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ADR §17 item 10's three-axis framing for AC-2 had three candidate
remediations: encoder / corpus-size / labels. Items 10 and 12 ruled
out corpus-size and encoder. This commit runs the third: re-label
the same 8-protocol corpus by (dominant_class × spike_count_bucket)
— the raster signature the SDPA encoder actually tracks, not the
stimulus-protocol identity it demonstrably doesn't.
Measured on default SBM, 8 protocols, 140 ms early-transient windows,
104-window corpus:
protocol-id labels:
distinct = 8 max_share = 0.12 precision@5 = 0.062 (below random 0.125)
raster-regime labels:
distinct = 2 max_share = 0.92 precision@5 = 1.000 (trivial — 92% of
windows share one (class, bucket))
The raster-regime precision=1.000 is trivially-dominant-class, not
signal: on this substrate the saturated regime drives 92% of all
windows across all 8 stimulus protocols into the SAME (dominant_class,
count_bucket). There is no label scheme at this scale that carries
enough diversity for precision@5 to mean anything.
Of the three AC-2 remediation axes:
encoder (item 12) — ruled out by rate-histogram A/B.
corpus (item 10) — ruled out by 8-protocol expansion.
labels (this) — ruled out by raster-regime monoculture.
**Substrate is the sole remaining AC-2 lever.** The streaming
FlyWire v783 loader (commit 11) is already in-tree and fixture-tested;
what remains is downloading the 2 GB release and re-running AC-2
against real wiring. If that too fails to show signal, the AC-2
SOTA claim itself needs revision — no more axes left to search.
Changes:
- src/analysis/types.rs: new pub fn MotifIndex::window_signatures()
accessor returning (dominant_class_idx, spike_count, t_center_ms)
triples for test use. Alongside the existing vectors() accessor.
- tests/ac_2_raster_regime_labels.rs: new diagnostic test.
Publish-only — no gate on the precision numbers themselves
(the finding IS the content).
- ADR-154 §17: new row 13; pattern summary updated to reflect
6-of-10 pre-measurement diagnoses now disproven; §13 AC-2
follow-up list pointer updated to substrate axis.
All prior tests still green. No source-code regression.
Co-Authored-By: claude-flow <ruv@ruv.net>
EOF
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| agentic-jujutsu | ||
| apify | ||
| app-clip | ||
| benchmarks | ||
| boundary-discovery | ||
| brain-boundary-discovery | ||
| climate-consciousness | ||
| cmb-boundary-discovery | ||
| cmb-consciousness | ||
| connectome-fly | ||
| data | ||
| decompiler-dashboard | ||
| delta-behavior | ||
| dna | ||
| docs | ||
| dragnes | ||
| earthquake-boundary-discovery | ||
| ecosystem-consciousness | ||
| edge | ||
| edge-full/pkg | ||
| edge-net | ||
| exo-ai-2025 | ||
| frb-boundary-discovery | ||
| gene-consciousness | ||
| google-cloud | ||
| graph | ||
| gw-consciousness | ||
| health-boundary-discovery | ||
| infrastructure-boundary-discovery | ||
| market-boundary-discovery | ||
| meta-cognition-spiking-neural-network | ||
| mincut | ||
| music-boundary-discovery | ||
| neural-trader | ||
| nodejs | ||
| onnx-embeddings | ||
| onnx-embeddings-wasm | ||
| OSpipe | ||
| pandemic-boundary-discovery | ||
| prime-radiant | ||
| pwa-loader | ||
| quantum-consciousness | ||
| real-eeg-analysis | ||
| real-eeg-multi-seizure | ||
| refrag-pipeline | ||
| robotics | ||
| rust | ||
| ruvLLM | ||
| rvf | ||
| rvf-desktop | ||
| rvf-kernel-optimized | ||
| scipix | ||
| seizure-clinical-report | ||
| seizure-therapeutic-sim | ||
| seti-boundary-discovery | ||
| seti-exotic-signals | ||
| spiking-network | ||
| subpolynomial-time | ||
| temporal-attractor-discovery | ||
| train-discoveries | ||
| ultra-low-latency-sim | ||
| verified-applications | ||
| vibecast-7sense | ||
| void-boundary-discovery | ||
| vwm-viewer | ||
| wasm/ios | ||
| wasm-react | ||
| wasm-vanilla | ||
| weather-boundary-discovery | ||
| bounded_instance_demo.rs | ||
| README.md | ||
| security_hardened.rvf | ||
RuVector Examples
Comprehensive examples demonstrating RuVector's capabilities across multiple platforms and use cases.
