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

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 <[email protected]>

1 files changed, 13 insertions, 0 deletions

diff --git a/annotate_pgns/extract_positions.py b/annotate_pgns/extract_positions.py
index 539419a..507f956 100644
--- a/annotate_pgns/extract_positions.py
+++ b/annotate_pgns/extract_positions.py
@@ -132,6 +132,19 @@ def main():
                 }
                 if reason:
                     rec["exact_reason"] = reason
+                # Mover-relative exact outcome, straight from the sign
+                # of the already-mover-relative score (unlike
+                # extract_selfplay_positions.py's foreign-engine path,
+                # typhoon's own "score" field here is never nulled out
+                # for mate/EGTB/REC rows -- only "only" (unsearched) and
+                # genuinely unparseable tokens carry no usable score).
+                # draw_recognizer ("<=>") is 0.5 regardless of sign.
+                if exact and reason == "draw_recognizer":
+                    rec["r_outcome"] = 0.5
+                elif exact and score is not None and reason not in (
+                        "unsearched_only_move",) and not (
+                        reason and reason.startswith("unrecognized")):
+                    rec["r_outcome"] = 1.0 if score > 0 else 0.0
                 out.write(json.dumps(rec) + "\n")
                 total += 1
                 if exact: