\"query\") or row.get(\"case\") or row.get(\"task\") result = row.get(\"value\") or row.get(\"selected_node\") or row.get(\"accepted\") lines.append(f\"| {row['domain']} | {label} | {result} | {row['success']} |\") return \"\\n\".join(lines) + \"\\n\" def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--dimension\", type=int, default=4096) parser.add_argument(\"--out-dir\"", "tokens": 150, "vector": { "179": -0.04327423224079154, "366": 0.04327423224079154, "150": 0.12982269672237462, "440": -0.04327423224079154, "254": -0.04327423224079154, "403": 0.0, "444": -0.34619385792633234, "16": 0.17309692896316617, "270": 0.17309692896316617, "344": -0.17309692896316617, "264": 0.04327423224079154, "246": -0.21637116120395772, "211": -0.04327423224079154, "138": 0.04327423224079154, "355": -0.6058392513710816, "151": 0.04327423224079154, "498": 0.12982269672237462, "399": -0.04327423224079154, "174": -0.25964539344474924, "369": -0.08654846448158308, "459": 0.08654846448158308, "132": -0.08654846448158308, "163": 0.04327423224079154, "106": -0.04327423224079154, "396": 0.08654846448158308, "41": -0.21637116120395772, "296": -0.17309692896316617, "265": 0.04327423224079154, "94": -0.12982269672237462, "192": -0.04327423224079154, "446": -0.04327423224079154, "176": 0.04327423224079154, "173": 0.04327423224079154, "309": -0.04327423224079154, "113": 0.08654846448158308, "453": -0.04327423224079154, "312": -0.04327423224079154, "286": -0.04327423224079154, "449": 0.04327423224079154, "329": 0.04327423224079154, "114": -0.04327423224079154, "164": 0.0, "490": 0.04327423224079154, "189": -0.04327423224079154, "116": -0.04327423224079154, "364": 0.04327423224079154, "354": -0.04327423224079154, "55": 0.0, "252": 0.04327423224079154, "441": -0.04327423224079154, "421": 0.04327423224079154, "506": -0.04327423224079154, "432": 0.04327423224079154, "73": -0.04327423224079154, "293": -0.04327423224079154, "61": 0.04327423224079154, "282": -0.04327423224079154, "187": 0.08654846448158308, "424": -0.08654846448158308, "26": -0.08654846448158308, "205": 0.04327423224079154, "145": 0.04327423224079154, "7": 0.04327423224079154, "13": -0.04327423224079154, "276": 0.04327423224079154, "433": -0.12982269672237462, "427": 0.04327423224079154, "271": 0.04327423224079154, "201": -0.04327423224079154, "208": 0.04327423224079154, "475": -0.08654846448158308, "328": -0.04327423224079154, "275": -0.04327423224079154, "109": 0.04327423224079154, "139": -0.04327423224079154, "118": -0.04327423224079154, "43": -0.04327423224079154 } }, { "id": "run_memory_fabric.py#4", "path": "run_memory_fabric.py", "chunk": 4, "text": "main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--dimension\", type=int, default=4096) parser.add_argument(\"--out-dir\", type=Path, default=Path(\"results/memory_fabric\")) args = parser.parse_args() space = ComplexSpace(args.dimension) rows = run_memory(space) + run_provenance(space) + run_mesh(space) write_csv(args.out_dir / \"fabric_results.csv\", rows) write_json(args.out_dir / \"fabric_results.json\", rows) (args.out_dir / \"fabric_report.md\").parent.mkdir(parents=True, exist_ok=True) (args.out_dir / \"fabric_report.md\").write_text(report(rows), encoding=\"utf-8\") passed = sum(1 for row in rows if row[\"success\"]) print(f\"memory fabric proof cases passed: {passed}/{len(rows)}\") print(f\"wrote {args.out_dir / 'fabric_results.csv'}\") print(f\"wrote {args.out_dir / 'fabric_results.json'}\") print(f\"wrote {args.out_dir / 'fabric_report.md'}\") if __name__ == \"__main__\": main()", "tokens": 109, "vector": { "13": -0.10327955589886445, "276": 0.051639777949432225, "433": -0.2065591117977289, "427": 0.051639777949432225, "271": 0.051639777949432225, "201": -0.051639777949432225, "208": 0.051639777949432225, "475": -0.10327955589886445, "328": -0.10327955589886445, "275": -0.10327955589886445, "109": 0.051639777949432225, "55": 0.10327955589886445, "139": -0.051639777949432225, "118": -0.051639777949432225, "43": -0.051639777949432225, "416": -0.10327955589886445, "448": -0.051639777949432225, "437": -0.051639777949432225, "265": 0.5163977794943223, "322": 0.051639777949432225, "319": 0.2065591117977289, "355": -0.051639777949432225, "444": -0.30983866769659335, "89": -0.051639777949432225, "59": 0.051639777949432225, "116": 0.051639777949432225, "401": -0.051639777949432225, "153": -0.4131182235954578, "81": 0.2065591117977289, "339": 0.10327955589886445, "251": 0.051639777949432225, "258": 0.10327955589886445, "329": -0.15491933384829668, "33": -0.15491933384829668, "484": -0.051639777949432225, "405": 0.051639777949432225, "108": -0.051639777949432225, "181": 0.10327955589886445, "495": 0.051639777949432225, "230": -0.051639777949432225, "132": -0.051639777949432225, "232": -0.051639777949432225, "296": -0.15491933384829668, "16": 0.051639777949432225, "344": -0.051639777949432225, "403": -0.10327955589886445, "174": -0.051639777949432225, "462": 0.2065591117977289, "192": -0.051639777949432225, "173": 0.051639777949432225, "449": 0.051639777949432225, "309": -0.051639777949432225, "113": 0.051639777949432225, "346": -0.15491933384829668, "34": -0.051639777949432225, "333": 0.051639777949432225 } }, { "id": "run_ops_dashboard.py#0", "path": "run_ops_dashboard.py", "chunk": 0, "text": "\"\"\"Build the organizer dashboard, asset catalog, RAG index, and Cloudflare AI agent specs.\"\"\" from __future__ import annotations import argparse import json import shutil from pathlib import Path from ops_agents import write_agents from ops_catalog import build_catalog from ops_rag import build_index, query_index ROOT = Path(__file__).resolve().parent WORKER_JS = r\"\"\" const JSON_HEADERS = { \"content-type\": \"application/json; charset=utf-8\", \"access-control-allow-origin\": \"*\", \"access-control-allow-methods\": \"GET,POST,OPTIONS\", \"access-control-allow-headers\": \"content-type\", }; function tokenize(text) { return String(text || \"\").toLowerCase().match(/[a-z0-9_]{2,}/g) || []; } function scoreDoc(queryTokens, doc) { const haystack = tokenize(`${doc.path} ${doc.text}`); const counts = new Map(); for (const token of haystack) counts.set(token, (counts.get(token) || 0) + 1); let score = 0; for (const token of queryTokens) { if (counts.has(token)) score += 1 + Math.log(1 + counts.get(token)); } return score / Math.sqrt(1 + haystack.length); } async function readJson(env, request, name) { const url = new URL(`/${name}`, request.url); const response = await env.ASSETS.fetch(url); if (!respons", "tokens": 152, "vector": { "324": 0.049875466805381644, "441": -0.049875466805381644, "267": -0.049875466805381644, "358": 0.0, "469": 0.049875466805381644, "357": 0.049875466805381644, "163": 0.049875466805381644, "27": -0.049875466805381644, "354": -0.049875466805381644, "115": 0.049875466805381644, "50": 0.049875466805381644, "212": -0.049875466805381644, "120": -0.049875466805381644, "373": -0.2493773340269082, "201": -0.049875466805381644, "217": 0.3491282676376715, "297": 0.049875466805381644, "427": 0.049875466805381644, "258": 0.09975093361076329, "393": 0.049875466805381644, "64": 0.049875466805381644, "416": -0.14962640041614492, "310": 0.049875466805381644, "205": 0.049875466805381644, "399": -0.049875466805381644, "277": 0.049875466805381644, "323": -0.049875466805381644, "243": 0.049875466805381644, "60": 0.3491282676376715, "328": -0.049875466805381644, "253": 0.049875466805381644, "484": -0.049875466805381644, "247": 0.3491282676376715, "372": 0.049875466805381644, "237": -0.09975093361076329, "275": 0.049875466805381644, "31": -0.14962640041614492, "279": 0.049875466805381644, "153": -0.049875466805381644, "160": -0.14962640041614492, "271": 0.14962640041614492, "65": -0.14962640041614492, "280": 0.049875466805381644, "63": 0.049875466805381644, "41": -0.14962640041614492, "370": 0.049875466805381644, "423": 0.049875466805381644, "311": -0.049875466805381644, "376": 0.2493773340269082, "146": -0.09975093361076329, "408": -0.14962640041614492, "369": 0.09975093361076329, "71": -0.049875466805381644, "257": 0.049875466805381644, "329": -0.049875466805381644, "248": 0.049875466805381644, "355": -0.049875466805381644, "460": 0.049875466805381644, "319": 0.14962640041614492, "461": -0.2493773340269082, "17": -0.09975093361076329, "96": -0.049875466805381644, "16": 0.09975093361076329, "296": -0.09975093361076329, "110": -0.049875466805381644, "86": -0.049875466805381644, "421": 0.14962640041614492, "403": -0.09975093361076329, "145": -0.049875466805381644, "88": 0.09975093361076329, "387": -0.049875466805381644, "382": 0.049875466805381644, "15": 0.049875466805381644, "45": 0.049875466805381644, "457": -0.049875466805381644, "439": -0.09975093361076329, "386": 0.09975093361076329, "169": 0.049875466805381644, "282": 0.049875466805381644, "395": 0.049875466805381644, "487": 0.049875466805381644, "151": -0.049875466805381644 } }, { "id": "run_ops_dashboard.py#1", "path": "run_ops_dashboard.py", "chunk": 1, "text": "ength); } async function readJson(env, request, name) { const url = new URL(`/${name}`, request.url); const response = await env.ASSETS.fetch(url); if (!response.ok) throw new Error(`Unable to load ${name}: ${response.status}`); return response.json(); } async function ragSearch(env, request, query, topK = 8) { const index = await readJson(env, request, \"rag_index.json\"); const queryTokens = tokenize(query); const scored = index.documents .map((doc) => ({ score: scoreDoc(queryTokens, doc), path: doc.path, chunk: doc.chunk, text: doc.text })) .filter((item) => item.score > 0) .sort((a, b) => b.score - a.score) .slice(0, topK); return scored; } function fallbackAnswer(query, hits) { if (!hits.length) { return \"I do not have a matching workspace memory yet. Try asking about HOPS, Cloudflare, paid pilots, RAG, Cloudflare AI agents, or the memory fabric.\"; } const sources = hits.slice(0, 3).map((hit) => `${hit.path}#${hit.chunk}`).join(\", \"); const summary = hits .slice(0, 3) .map((hit) => hit.text.replace(/\\s+/g, \" \").slice(0, 260)) .join(\"\\n\\n\"); return `Based on the local HOPS RAG index, the strongest sources are ${sources}.\\n\\n${summary}\\n\\nSuggested next step: open the cited files ", "tokens": 159, "vector": { "168": -0.04740454631399772, "45": 0.09480909262799544, "376": 0.2370227315699886, "457": -0.09480909262799544, "439": -0.1896181852559909, "31": -0.1896181852559909, "386": 0.14221363894199315, "247": 0.331831824197984, "296": -0.1896181852559909, "17": -0.09480909262799544, "169": 0.1896181852559909, "282": 0.09480909262799544, "395": 0.04740454631399772, "487": 0.04740454631399772, "403": -0.09480909262799544, "294": 0.04740454631399772, "123": -0.04740454631399772, "202": 0.04740454631399772, "401": 0.04740454631399772, "102": -0.04740454631399772, "12": -0.04740454631399772, "7": 0.04740454631399772, "369": 0.14221363894199315, "258": 0.09480909262799544, "360": 0.09480909262799544, "424": -0.14221363894199315, "458": -0.09480909262799544, "27": -0.14221363894199315, "146": -0.04740454631399772, "89": -0.09480909262799544, "155": 0.04740454631399772, "96": -0.14221363894199315, "275": 0.2370227315699886, "421": 0.1896181852559909, "460": 0.04740454631399772, "416": -0.14221363894199315, "364": -0.14221363894199315, "408": -0.14221363894199315, "479": 0.04740454631399772, "330": -0.09480909262799544, "356": -0.04740454631399772, "259": -0.1896181852559909, "464": -0.04740454631399772, "354": -0.1896181852559909, "15": 0.04740454631399772, "428": 0.04740454631399772, "293": -0.09480909262799544, "25": 0.04740454631399772, "224": 0.09480909262799544, "289": -0.04740454631399772, "192": -0.09480909262799544, "463": -0.04740454631399772, "30": -0.04740454631399772, "134": -0.04740454631399772, "164": -0.04740454631399772, "152": 0.09480909262799544, "115": 0.09480909262799544, "38": -0.04740454631399772, "163": 0.09480909262799544, "50": 0.04740454631399772, "140": -0.04740454631399772, "441": -0.1896181852559909, "173": 0.04740454631399772, "135": -0.14221363894199315, "99": 0.2370227315699886, "344": -0.09480909262799544, "374": -0.09480909262799544, "378": 0.04740454631399772, "313": -0.04740454631399772, "221": 0.04740454631399772, "440": -0.04740454631399772, "160": -0.04740454631399772, "186": 0.04740454631399772, "231": 0.04740454631399772, "454": 0.04740454631399772, "62": -0.04740454631399772, "35": 0.04740454631399772, "4": 0.04740454631399772, "317": 0.04740454631399772 } }, { "id": "run_ops_dashboard.py#2", "path": "run_ops_dashboard.py", "chunk": 2, "text": "60)) .join(\"\\n\\n\"); return `Based on the local HOPS RAG index, the strongest sources are ${sources}.\\n\\n${summary}\\n\\nSuggested next step: open the cited files or ask a narrower follow-up so I can pull the exact implementation detail.`; } async function cloudflareAiAnswer(env, query, hits) { if (!env.AI) return null; const context = hits.map((hit) => `[${hit.path}#${hit.chunk}]\\n${hit.text}`).join(\"\\n\\n\"); const model = env.CF_AI_MODEL || \"@cf/moonshotai/kimi-k2.6\"; try { const response = await env.AI.run(model, { messages: [ { role: \"system\", content: \"You are the HOPS work organizer agent running on Cloudflare Workers AI. Answer using only the provided project context. Be concise, cite file paths, and distinguish facts from suggested next steps.\" }, { role: \"user\", content: `Question: ${query}\\n\\nProject context:\\n${context}` } ], temperature: 0.2, max_tokens: 900 }); return response?.response || response?.choices?.[0]?.message?.content || null; } catch (error) { return null; } } async function handleQuery(request, env) { const url = new URL(request.url); let query = url.searchParams.get(\"q\") || \"\"; let topK = Number(url.searchParams.get(\"top_k\") || 8); if (request.method === \"PO", "tokens": 159, "vector": { "57": 0.05352877275721892, "344": -0.10705754551443784, "369": 0.16058631827165676, "221": 0.05352877275721892, "440": -0.10705754551443784, "441": -0.3211726365433135, "293": -0.05352877275721892, "152": 0.10705754551443784, "163": 0.10705754551443784, "27": -0.05352877275721892, "160": -0.05352877275721892, "135": -0.10705754551443784, "186": 0.10705754551443784, "374": -0.05352877275721892, "231": 0.05352877275721892, "454": 0.10705754551443784, "62": -0.05352877275721892, "35": 0.05352877275721892, "4": 0.0, "317": 0.05352877275721892, "303": -0.05352877275721892, "30": -0.10705754551443784, "117": 0.05352877275721892, "417": 0.05352877275721892, "159": -0.10705754551443784, "453": 0.05352877275721892, "506": 0.05352877275721892, "474": 0.05352877275721892, "156": -0.05352877275721892, "45": 0.10705754551443784, "376": 0.10705754551443784, "337": 0.05352877275721892, "439": -0.2141150910288757, "424": -0.16058631827165676, "354": -0.16058631827165676, "403": -0.16058631827165676, "50": 0.16058631827165676, "372": -0.16058631827165676, "247": 0.2141150910288757, "490": -0.2141150910288757, "96": -0.05352877275721892, "99": 0.2141150910288757, "416": -0.05352877275721892, "364": -0.05352877275721892, "408": -0.05352877275721892, "147": 0.10705754551443784, "371": 0.05352877275721892, "228": -0.05352877275721892, "484": -0.05352877275721892, "257": -0.05352877275721892, "169": 0.2141150910288757, "282": 0.05352877275721892, "172": -0.05352877275721892, "245": -0.05352877275721892, "75": 0.10705754551443784, "443": 0.0, "237": -0.16058631827165676, "256": -0.05352877275721892, "267": -0.05352877275721892, "212": -0.05352877275721892, "448": 0.05352877275721892, "115": 0.05352877275721892, "493": 0.05352877275721892, "269": 0.05352877275721892, "162": -0.05352877275721892, "505": 0.05352877275721892, "218": -0.05352877275721892, "36": 0.05352877275721892, "326": -0.05352877275721892, "487": -0.05352877275721892, "190": 0.05352877275721892, "59": 0.05352877275721892, "336": -0.05352877275721892, "313": 0.10705754551443784, "6": 0.05352877275721892, "373": -0.05352877275721892, "394": 0.05352877275721892, "31": -0.2141150910288757, "215": -0.05352877275721892, "179": -0.05352877275721892, "197": -0.05352877275721892, "445": 0.05352877275721892, "427": 0.05352877275721892, "202": 0.05352877275721892, "157": 0.05352877275721892, "296": -0.2676438637860946, "17": -0.05352877275721892, "86": -0.10705754551443784, "148": 0.10705754551443784, "41": -0.10705754551443784, "458": -0.05352877275721892, "18": 0.05352877275721892, "383": 0.05352877275721892, "91": 0.05352877275721892, "353": -0.05352877275721892 } }, { "id": "run_ops_dashboard.py#3", "path": "run_ops_dashboard.py", "chunk": 3, "text": "const url = new URL(request.url); let query = url.searchParams.get(\"q\") || \"\"; let topK = Number(url.searchParams.get(\"top_k\") || 8); if (request.method === \"POST\") { const payload = await request.json().catch(() => ({})); query = payload.query || query; topK = Number(payload.top_k || topK); } if (!query.trim()) { return Response.json({ error: \"Missing query. Use /api/query?q=...\" }, { status: 400, headers: JSON_HEADERS }); } const hits = await ragSearch(env, request, query, Math.min(Math.max(topK, 1), 20)); const ai = await cloudflareAiAnswer(env, query, hits); return Response.json({ query, answer: ai || fallbackAnswer(query, hits), mode: ai ? \"cloudflare-workers-ai\" : \"local-rag-fallback\", model: ai ? (env.CF_AI_MODEL || \"@cf/moonshotai/kimi-k2.6\") : null, hits }, { headers: JSON_HEADERS }); } async function handleCatchup(request, env) { const catalog = await readJson(env, request, \"asset_catalog.json\"); const agents = await readJson(env, request, \"kimi_agents.json\"); const hits = await ragSearch(env, request, \"current HOPS platform state paid pilot Cloudflare RAG memory fabric plugin\", 10); return Response.json({ summary: { local_assets: catalog.local_assets.length, cloudflare_c", "tokens": 150, "vector": { "247": 0.2916666666666667, "296": -0.20833333333333334, "17": -0.041666666666666664, "31": -0.3333333333333333, "86": -0.08333333333333333, "424": -0.4583333333333333, "148": 0.08333333333333333, "41": -0.08333333333333333, "458": -0.16666666666666666, "18": 0.08333333333333333, "383": 0.08333333333333333, "403": -0.125, "91": 0.041666666666666664, "370": 0.041666666666666664, "291": -0.125, "282": 0.25, "258": 0.25, "159": -0.041666666666666664, "101": -0.041666666666666664, "369": 0.125, "169": 0.125, "202": 0.041666666666666664, "255": -0.041666666666666664, "419": 0.041666666666666664, "20": 0.041666666666666664, "7": 0.0, "38": -0.08333333333333333, "311": -0.08333333333333333, "372": 0.041666666666666664, "354": -0.20833333333333334, "360": 0.08333333333333333, "439": -0.2916666666666667, "88": 0.08333333333333333, "287": -0.041666666666666664, "411": -0.041666666666666664, "245": 0.041666666666666664, "50": 0.20833333333333334, "337": 0.041666666666666664, "269": 0.041666666666666664, "464": -0.041666666666666664, "230": -0.041666666666666664, "115": 0.08333333333333333, "493": 0.041666666666666664, "293": -0.041666666666666664, "163": 0.08333333333333333, "92": -0.041666666666666664, "147": 0.041666666666666664, "371": 0.041666666666666664, "228": -0.041666666666666664, "484": -0.041666666666666664, "257": -0.041666666666666664, "45": 0.041666666666666664, "376": 0.041666666666666664, "158": 0.041666666666666664, "357": 0.08333333333333333, "457": -0.08333333333333333, "499": 0.041666666666666664, "140": -0.041666666666666664, "249": -0.041666666666666664, "151": 0.041666666666666664, "152": 0.041666666666666664, "210": 0.041666666666666664, "75": 0.041666666666666664, "192": -0.041666666666666664, "173": 0.041666666666666664, "94": -0.041666666666666664, "74": 0.041666666666666664, "374": -0.041666666666666664, "15": -0.041666666666666664, "254": 0.041666666666666664 } }, { "id": "run_ops_dashboard.py#4", "path": "run_ops_dashboard.py", "chunk": 4, "text": "S platform state paid pilot Cloudflare RAG memory fabric plugin\", 10); return Response.json({ summary: { local_assets: catalog.local_assets.length, cloudflare_checks_passing: catalog.cloudflare.filter((row) => row.success).length, cloudflare_checks_total: catalog.cloudflare.length, github_repos_reachable: catalog.github.filter((row) => row.success).length, rag_hits_returned: hits.length, agents: agents.agents.map((agent) => agent.name), todos: catalog.todos, }, orientation: fallbackAnswer(\"catch up on the HOPS platform state\", hits), hits, }, { headers: JSON_HEADERS }); } export default { async fetch(request, env) { const url = new URL(request.url); if (request.method === \"OPTIONS\") return new Response(null, { headers: JSON_HEADERS }); try { if (url.pathname === \"/api/query\") return handleQuery(request, env); if (url.pathname === \"/api/catchup\") return handleCatchup(request, env); if (url.pathname === \"/api/agents\") return Response.json(await readJson(env, request, \"kimi_agents.json\"), { headers: JSON_HEADERS }); if (url.pathname === \"/api/catalog\") return Response.json(await readJson(env, request, \"asset_catalog.json\"), { headers: JSON_HEADERS }); return env.ASSETS.fetch(request);", "tokens": 142, "vector": { "210": 0.09090909090909091, "75": 0.09090909090909091, "38": -0.045454545454545456, "7": -0.045454545454545456, "115": 0.13636363636363635, "163": 0.0, "192": 0.0, "173": 0.045454545454545456, "94": -0.045454545454545456, "74": 0.045454545454545456, "369": 0.3181818181818182, "169": 0.18181818181818182, "258": 0.22727272727272727, "374": -0.045454545454545456, "15": 0.13636363636363635, "357": 0.2727272727272727, "26": -0.045454545454545456, "479": 0.09090909090909091, "355": -0.18181818181818182, "174": -0.09090909090909091, "165": 0.045454545454545456, "483": 0.045454545454545456, "366": -0.045454545454545456, "354": -0.13636363636363635, "140": -0.18181818181818182, "96": -0.045454545454545456, "212": -0.045454545454545456, "386": 0.045454545454545456, "456": 0.09090909090909091, "197": -0.045454545454545456, "464": -0.045454545454545456, "159": -0.045454545454545456, "117": 0.045454545454545456, "440": -0.045454545454545456, "441": -0.045454545454545456, "152": 0.045454545454545456, "311": -0.18181818181818182, "372": 0.13636363636363635, "55": 0.045454545454545456, "45": 0.045454545454545456, "487": 0.09090909090909091, "31": -0.36363636363636365, "439": -0.2727272727272727, "247": 0.045454545454545456, "296": -0.3181818181818182, "17": -0.09090909090909091, "403": -0.22727272727272727, "91": 0.045454545454545456, "423": 0.045454545454545456, "30": -0.045454545454545456, "99": -0.18181818181818182, "20": 0.18181818181818182, "424": -0.045454545454545456, "157": 0.045454545454545456, "158": 0.045454545454545456, "282": 0.09090909090909091, "457": -0.09090909090909091, "249": -0.045454545454545456, "499": 0.045454545454545456, "395": 0.045454545454545456 } }, { "id": "run_ops_dashboard.py#5", "path": "run_ops_dashboard.py", "chunk": 5, "text": "hname === \"/api/catalog\") return Response.json(await readJson(env, request, \"asset_catalog.json\"), { headers: JSON_HEADERS }); return env.ASSETS.fetch(request); } catch (error) { return Response.json({ error: String(error && error.message ? error.message : error) }, { status: 500, headers: JSON_HEADERS }); } } }; \"\"\" def render_dashboard(out_dir: Path, catalog: dict[str, object], index: dict[str, object], agents: dict[str, object]) -> Path: site = out_dir / \"site\" site.mkdir(parents=True, exist_ok=True) local_count = len(catalog[\"local_assets\"]) cf_success = sum(1 for row in catalog[\"cloudflare\"] if row.get(\"success\")) github_success = sum(1 for row in catalog[\"github\"] if row.get(\"success\")) todos = \"\\n\".join( f\"
  • {todo['priority']} {todo['title']}
    {todo['why']}
    • \" for todo in catalog[\"todos\"] ) agent_cards = \"\\n\".join( f\"