Directory Structure
examples/
├── rust/ # Rust SDK examples
├── nodejs/ # Node.js SDK examples
├── graph/ # Graph database features
├── wasm-react/ # React + WebAssembly integration
├── wasm-vanilla/ # Vanilla JS + WebAssembly
├── agentic-jujutsu/ # AI agent version control
├── exo-ai-2025/ # Advanced cognitive substrate
├── refrag-pipeline/ # Document processing pipeline
└── docs/ # Additional documentation
Quick Start by Platform
Rust
cd rust
cargo run --example basic_usage
cargo run --example advanced_features
cargo run --example agenticdb_demo
Node.js
cd nodejs
npm install
node basic_usage.js
node semantic_search.js
WebAssembly (React)
cd wasm-react
npm install
npm run dev
WebAssembly (Vanilla)
cd wasm-vanilla
# Open index.html in browser
Example Categories
| Category | Directory | Description |
|---|---|---|
| Core API | rust/basic_usage.rs |
Vector DB fundamentals |
| Batch Ops | rust/batch_operations.rs |
High-throughput ingestion |
| RAG Pipeline | rust/rag_pipeline.rs |
Retrieval-Augmented Generation |
| Advanced | rust/advanced_features.rs |
Hypergraphs, neural hashing |
| AgenticDB | rust/agenticdb_demo.rs |
AI agent memory system |
| GNN | rust/gnn_example.rs |
Graph Neural Networks |
| Graph | graph/ |
Cypher queries, clustering |
| Node.js | nodejs/ |
JavaScript integration |
| WASM React | wasm-react/ |
Modern React apps |
| WASM Vanilla | wasm-vanilla/ |
Browser without framework |
| Agentic Jujutsu | agentic-jujutsu/ |
Multi-agent version control |
| EXO-AI 2025 | exo-ai-2025/ |
Cognitive substrate research |
| Refrag | refrag-pipeline/ |
Document fragmentation |
Feature Highlights
Vector Database Core
- High-performance similarity search
- Multiple distance metrics (Cosine, Euclidean, Dot Product)
- Metadata filtering
- Batch operations
Advanced Features
- Hypergraph Index: Multi-entity relationships
- Temporal Hypergraph: Time-aware relationships
- Causal Memory: Cause-effect chains
- Learned Index: ML-optimized indexing
- Neural Hash: Locality-sensitive hashing
- Topological Analysis: Persistent homology
AgenticDB
- Reflexion episodes (self-critique)
- Skill library (consolidated patterns)
- Causal memory (hypergraph relationships)
- Learning sessions (RL training data)
- Vector embeddings (core storage)
EXO-AI Cognitive Substrate
- exo-core: IIT consciousness, thermodynamics
- exo-temporal: Causal memory coordination
- exo-hypergraph: Topological structures
- exo-manifold: Continuous deformation
- exo-exotic: 10 cutting-edge experiments
- exo-wasm: Browser deployment
- exo-federation: Distributed consensus
- exo-node: Native bindings
- exo-backend-classical: Classical compute
Running Benchmarks
# Rust benchmarks
cargo bench --example advanced_features
# Refrag pipeline benchmarks
cd refrag-pipeline
cargo bench
# EXO-AI benchmarks
cd exo-ai-2025
cargo bench
Related Documentation
License
MIT OR Apache-2.0