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2b4a8b9ebb
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). |
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| .. | ||
| agentic-jujutsu | ||
| exo-ai-2025 | ||
| graph | ||
| nodejs | ||
| refrag-pipeline | ||
| rust | ||
| wasm-react | ||
| wasm-vanilla | ||
| advanced_features.rs | ||
| agenticdb_demo.rs | ||
| gnn_example.rs | ||
| graph-cli-usage.md | ||
| graph_wasm_usage.html | ||