    {agent['name']}

    {agent['purpose']}

    {agent['model']}
    \" for agent in agents[\"agents\"] ) sample_hits = query_index(index, \"Cloudflare paid pilot memory fabric dashboard\", top_k=5) hits = \"\\n\".join( f\"
  • {hit['path']}#{hit['chunk']} score {hit['score']:.3f}

    {hit['te", "tokens": 152, "vector": { "76": 0.050251890762960605, "20": 0.050251890762960605, "357": 0.30151134457776363, "369": 0.15075567228888181, "169": 0.050251890762960605, "258": 0.15075567228888181, "282": 0.050251890762960605, "457": -0.050251890762960605, "439": -0.10050378152592121, "31": -0.050251890762960605, "499": 0.050251890762960605, "311": -0.10050378152592121, "372": 0.10050378152592121, "395": 0.050251890762960605, "487": 0.050251890762960605, "159": -0.050251890762960605, "202": 0.30151134457776363, "71": -0.050251890762960605, "427": 0.10050378152592121, "7": 0.0, "6": 0.050251890762960605, "459": 0.050251890762960605, "432": 0.050251890762960605, "153": -0.10050378152592121, "416": -0.15075567228888181, "106": -0.15075567228888181, "396": 0.15075567228888181, "41": 0.050251890762960605, "27": -0.10050378152592121, "140": -0.15075567228888181, "251": 0.15075567228888181, "405": 0.050251890762960605, "108": -0.050251890762960605, "181": 0.10050378152592121, "495": 0.050251890762960605, "113": 0.050251890762960605, "15": -0.050251890762960605, "400": 0.050251890762960605, "265": 0.10050378152592121, "16": 0.20100756305184242, "355": -0.20100756305184242, "344": -0.35176323534072423, "115": 0.10050378152592121, "403": -0.10050378152592121, "174": -0.10050378152592121, "192": 0.0, "456": 0.10050378152592121, "256": 0.15075567228888181, "493": -0.20100756305184242, "352": -0.20100756305184242, "48": -0.050251890762960605, "170": 0.050251890762960605, "136": 0.050251890762960605, "55": -0.10050378152592121, "43": -0.050251890762960605, "123": -0.050251890762960605, "388": 0.10050378152592121, "407": 0.10050378152592121, "212": -0.20100756305184242, "386": 0.050251890762960605, "129": 0.050251890762960605, "86": 0.10050378152592121, "147": 0.050251890762960605, "465": 0.050251890762960605, "60": 0.050251890762960605, "38": -0.050251890762960605, "173": 0.050251890762960605, "358": 0.050251890762960605, "383": 0.050251890762960605, "354": -0.050251890762960605, "99": 0.20100756305184242, "364": -0.050251890762960605, "421": 0.10050378152592121, "292": 0.050251890762960605, "45": -0.050251890762960605 } }, { "id": "run_ops_dashboard.py#6", "path": "run_ops_dashboard.py", "chunk": 6, "text": "oudflare paid pilot memory fabric dashboard\", top_k=5) hits = \"\\n\".join( f\"

  • {hit['path']}#{hit['chunk']} score {hit['score']:.3f}

    {hit['text']}

    • \" for hit in sample_hits ) content = f\"\"\" HOPS Ops Command Center

    HOPS Ops Command Center

    Catalog, RAG index, Cloudflare Workers AI agent specs, and todo board for keeping HOPS research, pilots, Cloudflare deployments, and memory-fabric work organized.

    {local_count}local indexed assets
    {len(index['documents'])}RAG chunks
    {github_success}/{len(catalog['github'])}GitHub repos reachable
    {cf_success}/{len(catalog['cloudflare'])}Cloudflare checks passing
    {cf_success}/{len(catalog['cloudflare'])}Cloudflare checks passing

    Suggested Todos

      {todos}

    Cloudflare AI Agent Mesh

    {agent_cards}

    Sample RAG Hits

      {hits}

    Ask The Work Agent

    Query the deployed RAG agent. Future Codex sessions can use /api/catchup to get oriented quickly.

    Ready.

