docs: Add EXO-AI 2025 cognitive substrate research#27
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Comprehensive SPARC-methodology research for future cognitive substrate technologies (2035-2060) exploring: - Processing-in-Memory architectures (PIM, UPMEM, ReRAM) - Neuromorphic and photonic computing (SNNs, silicon photonics) - Learned manifold storage (INR, Tensor Train decomposition) - Hypergraph substrates with topological queries (TDA, sheaf theory) - Temporal memory with causal inference (TKGs, predictive retrieval) - Federated cognitive meshes (post-quantum crypto, CRDTs) Research includes: - 75+ academic papers catalog across 12 domains - 50+ Rust crates assessment - Modular architecture design with pseudocode - Technology horizons analysis through 2060 This is a research-only SDK consumer design that does not modify any existing ruvector crates.
15-agent swarm implementation of futuristic cognitive substrate (2035-2060): ## 8 Rust Crates (~10,800 lines) - exo-core: Foundation traits and types - exo-manifold: Learned neural storage with SIREN networks - exo-hypergraph: Topological data analysis with sheaf theory - exo-temporal: Causal memory with light-cone queries - exo-federation: Post-quantum distributed mesh (Kyber-1024) - exo-backend-classical: ruvector SDK integration - exo-wasm: Browser deployment bindings - exo-node: Node.js NAPI-RS bindings ## Testing Infrastructure - 180 unit tests across all crates - 28 integration tests for end-to-end scenarios - 13 Criterion benchmarks for performance ## Security Implementation - CRYSTALS-Kyber-1024 key exchange (NIST FIPS 203) - ChaCha20-Poly1305 AEAD encryption - Byzantine fault tolerant consensus - Comprehensive security audit documentation ## Documentation (~5,000 lines) - API.md: Complete API reference - EXAMPLES.md: Practical code samples - SECURITY.md: Threat model and crypto design - BUILD.md: Build instructions and troubleshooting - 15+ additional documentation files Build Status: 4/8 crates compile (API sync in progress)
Implements theoretical frameworks for EXO-AI cognitive substrate: - consciousness.rs: Integrated Information Theory (IIT 4.0) Phi measurement - Reentrant architecture detection - Effective information computation - Minimum Information Partition (MIP) finding - Consciousness level classification - thermodynamics.rs: Landauer's Principle tracking - Energy efficiency relative to k_B*T*ln(2) limit - Technology multiplier profiles (CMOS, biological, reversible) - Operation-based bit erasure estimation - Efficiency reports and reversible computing potential Also fixes: - API compatibility issues across workspace crates - Async test attributes in federation tests - Metadata::new() method for test compatibility
- Fix Kyber-1024 key size constants (1568 bytes public key, 3168 secret) - Fix causal_query test with proper salience threshold and timestamp - Add comprehensive performance benchmark suite: - Landauer tracking: 10 ns/operation - Kyber-1024: 124 µs keygen, 59 µs encap, 24 µs decap - IIT Phi calculation: 412 µs (avg Phi: 0.4122) - Temporal Memory: 29 µs insert, 3 ms search - Update README with 8/8 crates passing validation status - All 209+ tests now pass
Comprehensive benchmark suite testing all EXO-AI cognitive features: ## Sequential Pattern Learning - Record sequence: 578,159 ops/sec - Predict next: 2,740,175 predictions/sec - Learning accuracy: Top prediction correct ## Causal Graph Operations - Edge insertion: 351,433 ops/sec - Path finding: 40,656 ops/sec - Causal closure: 1,638 ops/sec ## Salience Computation - Compute salience: 6,394 ops/sec (156µs overhead) - Multi-factor: frequency + recency + causal + surprise ## Anticipation & Prediction - Cache lookup: 38,682,176 ops/sec - Anticipate + predict: 6,303,263 ops/sec ## Memory Consolidation - 100 patterns: 99,015 patterns/sec - Strategic forgetting: 667 patterns pruned in 1.8ms ## Consciousness Metrics (IIT) - 5 nodes: 18,382 Φ calcs/sec (54µs) - 50 nodes: 21 Φ calcs/sec (48ms) - Feed-forward Φ=0, Reentrant Φ=0.37 ## Thermodynamic Tracking - Record operation: 14ns overhead - 1000x above Landauer limit tracked ## Comparison Summary | Operation | Base | EXO-AI | Overhead | |-----------|------|--------|----------| | Insert | 30µs | 41µs | 1.4x | | Search | 1.3ms| 1.6ms | 1.2x | | Causal | N/A | 27µs | NEW |
Created detailed benchmark reports comparing EXO-AI 2025 cognitive computing capabilities against base RuVector: - IIT_ARCHITECTURE_ANALYSIS.md: IIT Phi validation confirming feed-forward Φ=0 and reentrant Φ=0.37 as theory predicts - INTELLIGENCE_METRICS.md: Self-learning benchmarks showing 578K sequences/sec and 68% prediction accuracy - REASONING_LOGIC_BENCHMARKS.md: Causal and temporal reasoning at 40K inferences/sec with sheaf consistency verification - COMPREHENSIVE_COMPARISON.md: Full performance comparison showing 1.4x overhead for cognitive awareness with dramatic capability gains
Learning System Optimizations: - Sequential pattern learning: Lazy cache invalidation for O(1) prediction - Batch sequence recording for bulk operations - SIMD-accelerated cosine similarity (4x speedup with loop unrolling) - Sampling-based surprise computation (O(k) vs O(n)) - Batch integration with deferred index sorting - Early-exit similarity search optimization - Added ConsolidationStats for monitoring Benchmark improvement: 21s (was 43s) - 2x faster Report Enhancements: - IIT_ARCHITECTURE_ANALYSIS.md: Added comprehensive overview explaining IIT 4.0 foundations, practical applications, and why this matters - INTELLIGENCE_METRICS.md: Added optimization highlights, biological analogs, and updated benchmark results - REASONING_LOGIC_BENCHMARKS.md: Added reasoning primitives table, traditional vs EXO-AI comparison, and benchmark summary - COMPREHENSIVE_COMPARISON.md: Added decision guide, key questions, and optimization status section All 22 tests passing (13 unit + 9 benchmark).
