add baseline eval methodology + results

EVAL_RESULTS.md

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+# binary-reasoning-v1 — baseline eval (2026-10-28)
+
+Model: `qwen3.8-flash-next-iq3_s` (local reasoning model, temp 0, ≤2500 tok, served via
+llama.cpp-style OpenAI endpoint on this PC). Sample: 23 stratified deterministic items
+(no judge needed): 10 func_id_exact, 8 func_id_mc, 5 crossarch_equivalence. Graded by our own
+`grading.py` (the same code the Hub environment uses).
+
+| family | score | note |
+|---|---|---|
+| func_id_exact | 1/10 = 10% | guessing internal symbol names from ~608-lib universe |
+| func_id_mc | 2/8 = 25% | at random-guess baseline (4 options) |
+| crossarch_equivalence | 2/5 = 40% | includes easy frim/frip pairs + hard distractors |
+| **TOTAL** | **5/23 = 22%** | |
+
+## Interpretation (honest)
+- A capable reasoning model is **at or near chance level** on this pack — the tasks are
+  genuinely frontier-hard for models, which is the point: verifiable headroom for RL/RFT.
+- Sample n=23 is small; treat as a sanity baseline, not a leaderboard number.
+- Caveats: one local model only; some items graded from reasoning text when final content
+  was truncated (we note it rather than hide it). Reproduce with `mini_eval_win.py`
+  (harness in this folder; raw per-item output in `EVAL_raw.txt`).
+
+## What we will NOT claim
+No "SOTA" or cross-model leaderboard until run on 2-3 hosted models (Prime Inference or
+any OpenAI-compatible key makes this a 10-minute job — same harness, swap URL/model).