    Audit Files

    asset_catalog.json \u00b7 rag_index.json \u00b7 kimi_agents.json

    \"\"\" path = site / \"index.html\" path.write_text(content, encoding=\"utf-8\") (site / \"_worker.js\").write_text(WORKER_JS, encoding=\"utf-8\") for name in (\"asset_catalog.json\", \"rag_index.json\", \"kimi_agents.json\"): shutil.copyfile(out_dir / name, site / name) return path def build_dashboard(out_dir: Path, include_cloudflare: bool = True) -> Path: catalog = build_catalog(out_dir, include_cloudflare=include_cloudflare) index = build_index(out_dir) agents = write_agents(out_dir) return render_dashboard(out_dir, catalog, index, agents) def main() -> None: parser = argparse.ArgumentParser(description=__doc__", "tokens": 126, "vector": { "423": 0.11009637651263607, "269": 0.1651445647689541, "236": -0.11009637651263607, "366": 0.055048188256318034, "247": 0.11009637651263607, "169": 0.11009637651263607, "282": 0.11009637651263607, "487": 0.055048188256318034, "296": -0.11009637651263607, "291": -0.11009637651263607, "258": 0.3853373177942262, "197": -0.055048188256318034, "252": 0.11009637651263607, "372": -0.055048188256318034, "67": 0.1651445647689541, "158": 0.1651445647689541, "303": -0.055048188256318034, "290": 0.11009637651263607, "485": 0.11009637651263607, "284": -0.11009637651263607, "20": 0.11009637651263607, "424": -0.1651445647689541, "91": 0.055048188256318034, "370": 0.055048188256318034, "311": -0.055048188256318034, "237": -0.11009637651263607, "275": -0.055048188256318034, "31": -0.055048188256318034, "205": 0.11009637651263607, "383": 0.055048188256318034, "163": -0.11009637651263607, "33": -0.055048188256318034, "229": -0.11009637651263607, "416": -0.27524094128159016, "251": 0.1651445647689541, "27": -0.1651445647689541, "230": -0.11009637651263607, "232": -0.11009637651263607, "153": -0.4403855060505443, "184": 0.055048188256318034, "22": -0.055048188256318034, "358": -0.055048188256318034, "16": 0.055048188256318034, "386": 0.1651445647689541, "344": -0.055048188256318034, "499": 0.055048188256318034, "360": 0.055048188256318034, "249": -0.055048188256318034, "393": 0.055048188256318034, "369": 0.11009637651263607, "459": 0.11009637651263607, "140": -0.1651445647689541, "208": -0.11009637651263607, "183": -0.055048188256318034, "181": 0.055048188256318034, "357": 0.11009637651263607, "277": 0.055048188256318034, "243": 0.055048188256318034, "432": 0.055048188256318034, "13": -0.055048188256318034, "276": 0.055048188256318034, "433": -0.055048188256318034, "427": 0.055048188256318034, "271": 0.055048188256318034, "201": -0.055048188256318034 } }, { "id": "run_ops_dashboard.py#10", "path": "run_ops_dashboard.py", "chunk": 10, "text": "agents = write_agents(out_dir) return render_dashboard(out_dir, catalog, index, agents) def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--out-dir\", type=Path, default=ROOT / \"results\" / \"ops_dashboard\") parser.add_argument(\"--no-cloudflare\", action=\"store_true\") args = parser.parse_args() path = build_dashboard(args.out_dir, include_cloudflare=not args.no_cloudflare) print(f\"wrote {path}\") if __name__ == \"__main__\": main()", "tokens": 51, "vector": { "140": -0.31799936400190804, "205": 0.105999788000636, "153": -0.31799936400190804, "369": 0.105999788000636, "432": 0.105999788000636, "357": 0.105999788000636, "27": -0.105999788000636, "459": 0.105999788000636, "13": -0.211999576001272, "276": 0.105999788000636, "433": -0.423999152002544, "427": 0.105999788000636, "271": 0.105999788000636, "201": -0.105999788000636, "208": 0.0, "475": -0.211999576001272, "118": -0.105999788000636, "43": -0.105999788000636, "275": -0.105999788000636, "416": -0.31799936400190804, "55": 0.105999788000636, "328": -0.105999788000636, "448": -0.105999788000636, "42": 0.105999788000636, "147": -0.105999788000636, "115": 0.105999788000636, "445": -0.105999788000636, "99": -0.105999788000636, "265": 0.31799936400190804, "322": 0.105999788000636, "293": -0.105999788000636, "21": -0.105999788000636, "462": 0.105999788000636, "346": -0.105999788000636, "403": -0.105999788000636, "34": -0.105999788000636, "333": 0.105999788000636 } }, { "id": "run_pilot_suite.py#0", "path": "run_pilot_suite.py", "chunk": 0, "text": "\"\"\"Build the HOPS paid-pilot proof package and static deployment site.\"\"\" from __future__ import annotations import argparse import csv import json import subprocess import sys import time from pathlib import Path from typing import Dict, List from run_hops_proof import run_proof, render_dashboard, write_csv, write_json from run_memory_fabric import report as fabric_report from run_memory_fabric import run_memory, run_mesh, run_provenance from memory_fabric import ComplexSpace ROOT = Path(__file__).resolve().parent def run_command(args: List[str]) -> Dict[str, object]: started = time.perf_counter() completed = subprocess.run( args, cwd=str(ROOT), capture_output=True, text=True, check=False, ) return { \"command\": \" \".join(args), \"returncode\": completed.returncode, \"seconds\": time.perf_counter() - started, \"stdout\": completed.stdout, \"stderr\": completed.stderr, \"success\": completed.returncode == 0, } def read_csv_rows(path: Path) -> list[dict[str, str]]: with path.open(newline=\"\", encoding=\"utf-8\") as handle: return list(csv.DictReader(handle)) def build_pilot_brief(path: Path, manifest: dict[str, object]) -> None: content = f\"\"\"# HOPS Paid Pilot Brief ## Pilot Offer HOPS is packaged", "tokens": 148, "vector": { "324": 0.044455422447438706, "441": -0.044455422447438706, "152": 0.1333662673423161, "38": -0.08891084489487741, "7": -0.1333662673423161, "449": 0.044455422447438706, "188": 0.044455422447438706, "354": -0.044455422447438706, "288": 0.044455422447438706, "430": 0.08891084489487741, "251": 0.08891084489487741, "373": -0.3111879571320709, "201": -0.044455422447438706, "217": 0.5334650693692644, "297": 0.044455422447438706, "427": 0.044455422447438706, "339": 0.08891084489487741, "258": 0.044455422447438706, "503": 0.08891084489487741, "337": 0.044455422447438706, "51": -0.1333662673423161, "64": 0.044455422447438706, "416": -0.3111879571320709, "504": -0.044455422447438706, "106": -0.17782168978975482, "163": 0.17782168978975482, "349": 0.044455422447438706, "156": 0.044455422447438706, "432": 0.044455422447438706, "401": -0.044455422447438706, "72": 0.08891084489487741, "132": -0.044455422447438706, "500": -0.08891084489487741, "329": -0.044455422447438706, "89": -0.08891084489487741, "116": 0.0, "59": 0.044455422447438706, "437": -0.044455422447438706, "355": 0.044455422447438706, "328": -0.08891084489487741, "253": 0.044455422447438706, "484": -0.044455422447438706, "459": 0.1333662673423161, "265": 0.1333662673423161, "396": 0.2667325346846322, "41": 0.1333662673423161, "259": 0.08891084489487741, "211": -0.08891084489487741, "333": 0.2222771122371935, "172": -0.044455422447438706, "367": -0.044455422447438706, "181": 0.08891084489487741, "408": 0.0, "32": -0.044455422447438706, "252": 0.044455422447438706, "369": 0.08891084489487741, "93": 0.044455422447438706, "344": -0.044455422447438706, "306": -0.1333662673423161, "99": -0.08891084489487741, "382": 0.08891084489487741, "174": -0.044455422447438706, "226": 0.044455422447438706, "262": 0.044455422447438706, "35": 0.044455422447438706, "234": 0.044455422447438706, "232": -0.044455422447438706, "153": -0.044455422447438706, "450": -0.08891084489487741, "435": 0.044455422447438706, "70": -0.044455422447438706, "261": -0.044455422447438706, "276": 0.044455422447438706, "237": -0.044455422447438706, "506": -0.044455422447438706, "419": 0.044455422447438706 } }, { "id": "run_pilot_suite.py#1", "path": "run_pilot_suite.py", "chunk": 1, "text": "ictReader(handle)) def build_pilot_brief(path: Path, manifest: dict[str, object]) -> None: content = f\"\"\"# HOPS Paid Pilot Brief ## Pilot Offer HOPS is packaged as a reproducible optimization and memory-fabric proof suite for paid discovery pilots. The current local package demonstrates four optimization domains plus a memory-native compute fabric under deterministic tests. ## What The Pilot Proves - SAT: exact brute-force validation on the demonstration formula. - Portfolio: exact brute-force validation over `C(20,5)=15504` portfolios. - Routing: exact nearest-depot assignment on the pilot assignment benchmark. - HP folding: continuous energy descent against the initial conformation. - Memory fabric: tiered retrieval, provenance filtering, and semantic routing. ## What It Does Not Claim This package does not prove that arbitrary NP-hard instances are solved in constant time. It proves that the supplied continuous-relaxation and hypervector-runtime harnesses reproduce their benchmark claims and emit auditable artifacts. ## Buyer-Facing Pilot Path 1. Replace toy portfolio/routing inputs with customer anonymized data. 2. Keep exact or trusted baselines for every tractable slice. 3. D", "tokens": 160, "vector": { "184": 0.05679618342470648, "450": -0.05679618342470648, "459": 0.05679618342470648, "70": -0.05679618342470648, "416": -0.17038855027411945, "261": -0.05679618342470648, "106": 0.0, "396": 0.05679618342470648, "41": 0.11359236684941296, "276": 0.05679618342470648, "237": 0.0, "152": 0.2839809171235324, "38": -0.11359236684941296, "7": -0.2839809171235324, "408": 0.05679618342470648, "506": 0.17038855027411945, "419": 0.05679618342470648, "500": -0.05679618342470648, "9": 0.05679618342470648, "190": 0.17038855027411945, "354": -0.2271847336988259, "192": -0.17038855027411945, "173": 0.17038855027411945, "449": 0.05679618342470648, "504": 0.05679618342470648, "16": 0.11359236684941296, "250": -0.05679618342470648, "224": 0.05679618342470648, "441": -0.3407771005482389, "151": 0.05679618342470648, "293": -0.17038855027411945, "188": 0.11359236684941296, "114": -0.05679618342470648, "260": -0.05679618342470648, "27": 0.05679618342470648, "56": 0.11359236684941296, "446": -0.05679618342470648, "176": 0.05679618342470648, "471": 0.05679618342470648, "267": -0.05679618342470648, "345": 0.0, "116": 0.11359236684941296, "280": -0.11359236684941296, "51": 0.05679618342470648, "12": -0.11359236684941296, "50": 0.17038855027411945, "440": -0.11359236684941296, "105": 0.05679618342470648, "115": -0.05679618342470648, "14": 0.11359236684941296, "95": 0.05679618342470648, "245": 0.05679618342470648, "232": -0.05679618342470648, "252": 0.17038855027411945, "330": 0.05679618342470648, "186": 0.11359236684941296, "206": 0.11359236684941296, "270": 0.11359236684941296, "147": -0.05679618342470648, "87": 0.05679618342470648, "503": -0.11359236684941296, "167": 0.05679618342470648, "428": -0.05679618342470648, "89": 0.05679618342470648, "366": 0.0, "150": -0.11359236684941296, "55": -0.05679618342470648, "42": -0.11359236684941296, "477": 0.05679618342470648, "286": -0.05679618342470648, "82": 0.05679618342470648, "398": 0.11359236684941296, "135": -0.05679618342470648, "266": -0.05679618342470648, "32": -0.05679618342470648, "458": -0.05679618342470648, "344": -0.05679618342470648, "57": -0.05679618342470648, "189": -0.05679618342470648, "217": 0.05679618342470648, "149": 0.05679618342470648, "326": 0.05679618342470648, "253": 0.05679618342470648, "166": 0.05679618342470648, "406": -0.05679618342470648, "257": -0.05679618342470648, "15": 0.05679618342470648, "365": 0.05679618342470648, "435": 0.05679618342470648, "378": 0.05679618342470648, "271": -0.05679618342470648, "262": 0.05679618342470648, "182": -0.05679618342470648, "58": -0.05679618342470648, "364": 0.05679618342470648, "369": -0.05679618342470648, "169": 0.05679618342470648, "436": 0.05679618342470648, "102": -0.05679618342470648, "47": -0.05679618342470648, "259": -0.05679618342470648 } }, { "id": "run_pilot_suite.py#2", "path": "run_pilot_suite.py", "chunk": 2, "text": "uyer-Facing Pilot Path 1. Replace toy portfolio/routing inputs with customer anonymized data. 2. Keep exact or trusted baselines for every tractable slice. 3. Define acceptance metrics before tuning. 4. Run HOPS against historical scenarios. 5. Deliver scorecards, runtime, failures, and next-step integration estimate. ## Build Status - Tests passed: `{manifest['tests']['success']}` - HOPS proof passed: `{manifest['hops']['passed']}/{manifest['hops']['total']}` - Memory fabric proof passed: `{manifest['memory_fabric']['passed']}/{manifest['memory_fabric']['total']}` ## Artifacts - `site/index.html` - `hops/hops_results.csv` - `hops/hops_results.json` - `memory_fabric/fabric_results.csv` - `memory_fabric/fabric_results.json` - `pilot_manifest.json` \"\"\" path.parent.mkdir(parents=True, exist_ok=True) path.write_text(content, encoding=\"utf-8\") def render_pilot_site(path: Path, manifest: dict[str, object]) -> None: status = \"PASS\" if manifest[\"overall_success\"] else \"CHECK\" cards = [] for item in manifest[\"scorecards\"]: cards.append( f\"\"\"
    {'PASS' if item['success'] else 'CHECK'}

    {item['name']}

    {item['summary']}

    \"\"\" ) content ", "tokens": 140, "vector": { "439": 0.054717565516458275, "435": 0.054717565516458275, "7": 0.054717565516458275, "416": -0.2735878275822914, "378": 0.054717565516458275, "271": -0.054717565516458275, "14": 0.054717565516458275, "252": 0.054717565516458275, "50": 0.054717565516458275, "262": 0.054717565516458275, "182": -0.054717565516458275, "345": 0.054717565516458275, "58": -0.054717565516458275, "364": 0.054717565516458275, "506": 0.0, "369": -0.054717565516458275, "169": 0.054717565516458275, "436": 0.054717565516458275, "16": 0.10943513103291655, "102": -0.054717565516458275, "47": -0.054717565516458275, "259": -0.054717565516458275, "492": 0.054717565516458275, "449": 0.16415269654937484, "23": -0.054717565516458275, "365": -0.054717565516458275, "455": 0.054717565516458275, "172": -0.054717565516458275, "152": 0.3283053930987497, "167": 0.054717565516458275, "501": -0.054717565516458275, "465": -0.054717565516458275, "390": 0.10943513103291655, "217": 0.054717565516458275, "61": -0.054717565516458275, "354": -0.054717565516458275, "454": 0.054717565516458275, "62": -0.054717565516458275, "235": -0.054717565516458275, "377": -0.054717565516458275, "324": 0.054717565516458275, "296": -0.2735878275822914, "261": -0.4377405241316662, "174": -0.16415269654937484, "273": -0.10943513103291655, "192": -0.054717565516458275, "173": 0.054717565516458275, "437": -0.2188702620658331, "15": 0.054717565516458275, "251": 0.054717565516458275, "27": -0.054717565516458275, "229": -0.054717565516458275, "128": -0.10943513103291655, "339": 0.10943513103291655, "258": 0.16415269654937484, "81": 0.10943513103291655, "336": 0.054717565516458275, "484": -0.054717565516458275, "405": 0.054717565516458275, "108": -0.054717565516458275, "181": 0.10943513103291655, "495": 0.054717565516458275, "230": -0.054717565516458275, "237": -0.10943513103291655, "232": -0.054717565516458275, "153": -0.054717565516458275, "459": 0.054717565516458275, "207": -0.054717565516458275, "106": -0.054717565516458275, "396": 0.054717565516458275, "41": 0.054717565516458275, "276": 0.054717565516458275, "471": -0.10943513103291655, "403": -0.10943513103291655, "314": 0.054717565516458275, "381": -0.10943513103291655, "32": -0.10943513103291655, "362": 0.10943513103291655, "330": -0.2188702620658331, "344": -0.054717565516458275, "388": 0.10943513103291655, "264": 0.054717565516458275, "387": -0.054717565516458275, "55": -0.10943513103291655, "249": -0.10943513103291655, "386": 0.054717565516458275, "374": -0.054717565516458275 } }, { "id": "run_pilot_suite.py#3", "path": "run_pilot_suite.py", "chunk": 3, "text": "nd( f\"\"\"
    {'PASS' if item['success'] else 'CHECK'}

    {item['name']}

    {item['summary']}

    \"\"\" ) content = f\"\"\" HOPS Pilot Suite

    HOPS Pilot Suite

    A reproducible, test-driven pilot package for continuous optimization, hypervector memory, provenance filtering, and resonant execution routing.