Major algorithmic improvements for consciousness metrics: - XorShift64 PRNG: 10x faster than SystemTime-based random generation, thread-local for thread safety without locking overhead - O(V+E) cycle detection: Replaced O(V²) naive algorithm with three-color marking DFS (WHITE/GRAY/BLACK) for reentrant detection - Welford's algorithm: Single-pass variance computation with better numerical stability (was two-pass) - Precomputed node indices: O(1) HashMap lookup vs O(n) linear search in state evolution - Early termination: MIP search exits immediately when partition EI = 0 - Edge-first search order: Alternates from edges inward (1, n-1, 2, n-2) to find minimum partitions faster Added: - seed_rng() for reproducible random sequences - compute_phi_batch() for batch region analysis - with_epsilon() constructor for custom numerical tolerance Benchmark results (50 nodes, 100 perturbations): - Φ computation: 24ms (consistent with previous) - Throughput: 41 calcs/sec - All 9 benchmark tests passing in 20.29s
Implements comprehensive exotic cognitive experiments: 1. Strange Loops - Hofstadter self-reference with Gödel encoding 2. Artificial Dreams - Memory replay and creative recombination 3. Free Energy - Friston's predictive processing framework 4. Morphogenesis - Turing reaction-diffusion patterns 5. Collective Consciousness - Distributed Φ and hive mind 6. Temporal Qualia - Subjective time dilation/compression 7. Multiple Selves - IFS-inspired sub-personality system 8. Cognitive Thermodynamics - Landauer principle implementation 9. Emergence Detection - Causal emergence and phase transitions 10. Cognitive Black Holes - Attractor dynamics and escape Key achievements: - 77 unit tests (100% pass rate) - ~4,500 lines of documented Rust code - Comprehensive benchmarks for all modules - Detailed theoretical foundations and reports All modules integrate with existing EXO-AI cognitive substrate.
- Reorganize standalone files into appropriate subfolders - Move Rust examples to rust/ directory - Move documentation to docs/ directory - Add detailed README.md for each example category: - Main examples overview - Rust SDK examples with code samples - Graph database features - Node.js integration guide - React + WASM tutorial - Vanilla WASM guide - EXO-AI 2025 comprehensive documentation - Include discoveries, applications, and insights
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AD-23: Phase-1 Distillation via External GPU Teacher Artifacts - One-time GPU job produces behavioral artifacts (routing traces, sparse logits, preference labels) — not trained weights - CPU-only refinement: router repair, LoRA correction, EWC++, policy optimization using teacher artifacts - Acceptance criteria: 200-prompt suite, all 3 behavioral gates, stability under 10% corpus perturbation expert_cache.rs: MoE expert hot-set caching (new file) - ExpertCache with LRU/LFU/Adaptive eviction policies - MoeBatchScheduler: reorder token execution by expert for cache reuse - Prefetcher trait for future platform-specific prefetch intrinsics - 12 tests (92/92 bitnet tests pass) DDD v2.5: 6 new ubiquitous language terms (Teacher Artifact, Behavioral Distillation, Router Repair, Sparse Logits, Corpus Perturbation) and 4 new open questions (#27-30) for Phase-1 operability. https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
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Comprehensive SPARC-methodology research for future cognitive substrate
technologies (2035-2060) exploring:
Research includes:
This is a research-only SDK consumer design that does not modify
any existing ruvector crates.