typhoon.git/src/docs, branch master

Learned FH% logistic-regression model (alpha full, beta whole-node-only)

2026-10-25T16:55:30Z

Replaces the data.c histogram tables (g_uJointFHThousandths/ g_uBetaFHThousandths and their _NNUE counterparts) with two quantized logistic regressions as the default FH%-prediction mechanism under FH_USE_REGRESSION_MODEL=1, trained against real collected search data (fhcollect.c) per docs/REGRESSION.md. Alpha side is a full per-move regression, validated against the old table (0.119pp weighted mean deviation across 10,684 matched cells). Beta side is whole-node-only (matching the old table's own scope -- no functionality lost), refit on the NO_MOVE population after an earlier mixed-tier fit saturated and over-pruned; its skip threshold (FH_BETA_SKIP_THRESHOLD=99000) was confirmed by direct self-play sweeps, not just ECM solve-count intuition (see docs/REGRESSION.md's "Category A vs B" discussion). Gated by an overnight SPRT match (regression vs HEAD tables, same NNUE eval, sd8): at 4120 games, llr=+2.53 (never dipped negative, peaked +2.8), score=0.517, candidate reaching comparable depth at higher nps/ebf than head. Did not cross the H1=+2.94 SPRT bound, but the trend across the full run is unambiguous and the marginal value of waiting out the remaining games was judged not worth it. The data.c tables are left in place as the inactive #else branch of FH_USE_REGRESSION_MODEL for now; a follow-up commit will flip the build default and remove them once this commit's head_reference baseline and ECM numbers are recorded. Co-Authored-By: Claude Sonnet 5

More aggressive move reductions; Wilson bound overrides static signal when available

2026-10-24T16:07:03Z

SPRT playtest (2000 games, sd/depth matched): -535 =875 +590, score=0.514, llr=+1.04 (floated around +1.0 the whole run, never went negative) toward H1=+2.94. Not a confirmed win, but searches meaningfully deeper (avg depth 14.35 vs 13.50, EBF 3.131 vs 3.305) at no measurable strength cost -- worth banking and continuing to accumulate games against. Co-Authored-By: Claude Sonnet 5

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

NNUE: re-fit raw-to-score calibration, NNU4 weight format, super-lazy default-on, lazy probe measured net-negative

2026-10-21T01:37:20Z

Fits an affine (a + b*raw) correction into every exported .nnue file (export_nnue_weights.py's CalibrateScoreMapping) so the engine's consumed centipawn output tracks Stockfish's real score scale rather than the net's nominal raw WDL-sigmoid output -- training variance alone shifts this mapping across retrains even with an identical recipe, so it's re-fit and baked in per export rather than assumed constant. Weight format bumped to "NNU4" to carry the two new floats; older NNU1/2/3 files are no longer readable. Super-lazy (material-only) pre-filter promoted to default-on (g_fUseNnueSuperLazy = TRUE) with a recalibrated per-material-bucket margin table (measured against the recalibrated net, 16-shard cross_sectional_200k.ep_ sample) -- real, measured nps win across all three curated suites. The separate lazy-probe gate (accumulator-based early exit) is measured net-negative on real search traffic at this net's probe-vs-forward latency ratio and stays off by default (g_fUseNnueLazyProbe = FALSE); code kept live since margin tuning could still flip this, per docs/NNUE.md. Also: direct per-call latency instrumentation for NnueProbeScore/ NnueForward (root.c, EVAL_TIME build) to make the lazy-probe breakeven math measurable directly instead of inferred; QSearch futility margins given real-data-derived NNUE_FULL/NNUE_LAZY starting points. Co-Authored-By: Claude Sonnet 5

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

Phase A NNUE: feature-indexing spec, annotation/training pipeline, dump nnuefeatures

2026-10-15T20:11:22Z

Builds out the NNUE Phase A prototype per docs/NNUE.md: - src/nnuefeatures.c: standalone HalfKAv2_hm feature-index reference implementation (new `dump nnuefeatures` command), sourced from Stockfish's half_ka_v2_hm.{h,cpp} (sf_16 tag), including the 0x88->64 square conversion typhoon's own board representation needs. - annotate_pgns/: sharded TWIC/self-play annotation harness (annotate.py), position extraction, quiet-position filtering (flat and game-shaped, plus an upstream --prefilter-quiet option), train/holdout game-level stratified split, WDL-sigmoid K fitting, the Python HalfKAv2_hm reference encoder (nnue_feature_spec.py) and its bit-for-bit cross-checker against the C side (cross_check_features.py), and a first PyTorch training pipeline (nnue_model.py, nnue_train.py, build_training_tensors.py) -- a 256x2-32-32-1-ish net trained locally on CPU against ~1.85M quiet TWIC positions as a pipeline/framework validation pass. - Finish an in-progress InitMagic/InitLMRTable/InitEval -> InitializeBitboardMagic/InitializeLMRTable/InitializeEval rename: chess.h's prototypes hadn't been updated to match the already-renamed definitions/call sites, leaving 4 implicit-function-declaration warnings. - tests/arasan.epd: new curated EPD suite. - docs/: add the project's design docs (previously untracked). Co-Authored-By: Claude Sonnet 5