Claude 92ce8f1e7f docs/research: expand Cluster A with non-distance autoresearch targets ... Cluster A previously listed only distance-kernel candidates (cosine, IVF partition selection, BM25 scoring), which understated the autoresearch opportunity in Lance. The single largest hot-cycle pile for analytical reads is the decode path in lance-encoding, not lance-linalg. Restructure Cluster A into three sub-groups, all sharing the autoresearch loop shape (single-agent, bit-exact oracle, seconds-scale eval, self-contained code) but differing in fixture shape: Distance kernels (lance-linalg): A1. Adjacent distance kernels (cosine, dot, hamming) A2. IVF partition-selection kernel A3. FTS BM25 scoring kernel Decode kernels (lance-encoding) - highest hot-cycle pile: A4. Bitpack integer decode (billions of values per analytical query; documented SIMD literature BP128 / simdcomp / Lemire bitpacking) A5. Dictionary decode (SIMD gather + prefetch wins on low-cardinality string columns) A6. FSST string decode (Tableau's 2x SIMD opportunity) Scan / merge kernels: A7. Take / gather (random-access reads; hot for ANN post-fetch) A8. Predicate / filter evaluation (per-type comparison kernels) A9. Posting list intersection (FTS AND queries; Lemire 2-5x SIMD wins) A10. Top-K k-way merge (every LIMIT / ANN query) Each new candidate notes why it's high-leverage, the documented SIMD opportunity if any, and the bit-exact oracle availability. Updates the cross-cluster prioritization to add a "largest absolute speedup on a real workload -> run A4" branch alongside the existing branches; notes that A1 and A4 can run in parallel by separate agents since they share loop shape but not scaffolding. scripts/check-agents-md.sh still passes (30/30 links). https://claude.ai/code/session_01Aq8kBUcjmEPobcEufnWbW5		2026-05-14 23:41:40 +00:00
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llm-evolutionary-sampling.md	docs/research: expand Cluster A with non-distance autoresearch targets	2026-05-14 23:41:40 +00:00