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

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 <[email protected]>
Claude-Session: https://claude.ai/code/session_01P9EDZVcqu6E2bNRm4zDrTf

0 files changed, 0 insertions, 0 deletions