The fine-tuned 7B (bfm-match-7b-v2) drafted real OPEN ov_SC01_077 stubs; whole-binary gate banked 4
(func_80160B34 func_8015CC74 func_8016084C func_801705C0). Sample: 9/22 match_one proxy -> 4/22
whole-binary banked (18%; the proxy->gate gap is the TU-plumbing wall). Model is format-robust (raw .s
== normalized). api_draft: NORMALIZE_ASM bridge (unused — model handles raw .s) + ghidra_c-empty fix.
Gives the no-tool local model the context the agents read: common.h, live matching cookbook
(COOKBOOK_FULL toggle), 2 byte-matched corpus examples. Qwen3.6-35B-A3B result: harness fixes
compile-fails but model stays stuck at fixed near-misses; FULL cookbook worse+2.3x slower than
curated (dilution, gate-confirmed). Bottleneck is model refinement, not context.
docs/gen2-mips-matching-model.md: the BFM/gcc-2.7.2 matching-specialist idea (LoRA on our own
gate-verified pairs — the corpus off-the-shelf RE LLMs lack). export_pairs.py mines 1307 banked
(asm<->C) pairs from build objects (asm/ is gitignored, so disasm the ROM-identical build, splat-like
format) + src defs -> datasets/match_pairs/{pairs,train,test}.jsonl (gitignored, 1174/133 split).
api_draft.py: TEMP env-tunable. .gitignore: datasets/ models/ weights.
Script equivalent of one worker_wave drafter for any OpenAI-compatible endpoint (LM Studio /
llama.cpp / vLLM / OpenRouter). Inlines asm+ghidra_c+toolkit, calls /chat/completions, extracts C,
iterates against match_one (diff fed back, keep best). Output scores as a 'local'/'glm' arm via
ab_score.py. Env: API_BASE/API_KEY/MODEL. Logic self-tested; HTTP is standard OpenAI format.
Drew T