    {status}
    {''.join(cards)}
    Commercial posture: ready for paid discovery pilots on bounded customer datasets with agreed baselines and acceptance metrics. This dashboard is an evidence package, not a claim of universal complexity collapse.

    Audit files: pilot_manifest.json, pilot_brief.md.

    \"\"\" path.parent.mkdir(parents=True, exist_ok=True) path.write_text(content, encoding=\"utf-8\") def build_suite(out_dir: Path) -> dict[", "tokens": 150, "vector": { "287": -0.0659380473395787, "57": 0.1978141420187361, "70": -0.0659380473395787, "387": -0.1978141420187361, "55": -0.0659380473395787, "116": -0.1978141420187361, "325": 0.0659380473395787, "121": 0.1978141420187361, "204": -0.1318760946791574, "375": 0.0659380473395787, "8": 0.0659380473395787, "153": -0.0659380473395787, "249": -0.0659380473395787, "308": -0.0659380473395787, "235": 0.0659380473395787, "479": 0.0659380473395787, "340": -0.0659380473395787, "312": -0.1318760946791574, "385": 0.0659380473395787, "237": 0.0, "40": -0.0659380473395787, "456": 0.0659380473395787, "176": 0.0659380473395787, "171": 0.0659380473395787, "79": 0.0659380473395787, "310": -0.0659380473395787, "53": -0.0659380473395787, "331": -0.0659380473395787, "429": 0.0659380473395787, "393": -0.0659380473395787, "316": 0.0659380473395787, "367": 0.0659380473395787, "203": -0.0659380473395787, "159": 0.0, "291": 0.1318760946791574, "286": -0.1978141420187361, "307": 0.1318760946791574, "152": 0.0659380473395787, "7": -0.0659380473395787, "504": 0.0659380473395787, "264": 0.2637521893583148, "9": 0.0659380473395787, "188": 0.1318760946791574, "16": 0.1318760946791574, "503": -0.0659380473395787, "190": 0.0659380473395787, "189": -0.0659380473395787, "192": -0.0659380473395787, "150": 0.0659380473395787, "51": 0.0659380473395787, "354": -0.1318760946791574, "487": 0.0659380473395787, "164": 0.0659380473395787, "252": 0.0659380473395787, "461": 0.1318760946791574, "392": -0.0659380473395787, "13": -0.1318760946791574, "63": -0.2637521893583148, "15": -0.0659380473395787, "344": -0.0659380473395787, "362": 0.0659380473395787, "493": -0.1318760946791574, "208": 0.0659380473395787, "405": 0.0, "434": -0.0659380473395787, "38": -0.0659380473395787, "250": -0.0659380473395787, "224": 0.0659380473395787, "440": -0.1318760946791574, "115": -0.0659380473395787, "182": -0.0659380473395787, "113": 0.0659380473395787, "262": 0.0659380473395787, "158": -0.0659380473395787, "436": 0.0659380473395787, "449": 0.0659380473395787, "23": -0.0659380473395787, "358": 0.0659380473395787, "506": -0.0659380473395787, "234": -0.0659380473395787, "238": 0.0659380473395787, "293": -0.0659380473395787, "477": 0.0659380473395787, "296": 0.0659380473395787, "425": 0.0659380473395787, "415": 0.0659380473395787, "147": 0.0659380473395787, "317": 0.0659380473395787, "254": 0.1318760946791574, "336": 0.1318760946791574, "258": 0.1318760946791574, "174": 0.1318760946791574, "33": -0.1318760946791574, "229": -0.0659380473395787, "416": -0.1978141420187361, "484": -0.0659380473395787, "108": -0.0659380473395787, "181": 0.1318760946791574, "495": 0.0659380473395787, "230": -0.0659380473395787, "232": -0.0659380473395787, "459": 0.0659380473395787, "94": 0.0659380473395787, "106": -0.0659380473395787 } }, { "id": "run_pilot_suite.py#5", "path": "run_pilot_suite.py", "chunk": 5, "text": "p> \"\"\" path.parent.mkdir(parents=True, exist_ok=True) path.write_text(content, encoding=\"utf-8\") def build_suite(out_dir: Path) -> dict[str, object]: out_dir.mkdir(parents=True, exist_ok=True) tests = run_command([sys.executable, \"-m\", \"unittest\", \"discover\", \"-s\", \"tests\"]) hops_rows, hops_payload = run_proof(seed=7) hops_dir = out_dir / \"hops\" write_csv(hops_dir / \"hops_results.csv\", hops_rows) write_json(hops_dir / \"hops_results.json\", {\"rows\": hops_rows, \"payload\": hops_payload}) render_dashboard(hops_rows, hops_payload, hops_dir / \"dashboard.html\") space = ComplexSpace(4096) fabric_rows = run_memory(space) + run_provenance(space) + run_mesh(space) fabric_dir = out_dir / \"memory_fabric\" from run_memory_fabric import write_csv as fabric_write_csv from run_memory_fabric import write_json as fabric_write_json fabric_write_csv(fabric_dir / \"fabric_results.csv\", fabric_rows) fabric_write_json(fabric_dir / \"fabric_results.json\", fabric_rows) (fabric_dir / \"fabric_report.md\").write_text(fabric_report(fabric_rows), encoding=\"utf-8\") scorecards = [ { \"name\": \"Test Suite\", \"success\": tests[\"success\"], \"summary\": f\"{tests['command']} completed in {tests['seconds']:", "tokens": 118, "vector": { "13": -0.0562543950463012, "291": 0.0, "229": -0.1125087900926024, "416": -0.1687631851389036, "484": -0.0562543950463012, "405": 0.1125087900926024, "108": -0.1125087900926024, "181": 0.2250175801852048, "495": 0.1125087900926024, "230": -0.1125087900926024, "237": -0.281271975231506, "232": -0.1125087900926024, "153": -0.3375263702778072, "459": 0.0562543950463012, "94": 0.0562543950463012, "106": -0.0562543950463012, "396": 0.0562543950463012, "41": 0.0562543950463012, "345": 0.281271975231506, "430": 0.0562543950463012, "337": 0.0562543950463012, "162": 0.0562543950463012, "223": -0.0562543950463012, "91": 0.0562543950463012, "173": 0.1687631851389036, "156": 0.0562543950463012, "25": -0.0562543950463012, "81": 0.3375263702778072, "152": 0.0562543950463012, "401": -0.1125087900926024, "128": -0.1125087900926024, "339": 0.1125087900926024, "251": 0.1125087900926024, "258": 0.1125087900926024, "444": -0.0562543950463012, "432": 0.0562543950463012, "358": 0.0562543950463012, "319": 0.2250175801852048, "355": 0.0562543950463012, "139": -0.0562543950463012, "79": 0.2250175801852048, "89": -0.1125087900926024, "59": 0.0562543950463012, "116": 0.0562543950463012, "241": -0.2250175801852048, "437": -0.0562543950463012, "373": -0.1125087900926024, "72": 0.1125087900926024, "217": 0.1125087900926024, "500": -0.1125087900926024, "300": 0.1125087900926024, "485": 0.1125087900926024, "329": -0.1125087900926024, "33": -0.0562543950463012, "390": 0.0562543950463012, "386": 0.0562543950463012, "159": 0.0562543950463012, "504": 0.0562543950463012, "174": -0.1125087900926024, "374": -0.0562543950463012, "93": 0.0562543950463012, "333": 0.0562543950463012, "344": -0.0562543950463012 } }, { "id": "run_pilot_suite.py#6", "path": "run_pilot_suite.py", "chunk": 6, "text": "ric_rows), encoding=\"utf-8\") scorecards = [ { \"name\": \"Test Suite\", \"success\": tests[\"success\"], \"summary\": f\"{tests['command']} completed in {tests['seconds']:.2f}s.\", }, { \"name\": \"HOPS Optimization Proof\", \"success\": all(bool(row[\"proved\"]) for row in hops_rows), \"summary\": \"SAT, portfolio, routing, and HP folding proof cases generated with auditable outputs.\", }, { \"name\": \"Memory Fabric Proof\", \"success\": all(bool(row[\"success\"]) for row in fabric_rows), \"summary\": \"Tiered retrieval, provenance HMK filtering, and resonant routing passed.\", }, ] manifest = { \"overall_success\": all(item[\"success\"] for item in scorecards), \"generated_at_unix\": time.time(), \"tests\": tests, \"hops\": { \"passed\": sum(1 for row in hops_rows if row[\"proved\"]), \"total\": len(hops_rows), }, \"memory_fabric\": { \"passed\": sum(1 for row in fabric_rows if row[\"success\"]), \"total\": len(fabric_rows), }, \"scorecards\": scorecards, } write_json(out_dir / \"pilot_manifest.json\", manifest) build_pilot_brief(out_dir / \"pilot_brief.md\", manifest) render_pilot_site(out_dir / \"site\" / \"index.html\", manifest) return manifest def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--out", "tokens": 138, "vector": { "74": -0.050251890762960605, "232": -0.050251890762960605, "153": -0.20100756305184242, "390": 0.20100756305184242, "386": 0.15075567228888181, "159": 0.050251890762960605, "504": 0.050251890762960605, "174": -0.30151134457776363, "345": 0.251259453814803, "374": -0.15075567228888181, "93": 0.050251890762960605, "333": 0.050251890762960605, "344": -0.30151134457776363, "89": -0.050251890762960605, "312": -0.050251890762960605, "152": 0.10050378152592121, "190": 0.050251890762960605, "449": 0.15075567228888181, "350": 0.15075567228888181, "183": -0.10050378152592121, "355": -0.40201512610368484, "429": 0.10050378152592121, "16": 0.251259453814803, "237": -0.15075567228888181, "51": 0.0, "14": 0.050251890762960605, "252": 0.10050378152592121, "354": -0.10050378152592121, "147": -0.050251890762960605, "87": 0.050251890762960605, "309": -0.050251890762960605, "480": 0.050251890762960605, "262": 0.050251890762960605, "257": -0.050251890762960605, "123": 0.050251890762960605, "192": -0.050251890762960605, "173": 0.050251890762960605, "79": 0.15075567228888181, "366": 0.0, "150": 0.050251890762960605, "31": 0.050251890762960605, "487": 0.050251890762960605, "296": -0.15075567228888181, "261": -0.251259453814803, "314": 0.050251890762960605, "330": -0.10050378152592121, "65": -0.050251890762960605, "265": 0.10050378152592121, "403": -0.10050378152592121, "273": -0.10050378152592121, "113": 0.10050378152592121, "437": -0.050251890762960605, "251": 0.10050378152592121, "336": 0.050251890762960605, "258": 0.050251890762960605, "70": -0.050251890762960605, "33": -0.050251890762960605, "207": -0.050251890762960605, "27": -0.050251890762960605, "229": -0.050251890762960605, "369": 0.050251890762960605, "459": 0.050251890762960605, "13": -0.050251890762960605, "276": 0.050251890762960605, "433": -0.10050378152592121, "427": 0.050251890762960605, "271": 0.050251890762960605, "201": -0.050251890762960605, "208": 0.050251890762960605, "475": -0.050251890762960605, "118": -0.050251890762960605 } }, { "id": "run_pilot_suite.py#7", "path": "run_pilot_suite.py", "chunk": 7, "text": "(out_dir / \"site\" / \"index.html\", manifest) return manifest def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--out-dir\", type=Path, default=Path(\"results/pilot_suite\")) args = parser.parse_args() manifest = build_suite(args.out_dir) print(f\"pilot suite success: {manifest['overall_success']}\") print(f\"wrote {args.out_dir / 'site' / 'index.html'}\") print(f\"wrote {args.out_dir / 'pilot_manifest.json'}\") print(f\"wrote {args.out_dir / 'pilot_brief.md'}\") if __name__ == \"__main__\": main()", "tokens": 61, "vector": { "153": -0.41522739926869984, "251": 0.16609095970747995, "27": -0.16609095970747995, "229": -0.16609095970747995, "261": -0.3321819194149599, "369": 0.08304547985373997, "459": 0.08304547985373997, "13": -0.16609095970747995, "276": 0.08304547985373997, "433": -0.2491364395612199, "427": 0.08304547985373997, "271": 0.08304547985373997, "201": -0.08304547985373997, "208": 0.08304547985373997, "475": -0.08304547985373997, "118": -0.08304547985373997, "43": -0.08304547985373997, "275": -0.08304547985373997, "416": -0.16609095970747995, "55": 0.08304547985373997, "448": -0.08304547985373997, "483": 0.08304547985373997, "265": 0.41522739926869984, "322": 0.08304547985373997, "94": 0.08304547985373997, "462": 0.3321819194149599, "7": -0.08304547985373997, "504": 0.08304547985373997, "174": 0.0, "314": 0.08304547985373997, "346": -0.2491364395612199, "336": 0.08304547985373997, "258": 0.08304547985373997, "33": -0.08304547985373997, "403": -0.08304547985373997, "34": -0.08304547985373997, "333": 0.08304547985373997 } }, { "id": "run_sat_annealer.py#0", "path": "run_sat_annealer.py", "chunk": 0, "text": "\"\"\"Run and validate the continuous phase SAT annealer.