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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 | |
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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 | ||