Wire NNUE eval into search, build the training pipeline, and add a fast

2026-10-20T16:26:44Z

move/eval-agreement benchmark against Stockfish (tests/lop) Implements the architecture decisions in src/docs/NNUE.md: HalfKAv2_hm feature set, int16/int8 quantization, per-ply accumulator backup for king moves, and a lazy-eval-ready integration into search.c/eval.c. Adds the full annotate_pgns/ training pipeline (extraction, K-fit/ labeling, quiet filtering, tensor building, training, export) plus an end-to-end orchestrator (run_nnue_pipeline.py) and validation tooling (sanity_check_net.py, compare_vs_stockfish_net.py). Adds tests/lop: a 10k-position list-of-positions benchmark comparing engine eval rank and move choice against pre-computed Stockfish/lc0 references, much cheaper than a full self-play match for day-to-day progress checks. First sd10-matched self-play result against classic HCE: LLR +2.28 (H1=+2.94), -72=119+169 over 360 games, confirming the net is "smarter per node" at matched depth. A real-time (--st 4) match is currently weaker (candidate loses ~3 plies of depth to a ~3.3-3.7x nps gap against an unrecalibrated search), consistent with NNUE.md's adoption-criterion section -- pruning/reduction constants still need retuning for the new eval's score scale before a fair real-time comparison is possible. Co-Authored-By: Claude Sonnet 5

typhoon.git/tests/lop/lop_out_to_triplets.py, branch master

Wire NNUE eval into search, build the training pipeline, and add a fast