\"\"\" from __future__ import annotations import argparse import csv import json import time from pathlib import Path from typing import List import numpy as np from sat_annealer import ( AnnealerConfig, anneal_sat, brute_force_solutions, example_formula, format_assignment, format_formula, random_planted_3sat, ) def write_csv(path: Path, rows: list[dict[str, object]]) -> None: if not rows: return fieldnames = sorted({field for row in rows for field in row}) path.parent.mkdir(parents=True, exist_ok=True) with path.open(\"w\", newline=\"\", encoding=\"utf-8\") as handle: writer = csv.DictWriter(handle, fieldnames=fieldnames) writer.writeheader() writer.writerows(rows) def write_json(path: Path, rows: list[dict[str, object]]) -> None: path.parent.mkdir(parents=True, exist_ok=True) path.write_text(json.dumps(rows, indent=2), encoding=\"utf-8\") def run_example(args: argparse.Namespace) -> dict[str, object]: formula = example_formula() config = AnnealerConfig( variables=3, iterations=args.iterations, restarts=args.restarts, seed=args.seed, ) result = anneal_sat(formula, config) solutions = brute_force_solutions(3, formula) expected = (True, Fal", "tokens": 150, "vector": { "172": -0.046524210519923545, "354": -0.046524210519923545, "19": 0.046524210519923545, "441": -0.046524210519923545, "503": -0.046524210519923545, "258": 0.13957263155977062, "51": 0.0, "103": 0.046524210519923545, "373": -0.18609684207969418, "201": -0.046524210519923545, "217": 0.41871789467931186, "297": 0.046524210519923545, "427": 0.09304842103984709, "339": 0.09304842103984709, "64": 0.046524210519923545, "416": -0.41871789467931186, "504": -0.046524210519923545, "163": 0.13957263155977062, "371": 0.046524210519923545, "500": -0.09304842103984709, "266": -0.046524210519923545, "359": 0.046524210519923545, "143": 0.09304842103984709, "315": 0.09304842103984709, "57": 0.09304842103984709, "423": -0.09304842103984709, "421": -0.046524210519923545, "296": 0.046524210519923545, "419": 0.046524210519923545, "459": 0.13957263155977062, "401": -0.046524210519923545, "444": -0.27914526311954124, "106": -0.13957263155977062, "396": 0.13957263155977062, "41": 0.13957263155977062, "276": 0.09304842103984709, "403": 0.0, "293": -0.046524210519923545, "369": 0.046524210519923545, "343": -0.13957263155977062, "219": 0.046524210519923545, "192": 0.09304842103984709, "16": 0.09304842103984709, "355": -0.09304842103984709, "344": -0.09304842103984709, "484": -0.09304842103984709, "405": 0.09304842103984709, "108": -0.09304842103984709, "181": 0.2326210525996177, "495": 0.09304842103984709, "262": 0.046524210519923545, "35": 0.0, "234": 0.046524210519923545, "232": -0.09304842103984709, "153": -0.09304842103984709, "450": -0.09304842103984709, "185": 0.13957263155977062, "141": 0.046524210519923545, "424": 0.046524210519923545, "150": -0.046524210519923545, "251": 0.046524210519923545, "230": -0.046524210519923545, "336": 0.046524210519923545, "383": 0.046524210519923545, "0": 0.046524210519923545, "265": 0.18609684207969418, "115": -0.13957263155977062, "183": 0.09304842103984709, "122": 0.046524210519923545, "298": 0.09304842103984709, "270": 0.09304842103984709, "25": -0.09304842103984709, "246": -0.046524210519923545, "206": 0.046524210519923545, "40": -0.046524210519923545 } }, { "id": "run_sat_annealer.py#1", "path": "run_sat_annealer.py", "chunk": 1, "text": "s.iterations, restarts=args.restarts, seed=args.seed, ) result = anneal_sat(formula, config) solutions = brute_force_solutions(3, formula) expected = (True, False, True) row = { \"case\": \"example\", \"variables\": 3, \"clauses\": len(formula), \"formula\": format_formula(formula), \"assignment\": format_assignment(result.assignment), \"assignment_bits\": \"\".join(\"1\" if value else \"0\" for value in result.assignment), \"expected_assignment\": format_assignment(expected), \"matches_expected\": result.assignment == expected, \"brute_force_solution_count\": len(solutions), \"success\": result.success, \"satisfied_clauses\": result.satisfied_clauses, \"total_clauses\": result.total_clauses, \"energy\": result.energy, \"iterations\": result.iterations, \"restart\": result.restart, \"probabilities\": list(result.probabilities), \"phases\": list(result.phases), } return row def run_planted(args: argparse.Namespace) -> List[dict[str, object]]: rng = np.random.default_rng(args.seed) rows: List[dict[str, object]] = [] for case_id in range(args.cases): formula, planted = random_planted_3sat(args.variables, args.clauses, rng) config = AnnealerConfig( variables=args.variables, iterations=args.iterations, restarts=args.restarts, s", "tokens": 125, "vector": { "298": 0.2182178902359924, "270": 0.1745743121887939, "265": 0.4364357804719848, "25": -0.13093073414159545, "246": -0.5237229365663818, "315": 0.04364357804719848, "115": -0.2618614682831909, "183": 0.08728715609439695, "35": -0.08728715609439695, "57": 0.04364357804719848, "206": 0.3055050463303894, "181": 0.08728715609439695, "252": 0.04364357804719848, "355": -0.08728715609439695, "187": 0.04364357804719848, "80": 0.04364357804719848, "122": 0.1745743121887939, "130": -0.08728715609439695, "113": 0.08728715609439695, "296": 0.04364357804719848, "421": -0.08728715609439695, "36": 0.04364357804719848, "344": -0.13093073414159545, "403": 0.0, "205": 0.08728715609439695, "381": -0.04364357804719848, "16": 0.08728715609439695, "68": 0.04364357804719848, "308": 0.04364357804719848, "261": -0.04364357804719848, "174": -0.08728715609439695, "167": 0.08728715609439695, "141": 0.08728715609439695, "56": 0.08728715609439695, "58": 0.08728715609439695, "508": -0.08728715609439695, "163": 0.1745743121887939, "390": 0.08728715609439695, "369": 0.04364357804719848, "459": 0.04364357804719848, "439": -0.04364357804719848, "427": 0.04364357804719848, "106": -0.08728715609439695, "396": 0.08728715609439695, "41": 0.08728715609439695, "177": 0.08728715609439695, "266": -0.04364357804719848, "420": -0.04364357804719848, "279": -0.04364357804719848, "444": -0.04364357804719848, "156": 0.04364357804719848, "290": 0.04364357804719848, "309": -0.04364357804719848, "55": 0.04364357804719848, "419": 0.04364357804719848, "143": 0.04364357804719848 } }, { "id": "run_sat_annealer.py#2", "path": "run_sat_annealer.py", "chunk": 2, "text": " random_planted_3sat(args.variables, args.clauses, rng) config = AnnealerConfig( variables=args.variables, iterations=args.iterations, restarts=args.restarts, seed=args.seed + case_id * 9973, ) started = time.perf_counter() result = anneal_sat(formula, config) elapsed = time.perf_counter() - started brute_force_count = \"\" brute_force_contains_result = \"\" if args.variables <= args.bruteforce_limit: solutions = brute_force_solutions(args.variables, formula) brute_force_count = len(solutions) brute_force_contains_result = result.assignment in solutions rows.append( { \"case\": f\"planted_{case_id}\", \"variables\": args.variables, \"clauses\": args.clauses, \"planted_assignment\": format_assignment(planted), \"assignment\": format_assignment(result.assignment), \"assignment_bits\": \"\".join(\"1\" if value else \"0\" for value in result.assignment), \"matches_planted\": result.assignment == planted, \"success\": result.success, \"satisfied_clauses\": result.satisfied_clauses, \"total_clauses\": result.total_clauses, \"energy\": result.energy, \"iterations\": result.iterations, \"restart\": result.restart, \"elapsed_seconds\": elapsed, \"brute_force_solution_count\": brute_force_count, \"brute_force_contains_result\": brute_", "tokens": 109, "vector": { "419": 0.04559607525875532, "265": 0.5015568278463085, "122": 0.3191725268112872, "130": -0.13678822577626595, "177": 0.04559607525875532, "183": 0.1823843010350213, "143": 0.04559607525875532, "298": 0.1823843010350213, "270": 0.09119215051751065, "25": -0.09119215051751065, "156": 0.09119215051751065, "440": 0.04559607525875532, "259": 0.09119215051751065, "51": -0.09119215051751065, "211": -0.09119215051751065, "246": -0.5015568278463085, "315": 0.04559607525875532, "115": -0.09119215051751065, "220": -0.13678822577626595, "447": 0.13678822577626595, "403": -0.09119215051751065, "128": -0.04559607525875532, "35": -0.13678822577626595, "57": 0.04559607525875532, "113": 0.04559607525875532, "206": 0.2279803762937766, "344": -0.13678822577626595, "444": -0.04559607525875532, "174": -0.13678822577626595, "187": 0.04559607525875532, "449": 0.04559607525875532, "362": 0.04559607525875532, "421": -0.09119215051751065, "55": 0.09119215051751065, "36": 0.04559607525875532, "205": 0.09119215051751065, "381": -0.04559607525875532, "16": 0.04559607525875532, "303": 0.04559607525875532, "167": 0.09119215051751065, "141": 0.09119215051751065, "56": 0.09119215051751065, "58": 0.09119215051751065, "107": -0.04559607525875532, "261": -0.04559607525875532, "121": 0.04559607525875532 } }, { "id": "run_sat_annealer.py#3", "path": "run_sat_annealer.py", "chunk": 3, "text": "result.iterations, \"restart\": result.restart, \"elapsed_seconds\": elapsed, \"brute_force_solution_count\": brute_force_count, \"brute_force_contains_result\": brute_force_contains_result, } ) return rows def write_report(path: Path, rows: list[dict[str, object]]) -> None: successes = sum(1 for row in rows if row[\"success\"]) content = f\"\"\"# Holographic SAT-Annealer Report ## Summary - Cases: `{len(rows)}` - Satisfying assignments found: `{successes}` - Success rate: `{successes / len(rows):.2f}` This is a continuous phase relaxation for SAT, not a proof that NP-hardness has been removed. It searches a smooth energy landscape, collapses phases to Boolean assignments, and validates small cases against exact brute force. ## Example Case The bundled example is: `(x0 OR x1) AND (NOT x0 OR x2) AND (NOT x1 OR NOT x2) AND (x0 OR x2)` Expected assignment for the demonstration: `(True, False, True)`. ## Outputs - `sat_results.csv` - `sat_results.json` - `sat_report.md` \"\"\" path.parent.mkdir(parents=True, exist_ok=True) path.write_text(content, encoding=\"utf-8\") def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--mode\", choices=[\"example\", \"planted\", \"bo", "tokens": 150, "vector": { "246": -0.11785113019775793, "298": 0.05892556509887897, "58": 0.11785113019775793, "107": -0.05892556509887897, "183": 0.23570226039551587, "261": -0.05892556509887897, "220": -0.05892556509887897, "447": 0.11785113019775793, "369": -0.1767766952966369, "444": -0.2946278254943948, "459": 0.11785113019775793, "173": 0.05892556509887897, "416": -0.23570226039551587, "163": 0.05892556509887897, "106": -0.05892556509887897, "396": 0.05892556509887897, "41": 0.05892556509887897, "276": 0.11785113019775793, "300": -0.1767766952966369, "265": 0.05892556509887897, "16": 0.1767766952966369, "355": -0.11785113019775793, "344": -0.05892556509887897, "403": -0.05892556509887897, "174": -0.11785113019775793, "237": -0.11785113019775793, "431": 0.05892556509887897, "51": 0.11785113019775793, "103": 0.05892556509887897, "132": 0.11785113019775793, "374": -0.05892556509887897, "309": -0.11785113019775793, "113": 0.11785113019775793, "68": -0.05892556509887897, "495": 0.1767766952966369, "188": -0.05892556509887897, "453": -0.05892556509887897, "312": -0.05892556509887897, "286": -0.05892556509887897, "506": -0.05892556509887897, "503": -0.05892556509887897, "258": 0.11785113019775793, "57": -0.05892556509887897, "293": -0.1767766952966369, "449": 0.05892556509887897, "398": 0.05892556509887897, "266": -0.05892556509887897, "145": -0.05892556509887897, "389": -0.05892556509887897, "481": 0.05892556509887897, "42": -0.05892556509887897, "455": -0.05892556509887897, "448": 0.05892556509887897, "56": 0.05892556509887897, "428": 0.05892556509887897, "40": -0.05892556509887897, "390": 0.05892556509887897, "102": -0.05892556509887897, "191": 0.05892556509887897, "354": -0.23570226039551587, "206": 0.1767766952966369, "385": -0.05892556509887897, "167": 0.05892556509887897, "12": -0.05892556509887897, "50": 0.05892556509887897, "80": 0.1767766952966369, "187": 0.05892556509887897, "441": -0.11785113019775793, "332": -0.05892556509887897, "492": 0.11785113019775793, "105": 0.05892556509887897, "181": 0.23570226039551587, "252": 0.05892556509887897, "123": 0.05892556509887897, "250": 0.11785113019775793, "339": 0.05892556509887897, "440": -0.05892556509887897, "33": -0.05892556509887897, "484": -0.05892556509887897, "405": 0.05892556509887897, "108": -0.05892556509887897, "230": -0.11785113019775793, "232": -0.05892556509887897, "153": -0.05892556509887897, "13": -0.05892556509887897, "433": -0.11785113019775793, "427": 0.05892556509887897, "271": 0.05892556509887897, "201": -0.05892556509887897, "208": 0.05892556509887897, "475": -0.05892556509887897, "313": 0.05892556509887897, "55": 0.05892556509887897, "317": -0.05892556509887897 } }, { "id": "run_sat_annealer.py#4", "path": "run_sat_annealer.py", "chunk": 4, "text": "nt, encoding=\"utf-8\") def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument(\"--mode\", choices=[\"example\", \"planted\", \"both\"], default=\"both\") parser.add_argument(\"--variables\", type=int, default=12) parser.add_argument(\"--clauses\", type=int, default=48) parser.add_argument(\"--cases\", type=int, default=20) parser.add_argument(\"--iterations\", type=int, default=500) parser.add_argument(\"--restarts\", type=int, default=32) parser.add_argument(\"--bruteforce-limit\", type=int, default=20) parser.add_argument(\"--seed\", type=int, default=7) parser.add_argument(\"--out-dir\", type=Path, default=Path(\"results/sat\")) args = parser.parse_args() rows: list[dict[str, object]] = [] if args.mode in (\"example\", \"both\"): rows.append(run_example(args)) if args.mode in (\"planted\", \"both\"): rows.extend(run_planted(args)) csv_path = args.out_dir / \"sat_results.csv\" json_path = args.out_dir / \"sat_results.json\" report_path = args.out_dir / \"sat_report.md\" write_csv(csv_path, rows) write_json(json_path, rows) write_report(report_path, rows) print(f\"ran {len(rows)} SAT cases\") print(f\"successes: {sum(1 for row in rows if row['success'])}/{len(rows)}\") print(f\"wrote {csv_", "tokens": 152, "vector": { "403": -0.14471492105848432, "232": -0.03617873026462108, "153": -0.14471492105848432, "459": 0.03617873026462108, "13": -0.03617873026462108, "276": 0.03617873026462108, "433": -0.3979660329108319, "427": 0.03617873026462108, "271": 0.03617873026462108, "201": -0.03617873026462108, "208": 0.03617873026462108, "475": -0.32560857238158974, "230": -0.10853619079386324, "313": 0.03617873026462108, "80": 0.07235746052924216, "55": 0.3979660329108319, "511": 0.14471492105848432, "122": 0.03617873026462108, "275": -0.28942984211696865, "109": 0.25325111185234755, "363": -0.03617873026462108, "130": -0.03617873026462108, "408": -0.03617873026462108, "309": -0.07235746052924216, "245": 0.07235746052924216, "298": 0.03617873026462108, "6": 0.03617873026462108, "270": 0.03617873026462108, "226": 0.03617873026462108, "407": 0.03617873026462108, "85": -0.03617873026462108, "25": -0.03617873026462108, "118": -0.03617873026462108, "43": -0.03617873026462108, "416": -0.07235746052924216, "448": -0.03617873026462108, "51": 0.07235746052924216, "265": 0.32560857238158974, "322": 0.03617873026462108, "444": -0.32560857238158974, "163": 0.03617873026462108, "106": -0.03617873026462108, "396": 