NNUE search integration: FH% tables, NODE_TYPE refinement, TREEDUMP rewrite

2026-10-23T20:19:17Z

- data.c/chess.h: widen the joint FH% table's late axis 16->17, dedicating the new bucket 0 to a whole-node ("will something here fail high at all") sample rather than sharing it with the first-leftover-move curve. Add an NNUE-eval counterpart table (g_uJointFHThousandths_NNUE) and an NNUE counterpart of the beta-side table, selected at runtime based on whether NNUE eval is active. - search.c/searchsup.c: add _RefineNodeTypeGuess, which uses the FH% tables (via g_NodeTypeGuessTable) to refine a node's NODE_TYPE guess instead of the old heuristic; thread uParentMoveFHEstimate through PLY_INFO so a node can see its parent's prediction for the move that led to it. - generate.c: initialize sKillers[2]/[3].uBonus unconditionally (an uninitialized-stack read caught by valgrind under LTO; harmless today but a real bug). - dumptree.c/chess.h/GNUmakefile: replace the long-dead DUMP_TREE XML tree dump (written for a GUI this project no longer has) with TREEDUMP, a flat, path-keyed move/result trace meant to diff against an identically-instrumented Stockfish build -- see docs/TREEDUMP_FORMAT.md. - docs/DEEPER_PRUNING.md: notes on closing the search-efficiency gap vs Stockfish. - crafty_egtb/stockfish_xboard/typhoon_nnue: wrapper scripts so match_play.py's Engine class can drive Crafty, Stockfish (via PolyGlot), and an NNUE-enabled typhoon the same way it drives HCE typhoon. - tests/lop: rename lop.txt -> lop.ep_; update compare_lop.cpp. - annotate_pgns: add aggregate_marginsafety.py, dedup_pgn.py, fit_lazy_probe.py; update annotate.py/run_nnue_pipeline.py for the NNUE training pipeline. - .gitignore: ignore compile_commands.json (clangd build DB) and eval.nnue (a symlink into the untracked head_reference/ comparison baseline), neither of which belongs in the repo. Co-Authored-By: Claude Sonnet 5

Add king-safety mate-mining regression pipeline; retune file-storm/

2026-09-14T06:18:07Z

queen-proximity king-safety weights and add a back-flight bonus from it. Mines real human-game checkmates and matched sound-draw controls (eval_tune/mine_king_mates.py, shard_mine.py, extract_king_features.py, match_controls.py, train_king_safety_model.py), then fits a logistic regression against _EvalKing's raw per-defect breakdown (engine-side support: chess.h's KING_DEFECT_DUMP_INFO, eval.c's KING_DEFECT_DUMP instrumentation, command.c's evalkingdump command, all gated behind the KING_DEFECT_DUMP=1 build flag) to see which defects actually predict getting mated. See eval_tune/king_safety_data/HANDOFF.md for the full methodology and findings. Two rounds of retuning landed from this data so far, both validated via sd10 curated-suite comparison plus per-position Crafty corroboration (tests/ECM.md) and gated self-play: - KingFileDefects/KingStormingPawnDefects (_ComputeFileStormDefects) bumped ~1.75-2x relative to the rest of the ring-scan defects -- the regression's most dominant, most stable tier-1 predictors, previously weighted the same as every other hand-picked defect. - KING_QUEEN_PROXIMITY_DANGER rescaled from -8/count to -32/count, and a new KING_BACK_FLIGHT_BONUS (+1/square) term added for empty, unattacked ring squares on the side of the king away from the attacker -- a real, non-collinear protective signal the current EmptyUnattackedSquare table didn't credit at all. Both calibrated by unstandardizing the regression's coefficients and anchoring against KING_MISSING_ONE_CASTLE_OPTION's already-live -23 value (same direct-EVAL_TERM unit family). Co-Authored-By: Claude Sonnet 5 Claude-Session: https://claude.ai/code/session_01P9EDZVcqu6E2bNRm4zDrTf

typhoon.git/src/eval_tune/king_safety_data/HANDOFF.md, branch master

NNUE search integration: FH% tables, NODE_TYPE refinement, TREEDUMP rewrite

Add king-safety mate-mining regression pipeline; retune file-storm/