V3 Performance Optimization

Use the AttentionBenchmarks class to run standard and Flash Attention over sequences of 512 to 4096 tokens, then compare execution time and memory. The benchmark reports speedup per sequence length and checks it against the 2.49x-7.47x target range.

The MemoryBenchmarks class times both the current linear search and AgentDB HNSW search over 10,000 generated queries, then computes the improvement ratio. Results are validated against the 150x-12,500x target range with sub-100ms latency for 1M+ entries.

Targets include 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvement via HNSW indexing, 50-75% memory reduction, under 500ms cold start, under 0.05ms SONA adaptation, and efficient 15-agent swarm coordination.

The PerformanceRegression class runs the full benchmark and compares each metric against a stored baseline. Any metric degrading more than 5% is flagged with its regression percentage, severity classification, and generated fix recommendations.

Yes, the documentation lists npm scripts for targeted runs such as benchmark:flash-attention, benchmark:agentdb-search, and benchmark:memory-optimization, alongside the full benchmark:v3 suite and a continuous monitor:performance command.