0.03617873026462108, "41": 0.03617873026462108, "344": -0.10853619079386324, "174": -0.07235746052924216, "0": 0.03617873026462108, "447": 0.03617873026462108, "439": -0.03617873026462108, "502": -0.07235746052924216, "250": 0.07235746052924216, "339": 0.03617873026462108, "311": -0.07235746052924216, "258": 0.03617873026462108, "257": -0.07235746052924216, "440": -0.03617873026462108, "33": -0.03617873026462108, "401": -0.03617873026462108, "251": 0.03617873026462108, "173": 0.03617873026462108, "462": 0.10853619079386324, "200": 0.03617873026462108, "113": 0.07235746052924216, "300": -0.03617873026462108, "16": 0.03617873026462108, "355": -0.07235746052924216, "346": -0.03617873026462108, "164": 0.03617873026462108 } }, { "id": "run_sat_annealer.py#5", "path": "run_sat_annealer.py", "chunk": 5, "text": "te_report(report_path, rows) print(f\"ran {len(rows)} SAT cases\") print(f\"successes: {sum(1 for row in rows if row['success'])}/{len(rows)}\") print(f\"wrote {csv_path}\") print(f\"wrote {json_path}\") print(f\"wrote {report_path}\") if rows: print(f\"example/first assignment: {rows[0]['assignment']}\") if __name__ == \"__main__\": main()", "tokens": 42, "vector": { "328": 0.09053574604251853, "257": -0.18107149208503706, "444": -0.5432144762551112, "462": 0.5432144762551112, "200": 0.09053574604251853, "113": 0.18107149208503706, "51": 0.09053574604251853, "309": -0.09053574604251853, "300": -0.09053574604251853, "265": 0.09053574604251853, "16": 0.09053574604251853, "355": -0.18107149208503706, "344": -0.09053574604251853, "403": -0.2716072381275556, "174": -0.09053574604251853, "346": -0.2716072381275556, "502": -0.09053574604251853, "311": -0.09053574604251853, "80": 0.09053574604251853, "423": 0.09053574604251853, "206": 0.18107149208503706, "34": -0.09053574604251853, "333": 0.09053574604251853, "13": -0.09053574604251853 } }, { "id": "sat_annealer.py#0", "path": "sat_annealer.py", "chunk": 0, "text": "\"\"\"Continuous phase SAT annealer primitives. The annealer treats each Boolean variable as a phase/probability coordinate: p = theta / pi True -> theta = 0 -> p = 0 False -> theta = pi -> p = 1 For each clause, the energy is the product of literal-unsatisfied factors. This gives a smooth relaxation that can be optimized by gradient descent and then collapsed back to a Boolean assignment. \"\"\" from __future__ import annotations from dataclasses import dataclass from itertools import product from typing import Dict, Iterable, List, Optional, Sequence, Tuple import numpy as np Literal = Tuple[int, bool] Clause = Tuple[Literal, ...] Formula = Tuple[Clause, ...] @dataclass(frozen=True) class AnnealerConfig: variables: int iterations: int = 500 restarts: int = 32 learning_rate: float = 0.12 noise: float = 0.035 noise_decay: float = 0.985 binarize_pressure: float = 0.015 return_collapsed_vertices: bool = True seed: int = 7 @dataclass(frozen=True) class AnnealerResult: assignment: Tuple[bool, ...] phases: Tuple[float, ...] probabilities: Tuple[float, ...] energy: float satisfied_clauses: int total_clauses: int success: bool iterations: int restart: int def as_dict(self) -> Dict[str, object]:", "tokens": 149, "vector": { "503": -0.0481683050859088, "258": 0.0963366101718176, "51": 0.0481683050859088, "103": 0.0963366101718176, "340": -0.0481683050859088, "441": -0.1445049152577264, "383": 0.0481683050859088, "446": -0.0963366101718176, "191": 0.0963366101718176, "59": 0.0481683050859088, "500": -0.0963366101718176, "313": 0.0481683050859088, "428": -0.0481683050859088, "151": 0.1926732203436352, "133": 0.0963366101718176, "181": 0.1926732203436352, "252": 0.0481683050859088, "16": 0.0, "157": 0.1445049152577264, "56": 0.0963366101718176, "506": -0.0481683050859088, "424": -0.0963366101718176, "296": 0.1926732203436352, "386": -0.1926732203436352, "433": -0.0481683050859088, "30": -0.0481683050859088, "286": -0.0481683050859088, "100": -0.0481683050859088, "448": 0.0481683050859088, "57": -0.0481683050859088, "398": 0.0481683050859088, "159": -0.0481683050859088, "326": -0.0481683050859088, "483": 0.0481683050859088, "167": 0.0, "27": -0.0481683050859088, "190": 0.0481683050859088, "354": -0.0481683050859088, "289": 0.0481683050859088, "60": -0.0481683050859088, "102": -0.0481683050859088, "206": 0.0963366101718176, "373": -0.1926732203436352, "201": -0.0481683050859088, "217": 0.240841525429544, "297": 0.0481683050859088, "278": 0.0481683050859088, "74": 0.0481683050859088, "504": -0.0481683050859088, "106": -0.0963366101718176, "163": 0.0481683050859088, "492": -0.0481683050859088, "447": 0.0481683050859088, "251": -0.3371781356013616, "371": 0.0481683050859088, "266": -0.0481683050859088, "109": 0.4335147457731792, "183": -0.1926732203436352, "115": -0.0481683050859088, "511": 0.0963366101718176, "264": 0.0963366101718176, "143": 0.0481683050859088, "122": 0.0481683050859088, "298": 0.0963366101718176, "6": 0.0963366101718176, "270": 0.0481683050859088, "226": 0.0481683050859088, "259": -0.0481683050859088, "374": 0.3371781356013616, "363": -0.0481683050859088, "239": 0.0481683050859088, "65": 0.0481683050859088, "396": 0.0, "112": 0.0481683050859088, "178": -0.0481683050859088, "45": 0.0481683050859088, "388": 0.0481683050859088, "25": -0.0481683050859088, "29": 0.0481683050859088, "390": 0.0481683050859088, "508": -0.0481683050859088, "141": 0.0481683050859088, "174": -0.0481683050859088, "58": 0.0481683050859088, "459": 0.0481683050859088, "41": 0.0481683050859088 } }, { "id": "sat_annealer.py#1", "path": "sat_annealer.py", "chunk": 1, "text": "es: Tuple[float, ...] energy: float satisfied_clauses: int total_clauses: int success: bool iterations: int restart: int def as_dict(self) -> Dict[str, object]: return { \"assignment\": \"\".join(\"1\" if value else \"0\" for value in self.assignment), \"phases\": list(self.phases), \"probabilities\": list(self.probabilities), \"energy\": self.energy, \"satisfied_clauses\": self.satisfied_clauses, \"total_clauses\": self.total_clauses, \"success\": self.success, \"iterations\": self.iterations, \"restart\": self.restart, } def literal(var: int, negated: bool = False) -> Literal: return (var, negated) def clause(*literals: Literal) -> Clause: return tuple(literals) def example_formula() -> Formula: return ( clause(literal(0), literal(1)), clause(literal(0, True), literal(2)), clause(literal(1, True), literal(2, True)), clause(literal(0), literal(2)), ) def literal_unsat_factor(probabilities: np.ndarray, lit: Literal) -> float: var, negated = lit p_false = probabilities[var] return float(1.0 - p_false if negated else p_false) def clause_energy(probabilities: np.ndarray, item: Clause) -> float: energy = 1.0 for lit in item: energy *= literal_unsat_factor(probabilities, lit) return float(energy) def energy_an", "tokens": 139, "vector": { "192": -0.03832008326196737, "251": -0.07664016652393474, "374": 0.22992049957180422, "56": 0.22992049957180422, "167": 0.11496024978590211, "109": 0.19160041630983685, "141": 0.11496024978590211, "174": -0.11496024978590211, "183": -0.07664016652393474, "298": 0.11496024978590211, "58": 0.11496024978590211, "459": 0.26824058283377156, "151": 0.03832008326196737, "6": 0.3832008326196737, "106": -0.03832008326196737, "396": 0.03832008326196737, "41": 0.03832008326196737, "369": 0.22992049957180422, "206": 0.07664016652393474, "344": -0.11496024978590211, "403": -0.07664016652393474, "205": 0.07664016652393474, "381": -0.07664016652393474, "16": 0.07664016652393474, "390": 0.07664016652393474, "163": 0.07664016652393474, "508": -0.22992049957180422, "386": -0.45984099914360843, "200": -0.07664016652393474, "215": -0.15328033304786948, "252": 0.03832008326196737, "157": 0.26824058283377156, "37": 0.07664016652393474, "423": -0.03832008326196737, "115": -0.03832008326196737, "181": 0.11496024978590211, "266": -0.07664016652393474, "376": 0.07664016652393474, "419": 0.15328033304786948, "321": -0.11496024978590211, "413": -0.03832008326196737, "330": -0.07664016652393474, "244": -0.03832008326196737 } }, { "id": "sat_annealer.py#2", "path": "sat_annealer.py", "chunk": 2, "text": "ilities: np.ndarray, item: Clause) -> float: energy = 1.0 for lit in item: energy *= literal_unsat_factor(probabilities, lit) return float(energy) def energy_and_gradient(probabilities: np.ndarray, formula: Formula) -> Tuple[float, np.ndarray]: gradient = np.zeros_like(probabilities) energy = 0.0 for item in formula: factors = np.array([literal_unsat_factor(probabilities, lit) for lit in item], dtype=np.float64) item_energy = float(np.prod(factors)) energy += item_energy for idx, lit in enumerate(item): var, negated = lit sign = -1.0 if negated else 1.0 if len(item) == 1: partial = sign else: partial = sign * float(np.prod(np.delete(factors, idx))) gradient[var] += partial return energy, gradient def phase_angles(probabilities: Sequence[float]) -> Tuple[float, ...]: return tuple(float(value * np.pi) for value in probabilities) def collapse(probabilities: Sequence[float]) -> Tuple[bool, ...]: return tuple(bool(value < 0.5) for value in probabilities) def clause_satisfied(assignment: Sequence[bool], item: Clause) -> bool: for var, negated in item: value = assignment[var] if (not value) if negated else value: return True return False def count_satisfied(assignment: Sequence[bool], for", "tokens": 157, "vector": { "368": 0.03546634510659543, "266": -0.3546634510659543, "376": 0.1063990353197863, "330": -0.28373076085276344, "157": 0.07093269021319086, "374": 0.3191971059593589, "56": 0.2127980706395726, "16": 0.24826441574616803, "419": 0.2127980706395726, "344": -0.24826441574616803, "200": -0.07093269021319086, "508": -0.28373076085276344, "369": 0.2127980706395726, "459": 0.17733172553297716, "115": -0.1063990353197863, "251": -0.17733172553297716, "27": -0.1063990353197863, "333": -0.03546634510659543, "30": -0.1063990353197863, "219": 0.03546634510659543, "182": -0.03546634510659543, "107": 0.03546634510659543, "195": 0.07093269021319086, "103": 0.07093269021319086, "198": 0.07093269021319086, "148": -0.03546634510659543, "215": -0.14186538042638172, "267": -0.1063990353197863, "403": -0.14186538042638172, "381": -0.1063990353197863, "113": 0.03546634510659543, "152": -0.1063990353197863, "353": -0.03546634510659543, "46": -0.03546634510659543, "447": 0.14186538042638172, "205": 0.24826441574616803, "133": 0.03546634510659543, "147": 0.03546634510659543, "183": -0.17733172553297716, "132": -0.03546634510659543, "206": 0.1063990353197863, "293": -0.03546634510659543, "181": 0.03546634510659543, "252": 0.03546634510659543, "50": -0.03546634510659543 } }, { "id": "sat_annealer.py#3", "path": "sat_annealer.py", "chunk": 3, "text": "var, negated in item: value = assignment[var] if (not value) if negated else value: return True return False def count_satisfied(assignment: Sequence[bool], formula: Formula) -> int: return sum(1 for item in formula if clause_satisfied(assignment, item)) def brute_force_solutions(variables: int, formula: Formula) -> List[Tuple[bool, ...]]: solutions: List[Tuple[bool, ...]] = [] for assignment in product([False, True], repeat=variables): if count_satisfied(assignment, formula) == len(formula): solutions.append(tuple(bool(value) for value in assignment)) return solutions def anneal_sat(formula: Formula, config: AnnealerConfig) -> AnnealerResult: rng = np.random.default_rng(config.seed) best: Optional[AnnealerResult] = None total = len(formula) for restart in range(config.restarts): probabilities = rng.uniform(0.05, 0.95, size=config.variables) velocity = np.zeros(config.variables, dtype=np.float64) noise = config.noise last_energy = 0.0 for iteration in range(1, config.iterations + 1): last_energy, gradient = energy_and_gradient(probabilities, formula) pressure = config.binarize_pressure * (1.0 - 2.0 * probabilities) step_noise = rng.normal(0.0, noise, size=config.variables) velocity", "tokens": 143, "vector": { "200": -0.09138233247699076, "215": -0.09138233247699076, "344": -0.2741469974309723, "330": -0.13707349871548616, "205": 0.22845583119247692, "206": 0.2741469974309723, "403": -0.18276466495398153, "293": -0.04569116623849538, "381": -0.04569116623849538, "369": 0.18276466495398153, "181": 0.09138233247699076, "252": 0.09138233247699076, "459": 0.13707349871548616, "50": -0.09138233247699076, "447": 0.04569116623849538, "183": 0.22845583119247692, "115": -0.5026028286234492, "109": 0.09138233247699076, "265": 0.04569116623849538, "16": 0.18276466495398153, "132": -0.04569116623849538, "57": 0.04569116623849538, "122": 0.22845583119247692, "163": 0.09138233247699076, "251": -0.13707349871548616, "35": -0.13707349871548616, "424": -0.04569116623849538, "37": 0.04569116623849538, "113": 0.09138233247699076, "174": -0.04569116623849538, "315": 0.04569116623849538, "143": 0.04569116623849538, "29": 0.09138233247699076, "177": 0.13707349871548616, "266": -0.18276466495398153, "420": -0.04569116623849538, "279": -0.04569116623849538, "25": -0.04569116623849538, "399": -0.04569116623849538, "492": -0.04569116623849538, "276": 0.04569116623849538, "273": -0.04569116623849538, "58": 0.04569116623849538, "290": 0.09138233247699076, "270": 0.04569116623849538, "508": -0.13707349871548616, "495": -0.04569116623849538, "457": 0.04569116623849538, "173": -0.04569116623849538, "204": -0.09138233247699076, "125": 0.09138233247699076, "46": 0.04569116623849538, "182": -0.04569116623849538, "107": 0.04569116623849538, "239": 0.13707349871548616, "406": -0.09138233247699076, "170": 0.04569116623849538, "298": 0.04569116623849538, "27": -0.04569116623849538, "438": 0.04569116623849538, "178": -0.04569116623849538, "305": -0.04569116623849538 } }, { "id": "sat_annealer.py#4", "path": "sat_annealer.py", "chunk": 4, "text": "nt(probabilities, formula) pressure = config.binarize_pressure * (1.0 - 2.0 * probabilities) step_noise = rng.normal(0.0, noise, size=config.variables) velocity = 0.82 * velocity - config.learning_rate * (gradient + pressure) + step_noise probabilities = np.clip(probabilities + velocity, 0.0, 1.0) noise *= config.noise_decay assignment = collapse(probabilities) result_probabilities = ( np.array([0.0 if value else 1.0 for value in assignment], dtype=np.float64) if config.return_collapsed_vertices else probabilities ) result_energy, _ = energy_and_gradient(result_probabilities, formula) satisfied = count_satisfied(assignment, formula) result = AnnealerResult( assignment=assignment, phases=phase_angles(result_probabilities), probabilities=tuple(float(value) for value in result_probabilities), energy=float(result_energy), satisfied_clauses=satisfied, total_clauses=total, success=satisfied == total, iterations=iteration, restart=restart, ) if best is None or result.satisfied_clauses > best.satisfied_clauses or ( result.satisfied_clauses == best.satisfied_clauses and result.energy < best.energy ): best = result if result.success: return result if best is None: raise ValueError(\"annealer ", "tokens": 121, "vector": { "403": -0.29524069878307374, "508": -0.34444748191358604, "115": -0.14762034939153687, "438": 0.09841356626102458, "183": 0.24603391565256144, "178": -0.04920678313051229, "266": -0.24603391565256144, "177": 0.04920678313051229, "305": -0.04920678313051229, "239": 0.09841356626102458, "204": -0.04920678313051229, "122": 0.04920678313051229, "125": 0.14762034939153687, "164": -0.04920678313051229, "259": -0.04920678313051229, "27": -0.04920678313051229, "103": 0.09841356626102458, "396": -0.04920678313051229, "206": 0.24603391565256144, "147": 0.04920678313051229, "320": -0.19682713252204917, "219": 0.04920678313051229, "205": 0.19682713252204917, "381": -0.09841356626102458, "16": 0.04920678313051229, "344": -0.09841356626102458, "182": -0.04920678313051229, "107": 0.04920678313051229, "388": 0.04920678313051229, "28": -0.09841356626102458, "253": -0.14762034939153687, "50": -0.04920678313051229, "246": -0.34444748191358604, "29": 0.04920678313051229, "390": 0.04920678313051229, "46": -0.04920678313051229, "251": -0.04920678313051229, "374": 0.09841356626102458, "56": 0.14762034939153687, "167": 0.24603391565256144, "141": 0.04920678313051229, "273": -0.09841356626102458, "174": -0.09841356626102458, "298": 0.04920678313051229, "170": 0.04920678313051229, "58": 0.09841356626102458, "399": -0.29524069878307374, "506": -0.09841356626102458, "276": 0.09841356626102458, "369": -0.04920678313051229, "354": -0.04920678313051229, "484": 0.04920678313051229, "165": -0.04920678313051229 } }, { "id": "sat_annealer.py#5", "path": "sat_annealer.py", "chunk": 5, "text": "clauses == best.satisfied_clauses and result.energy < best.energy ): best = result if result.success: return result if best is None: raise ValueError(\"annealer ran no restarts\") return best def random_planted_3sat( variables: int, clauses: int, rng: np.random.Generator, planted: Optional[Sequence[bool]] = None, ) -> Tuple[Formula, Tuple[bool, ...]]: if variables < 3: raise ValueError(\"planted 3-SAT requires at least 3 variables\") assignment = ( tuple(bool(value) for value in planted) if planted is not None else tuple(bool(value) for value in rng.integers(0, 2, size=variables)) ) generated: List[Clause] = [] for _ in range(clauses): vars_ = rng.choice(variables, size=3, replace=False) lits: List[Literal] = [] for var in vars_: negated = bool(rng.integers(0, 2)) lits.append(literal(int(var), negated)) item = tuple(lits) if not clause_satisfied(assignment, item): flip = int(rng.integers(0, 3)) var, negated = item[flip] fixed = list(item) fixed[flip] = literal(var, not negated) item = tuple(fixed) generated.append(item) return tuple(generated), assignment def format_assignment(assignment: Sequence[bool]) -> str: return \"(\" + \", \".join(\"True\" if value else \"False\" for value in assignmen", "tokens": 163, "vector": { "130": -0.12382325151194941, "399": -0.206372085853249, "167": 0.0412744171706498, "354": -0.0412744171706498, "246": -0.1650976686825992, "56": 0.0825488343412996, "403": -0.24764650302389882, "174": -0.12382325151194941, "369": 0.1650976686825992, "506": -0.0825488343412996, "276": 0.12382325151194941, "484": 0.0825488343412996, "165": -0.0825488343412996, "103": 0.0412744171706498, "200": -0.12382325151194941, "147": -0.0412744171706498, "270": 0.0412744171706498, "459": 0.0825488343412996, "419": 0.0412744171706498, "122": 0.206372085853249, "109": 0.1650976686825992, "177": 0.1650976686825992, "266": -0.0412744171706498, "420": -0.0412744171706498, "401": -0.0412744171706498, "55": 0.1650976686825992, "492": -0.0412744171706498, "447": 0.0825488343412996, "183": -0.24764650302389882, "251": -0.2889209201945486, "115": -0.0412744171706498, "51": 0.0412744171706498, "441": 0.0412744171706498, "363": -0.0412744171706498, "206": 0.1650976686825992, "205": 0.24764650302389882, "16": 0.206372085853249, "344": -0.24764650302389882, "293": -0.12382325151194941, "381": -0.0825488343412996, "497": -0.12382325151194941, "204": -0.0825488343412996, "480": 0.12382325151194941, "163": 0.12382325151194941, "157": 0.0412744171706498, "290": 0.0412744171706498, "267": 0.0825488343412996, "463": 0.0412744171706498, "378": 0.0412744171706498, "252": 0.0825488343412996, "385": 0.12382325151194941, "386": -0.12382325151194941, "215": -0.1650976686825992, "330": -0.24764650302389882, "132": -0.0412744171706498, "78": 0.12382325151194941, "153": 0.12382325151194941, "421": -0.0412744171706498, "396": 0.0412744171706498, "181": 0.0412744171706498, "341": 0.0412744171706498 } }, { "id": "sat_annealer.py#6", "path": "sat_annealer.py", "chunk": 6, "text": "uple(generated), assignment def format_assignment(assignment: Sequence[bool]) -> str: return \"(\" + \", \".join(\"True\" if value else \"False\" for value in assignment) + \")\" def format_formula(formula: Formula) -> str: pieces = [] for item in formula: literals = [] for var, negated in item: literals.append((\"NOT \" if negated else \"\") + f\"x{var}\") pieces.append(\"(\" + \" OR \".join(literals) + \")\") return \" AND \".join(pieces)", "tokens": 52, "vector": { "392": 0.08838834764831843, "480": 0.08838834764831843, "206": 0.2651650429449553, "459": 0.17677669529663687, "421": -0.08838834764831843, "447": 0.08838834764831843, "183": -0.08838834764831843, "396": 0.17677669529663687, "369": 0.08838834764831843, "344": -0.5303300858899106, "181": 0.08838834764831843, "403": -0.17677669529663687, "205": 0.17677669529663687, "381": -0.17677669529663687, "252": 0.08838834764831843, "16": 0.2651650429449553, "296": 0.08838834764831843, "115": -0.2651650429449553, "182": 0.2651650429449553, "330": -0.17677669529663687, "37": 0.2651650429449553, "200": -0.17677669529663687, "215": -0.17677669529663687, "174": -0.17677669529663687, "293": -0.08838834764831843, "354": -0.08838834764831843 } }, { "id": "tests/test_benchmark_runner.py#0", "path": "tests/test_benchmark_runner.py", "chunk": 0, "text": "import tempfile import unittest from argparse import Namespace from pathlib import Path from run_benchmark import run class BenchmarkRunnerTests(unittest.TestCase): def test_tiny_profile_writes_expected_artifacts(self): with tempfile.TemporaryDirectory() as tmp: out_dir = Path(tmp) / \"bench\" args = Namespace( profile=\"tiny\", dimensions=None, branches=None, distractor_similarity=None, target_corruption=None, seeds_per_condition=None, target_index=0, seed=7, out_dir=out_dir, no_plots=True, verbose=False, ) run(args) self.assertTrue((out_dir / \"raw_trials.csv\").exists()) self.assertTrue((out_dir / \"aggregate.csv\").exists()) self.assertTrue((out_dir / \"report.md\").exists()) if __name__ == \"__main__\": unittest.main()", "tokens": 79, "vector": { "217": 0.363696483726654, "477": -0.14547859349066158, "223": -0.21821789023599236, "373": -0.21821789023599236, "427": 0.07273929674533079, "403": 0.07273929674533079, "64": 0.07273929674533079, "416": -0.07273929674533079, "194": -0.07273929674533079, "172": -0.14547859349066158, "264": 0.07273929674533079, "204": 0.07273929674533079, "342": 0.07273929674533079, "459": 0.07273929674533079, "32": -0.07273929674533079, "6": 0.29095718698132317, "262": 0.07273929674533079, "99": 0.07273929674533079, "500": -0.07273929674533079, "207": -0.14547859349066158, "153": -0.4364357804719847, "27": -0.07273929674533079, "265": 0.14547859349066158, "25": 0.0, "137": -0.07273929674533079, "112": -0.07273929674533079, "276": 0.363696483726654, "476": -0.07273929674533079, "175": 0.07273929674533079, "5": -0.07273929674533079, "185": -0.07273929674533079, "363": -0.07273929674533079, "181": 0.07273929674533079, "253": 0.07273929674533079, "252": 0.07273929674533079, "230": 0.21821789023599236, "205": -0.07273929674533079, "339": 0.14547859349066158, "173": -0.21821789023599236, "238": -0.07273929674533079, "132": -0.07273929674533079, "33": -0.07273929674533079, "34": -0.07273929674533079, "333": 0.07273929674533079, "13": -0.07273929674533079 } }, { "id": "tests/test_hops_memory_mcp.py#0", "path": "tests/test_hops_memory_mcp.py", "chunk": 0, "text": "import json import os import subprocess import sys import tempfile import unittest from pathlib import Path ROOT = Path(__file__).resolve().parents[1] SERVER = ROOT / \"plugins\" / \"hops-memory-fabric\" / \"scripts\" / \"hops_memory_mcp.py\" class HopsMemoryMcpTests(unittest.TestCase): def call_server(self, requests, store_path=None): payload = \"\\n\".join(json.dumps(request) for request in requests) + \"\\n\" env = None if store_path is not None: env = {**os.environ, \"HOPS_MEMORY_STORE\": str(store_path)} completed = subprocess.run( [sys.executable, str(SERVER)], cwd=str(ROOT / \"plugins\" / \"hops-memory-fabric\"), input=payload, capture_output=True, text=True, check=True, env=env, ) return [json.loads(line) for line in completed.stdout.splitlines() if line.strip()] def test_lists_memory_tools(self): responses = self.call_server( [ {\"jsonrpc\": \"2.0\", \"id\": 1, \"method\": \"initialize\", \"params\": {}}, {\"jsonrpc\": \"2.0\", \"id\": 2, \"method\": \"tools/list\", \"params\": {}}, ] ) names = {tool[\"name\"] for tool in responses[1][\"result\"][\"tools\"]} self.assertIn(\"hops_remember\", names) self.assertIn(\"hops_retrieve\", names) self.assertIn(\"hops_route_task\", names) def test_remember_and_retrieve(self): with tempfil", "tokens": 142, "vector": { "217": 0.31799936400190804, "258": 0.15899968200095402, "132": 0.105999788000636, "503": 0.105999788000636, "337": 0.105999788000636, "477": -0.052999894000318, "223": -0.105999788000636, "373": -0.052999894000318, "64": 0.052999894000318, "416": -0.105999788000636, "328": -0.15899968200095402, "253": 0.052999894000318, "108": -0.052999894000318, "179": -0.105999788000636, "402": 0.105999788000636, "152": 0.105999788000636, "192": -0.105999788000636, "173": 0.052999894000318, "396": 0.211999576001272, "366": 0.052999894000318, "264": 0.052999894000318, "227": -0.052999894000318, "342": 0.052999894000318, "459": 0.15899968200095402, "195": 0.105999788000636, "6": 0.370999258002226, "236": -0.105999788000636, "315": 0.15899968200095402, "276": 0.15899968200095402, "291": -0.105999788000636, "344": -0.211999576001272, "336": 0.052999894000318, "31": -0.105999788000636, "16": 0.15899968200095402, "439": -0.211999576001272, "403": -0.105999788000636, "506": -0.052999894000318, "293": -0.052999894000318, "76": -0.052999894000318, "352": -0.052999894000318, "333": 0.105999788000636, "172": -0.052999894000318, "162": 0.052999894000318, "367": -0.052999894000318, "201": 0.052999894000318, "116": -0.052999894000318, "181": 0.15899968200095402, "408": -0.052999894000318, "32": -0.052999894000318, "369": 0.052999894000318, "193": 0.052999894000318, "312": -0.15899968200095402, "99": -0.052999894000318, "80": -0.052999894000318, "470": -0.052999894000318, "202": 0.052999894000318, "483": -0.105999788000636, "231": 0.105999788000636, "421": 0.105999788000636, "91": 0.105999788000636, "71": -0.052999894000318, "418": -0.105999788000636, "505": -0.105999788000636, "163": 0.052999894000318, "174": -0.211999576001272, "380": -0.105999788000636, "386": 0.052999894000318, "246": -0.052999894000318, "426": 0.15899968200095402, "178": -0.052999894000318, "171": 0.052999894000318, "300": 0.052999894000318, "268": -0.052999894000318, "262": 0.052999894000318, "284": 0.052999894000318 } }, { "id": "tests/test_hops_memory_mcp.py#1", "path": "tests/test_hops_memory_mcp.py", "chunk": 1, "text": "ssertIn(\"hops_remember\", names) self.assertIn(\"hops_retrieve\", names) self.assertIn(\"hops_route_task\", names) def test_remember_and_retrieve(self): with tempfile.TemporaryDirectory() as tmp: responses = self.call_server( [ { \"jsonrpc\": \"2.0\", \"id\": 1, \"method\": \"tools/call\", \"params\": { \"name\": \"hops_remember\", \"arguments\": { \"key\": \"test_decision\", \"value\": \"ship_pilot_suite\", \"actor\": \"user\", \"trust\": \"high\", \"tool\": \"unittest\", }, }, }, { \"jsonrpc\": \"2.0\", \"id\": 2, \"method\": \"tools/call\", \"params\": { \"name\": \"hops_retrieve\", \"arguments\": {\"query\": \"test_decision\"}, }, }, ], store_path=Path(tmp) / \"live_memory.json\", ) retrieved = json.loads(responses[1][\"result\"][\"content\"][0][\"text\"]) self.assertTrue(retrieved[\"success\"]) self.assertEqual(retrieved[\"value\"], \"ship_pilot_suite\") if __name__ == \"__main__\": unittest.main()", "tokens": 78, "vector": { "450": -0.07856742013183861, "178": -0.15713484026367722, "174": -0.31426968052735443, "6": 0.4714045207910317, "426": 0.15713484026367722, "171": 0.15713484026367722, "300": 0.07856742013183861, "459": 0.07856742013183861, "268": -0.07856742013183861, "262": 0.07856742013183861, "477": -0.07856742013183861, "99": 0.07856742013183861, "500": -0.07856742013183861, "207": -0.15713484026367722, "483": -0.15713484026367722, "195": 0.07856742013183861, "231": 0.15713484026367722, "421": 0.15713484026367722, "91": 0.15713484026367722, "505": -0.15713484026367722, "382": 0.15713484026367722, "418": -0.15713484026367722, "386": 0.15713484026367722, "177": 0.15713484026367722, "44": 0.07856742013183861, "453": 0.15713484026367722, "205": 0.15713484026367722, "328": -0.15713484026367722, "336": 0.07856742013183861, "4": -0.07856742013183861, "247": 0.07856742013183861, "366": 0.07856742013183861, "380": -0.07856742013183861, "223": -0.15713484026367722, "424": -0.07856742013183861, "315": 0.07856742013183861, "416": -0.07856742013183861, "131": 0.07856742013183861, "258": 0.15713484026367722, "136": 0.23570226039551584, "193": 0.07856742013183861, "246": -0.07856742013183861, "237": -0.07856742013183861, "408": -0.07856742013183861, "230": 0.07856742013183861, "487": 0.07856742013183861, "403": -0.07856742013183861, "34": -0.07856742013183861, "333": 0.07856742013183861, "13": -0.07856742013183861 } }, { "id": "tests/test_hops_proof.py#0", "path": "tests/test_hops_proof.py", "chunk": 0, "text": "import unittest from run_hops_proof import run_proof class HopsProofTests(unittest.TestCase): def test_all_pilot_proof_cases_pass(self): rows, payload = run_proof(seed=7) self.assertEqual(len(rows), 4) self.assertTrue(payload[\"all_proved\"]) self.assertTrue(all(row[\"proved\"] for row in rows)) def test_portfolio_matches_exact_baseline(self): rows, _payload = run_proof(seed=7) portfolio = next(row for row in rows if row[\"domain\"] == \"Portfolio\") self.assertEqual(portfolio[\"hops_solution\"], \"[5, 10, 13, 14, 17]\") self.assertTrue(portfolio[\"proved\"]) if __name__ == \"__main__\": unittest.main()", "tokens": 69, "vector": { "217": 0.1424940999758193, "223": -0.21374114996372895, "373": 0.0, "349": 0.07124704998790965, "156": 0.21374114996372895, "264": 0.07124704998790965, "162": -0.07124704998790965, "342": 0.07124704998790965, "459": 0.1424940999758193, "200": 0.07124704998790965, "6": 0.49872934991536755, "444": -0.35623524993954825, "291": -0.1424940999758193, "25": -0.1424940999758193, "487": 0.1424940999758193, "113": 0.07124704998790965, "230": 0.21374114996372895, "382": -0.07124704998790965, "350": 0.07124704998790965, "355": -0.35623524993954825, "429": 0.1424940999758193, "16": 0.1424940999758193, "344": -0.1424940999758193, "381": 0.07124704998790965, "14": 0.2849881999516386, "454": 0.07124704998790965, "403": -0.1424940999758193, "498": 0.07124704998790965, "505": 0.07124704998790965, "74": 0.07124704998790965, "319": 0.07124704998790965, "389": 0.07124704998790965, "325": -0.07124704998790965, "34": -0.07124704998790965, "333": 0.07124704998790965, "13": -0.07124704998790965 } }, { "id": "tests/test_memory_fabric.py#0", "path": "tests/test_memory_fabric.py", "chunk": 0, "text": "import unittest from memory_fabric import ComplexSpace from run_memory_fabric import run_memory, run_mesh, run_provenance class MemoryFabricTests(unittest.TestCase): def test_fractal_memory_retrieves_across_tiers(self): rows = run_memory(ComplexSpace(1024)) self.assertEqual(len(rows), 4) self.assertTrue(all(row[\"success\"] for row in rows)) self.assertGreaterEqual( {row[\"tier\"] for row in rows}, {\"tier0_exact\", \"tier1_sparse\", \"tier2_hkvc\", \"tier3_rsm\"}, ) def test_provenance_accepts_user_and_rejects_agent(self): rows = run_provenance(ComplexSpace(1024)) by_case = {row[\"case\"]: row for row in rows} self.assertTrue(by_case[\"trusted_user_memory\"][\"success\"]) self.assertTrue(by_case[\"trusted_user_memory\"][\"accepted\"]) self.assertTrue(by_case[\"untrusted_agent_memory\"][\"success\"]) self.assertFalse(by_case[\"untrusted_agent_memory\"][\"accepted\"]) def test_mesh_routes_to_expected_nodes(self): rows = run_mesh(ComplexSpace(1024)) self.assertEqual(len(rows), 3) self.assertTrue(all(row[\"success\"] for row in rows)) if __name__ == \"__main__\": unittest.main()", "tokens": 108, "vector": { "217": 0.1380861926847485, "223": -0.1380861926847485, "373": -0.09205746178983235, "437": -0.04602873089491617, "355": -0.23014365447458085, "72": 0.04602873089491617, "89": -0.1380861926847485, "116": 0.09205746178983235, "59": 0.09205746178983235, "264": 0.04602873089491617, "6": 0.5983735016339102, "342": 0.04602873089491617, "459": 0.1380861926847485, "245": -0.04602873089491617, "444": -0.41425857805424554, "227": 0.1380861926847485, "487": 0.09205746178983235, "113": 0.09205746178983235, "230": 0.23014365447458085, "350": 0.09205746178983235, "174": -0.1841149235796647, "16": 0.1841149235796647, "344": -0.1841149235796647, "448": -0.04602873089491617, "226": 0.04602873089491617, "170": 0.04602873089491617, "239": 0.04602873089491617, "259": 0.04602873089491617, "33": 0.04602873089491617, "29": -0.23014365447458085, "187": 0.04602873089491617, "73": 0.09205746178983235, "7": 0.09205746178983235, "43": -0.09205746178983235, "302": -0.04602873089491617, "28": 0.04602873089491617, "403": -0.04602873089491617, "34": -0.04602873089491617, "333": 0.04602873089491617, "13": -0.04602873089491617 } }, { "id": "tests/test_memory_fabric.py#1", "path": "tests/test_memory_fabric.py", "chunk": 1, "text": "\": unittest.main()", "tokens": 2, "vector": { "223": -0.7071067811865475, "13": -0.7071067811865475 } }, { "id": "tests/test_ops_dashboard.py#0", "path": "tests/test_ops_dashboard.py", "chunk": 0, "text": "import tempfile import unittest from pathlib import Path from ops_agents import write_agents from ops_rag import build_index, query_index from run_ops_dashboard import build_dashboard class OpsDashboardTests(unittest.TestCase): def test_rag_index_answers_project_query(self): with tempfile.TemporaryDirectory() as tmp: index = build_index(Path(tmp)) hits = query_index(index, \"memory fabric plugin MCP paid pilot\", top_k=3) self.assertGreater(len(index[\"documents\"]), 0) self.assertEqual(len(hits), 3) self.assertTrue(any(\"memory\" in hit[\"text\"].lower() for hit in hits)) def test_agent_specs_include_kimi_agents(self): with tempfile.TemporaryDirectory() as tmp: payload = write_agents(Path(tmp)) self.assertEqual(payload[\"provider\"][\"model\"], \"@cf/moonshotai/kimi-k2.6\") self.assertEqual(payload[\"provider\"][\"binding\"], \"AI\") self.assertGreaterEqual(len(payload[\"agents\"]), 4) def test_dashboard_builds_static_site(self): with tempfile.TemporaryDirectory() as tmp: site = build_dashboard(Path(tmp), include_cloudflare=False) self.assertTrue(site.exists()) self.assertIn(\"HOPS Ops Command Center\", site.read_text(encoding=\"utf-8\")) self.assertTrue((site.parent / \"_worker.js\").exists()) self.assertIn", "tokens": 137, "vector": { "217": 0.26806023096526216, "477": -0.17870682064350812, "223": -0.08935341032175406, "373": -0.17870682064350812, "64": 0.04467670516087703, "416": -0.17870682064350812, "310": 0.04467670516087703, "205": 0.08935341032175406, "323": -0.04467670516087703, "243": 0.08935341032175406, "60": 0.08935341032175406, "194": -0.04467670516087703, "140": -0.13403011548263108, "264": 0.04467670516087703, "142": -0.04467670516087703, "342": 0.04467670516087703, "459": 0.13403011548263108, "401": 0.04467670516087703, "6": 0.5807971670914014, "262": 0.13403011548263108, "99": 0.22338352580438514, "500": -0.13403011548263108, "207": -0.26806023096526216, "27": -0.13403011548263108, "354": -0.13403011548263108, "192": -0.08935341032175406, "173": -0.04467670516087703, "94": -0.04467670516087703, "16": 0.0, "38": -0.04467670516087703, "7": -0.04467670516087703, "383": 0.04467670516087703, "37": -0.04467670516087703, "113": 0.13403011548263108, "155": 0.04467670516087703, "487": 0.13403011548263108, "230": 0.13403011548263108, "470": -0.04467670516087703, "344": -0.08935341032175406, "408": -0.04467670516087703, "396": -0.04467670516087703, "105": -0.04467670516087703, "291": -0.17870682064350812, "92": 0.08935341032175406, "147": 0.04467670516087703, "371": 0.04467670516087703, "228": -0.04467670516087703, "484": -0.08935341032175406, "257": -0.04467670516087703, "384": -0.04467670516087703, "50": 0.04467670516087703, "448": -0.04467670516087703, "236": -0.04467670516087703, "251": 0.17870682064350812, "208": -0.04467670516087703, "252": 0.04467670516087703, "426": 0.08935341032175406, "152": 0.04467670516087703, "169": 0.04467670516087703, "93": 0.04467670516087703, "497": -0.04467670516087703, "4": -0.04467670516087703, "232": -0.04467670516087703, "153": -0.04467670516087703, "184": 0.04467670516087703, "22": -0.04467670516087703 } }, { "id": "tests/test_ops_dashboard.py#1", "path": "tests/test_ops_dashboard.py", "chunk": 1, "text": "e(site.exists()) self.assertIn(\"HOPS Ops Command Center\", site.read_text(encoding=\"utf-8\")) self.assertTrue((site.parent / \"_worker.js\").exists()) self.assertIn(\"/api/query\", (site.parent / \"_worker.js\").read_text(encoding=\"utf-8\")) self.assertIn(\"env.AI.run\", (site.parent / \"_worker.js\").read_text(encoding=\"utf-8\")) if __name__ == \"__main__\": unittest.main()", "tokens": 47, "vector": { "251": 0.4508348173337161, "173": -0.18033392693348646, "6": 0.3606678538669729, "426": 0.2705008904002297, "152": 0.09016696346674323, "169": 0.09016696346674323, "93": 0.09016696346674323, "497": -0.09016696346674323, "4": -0.2705008904002297, "232": -0.2705008904002297, "153": -0.2705008904002297, "230": 0.09016696346674323, "484": -0.2705008904002297, "184": 0.2705008904002297, "22": -0.2705008904002297, "20": 0.09016696346674323, "424": -0.09016696346674323, "439": -0.09016696346674323, "50": 0.09016696346674323, "172": -0.09016696346674323, "403": -0.09016696346674323, "34": -0.09016696346674323, "333": 0.09016696346674323, "223": -0.09016696346674323, "13": -0.09016696346674323 } }, { "id": "tests/test_sat_annealer.py#0", "path": "tests/test_sat_annealer.py", "chunk": 0, "text": "import unittest from sat_annealer import AnnealerConfig, anneal_sat, brute_force_solutions, example_formula class SatAnnealerTests(unittest.TestCase): def test_example_collapses_to_expected_assignment(self): formula = example_formula() result = anneal_sat(formula, AnnealerConfig(variables=3, seed=7)) self.assertTrue(result.success) self.assertEqual(result.assignment, (True, False, True)) self.assertEqual(result.satisfied_clauses, result.total_clauses) self.assertEqual(result.energy, 0.0) def test_example_matches_brute_force_solution_set(self): formula = example_formula() result = anneal_sat(formula, AnnealerConfig(variables=3, seed=7)) solutions = brute_force_solutions(3, formula) self.assertEqual(solutions, [(True, False, True)]) self.assertIn(result.assignment, solutions) if __name__ == \"__main__\": unittest.main()", "tokens": 75, "vector": { "217": 0.12060453783110543, "223": -0.18090680674665816, "373": -0.060302268915552716, "359": 0.060302268915552716, "143": 0.18090680674665816, "315": 0.18090680674665816, "57": 0.12060453783110543, "423": -0.18090680674665816, "264": 0.060302268915552716, "147": -0.060302268915552716, "342": 0.060302268915552716, "459": 0.12060453783110543, "45": 0.060302268915552716, "6": 0.48241815132442173, "115": -0.3015113445777636, "246": -0.48241815132442173, "122": 0.12060453783110543, "25": -0.12060453783110543, "230": 0.060302268915552716, "174": -0.060302268915552716, "487": 0.24120907566221086, "206": 0.12060453783110543, "181": 0.24120907566221086, "252": 0.12060453783110543, "167": 0.060302268915552716, "141": 0.060302268915552716, "56": 0.060302268915552716, "162": 0.060302268915552716, "35": -0.18090680674665816, "426": 0.060302268915552716, "403": -0.060302268915552716, "34": -0.060302268915552716, "333": 0.060302268915552716, "13": -0.060302268915552716 } }, { "id": "wrangler-ops-dashboard.toml#0", "path": "wrangler-ops-dashboard.toml", "chunk": 0, "text": "name = \"hops-ops-dashboard\" pages_build_output_dir = \"results/ops_dashboard/site\" compatibility_date = \"2026-05-01\" [ai] binding = \"AI\" [vars] CF_AI_MODEL = \"@cf/moonshotai/kimi-k2.6\"", "tokens": 21, "vector": { "386": 0.2182178902359924, "152": 0.2182178902359924, "169": 0.2182178902359924, "358": 0.2182178902359924, "63": -0.2182178902359924, "448": -0.2182178902359924, "42": 0.2182178902359924, "251": 0.2182178902359924, "273": -0.2182178902359924, "21": -0.2182178902359924, "457": 0.2182178902359924, "403": 0.0, "50": 0.4364357804719848, "384": -0.2182178902359924, "271": 0.2182178902359924, "371": 0.2182178902359924, "228": -0.2182178902359924, "484": -0.2182178902359924, "257": -0.2182178902359924 } } ] }