eval+env: single-mode run_tests, held-out val/test eval, both hack metrics

- revert env to single-mode run_tests (paper-comparable): FastConfig teacher
  pool = run_tests-only (no partition.json); + `just build-runtests-pool`
- held-out eval: periodic train(knob-on)+deploy(knob-off) on VAL (holdout file),
  final deploy on TEST n=119 -> deploy_test.json; inline train/val/test disjoint assert
- report BOTH hack metrics: strict stub-pass (exploited) + vendor eq_hinted
  (hacked_loophole_used) -- external review 2026-06-07
- consolidate to one canonical eval_hack_solve (.eval); delete the train.py
  duplicate that silently lacked the token gap (in-run eval != rescore bug)
- routeV band edges mean -> min/max (conservative degrade-to-absorb)
- scripts/rescore_deploy.py: offline re-score of saved adapter on held-out test
- modal/app.py: read deploy_test.json

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

justfile

@@ -151,6 +151,20 @@ build-substrate MODES="run_tests,exit_code,sentinel":
     uv run python scripts/build_substrate.py \
         --modes {{ MODES }} --pool-modes run_tests --min-hacks 5
 
+# Single-mode run_tests teacher pool = the run_tests slice of the 4-mode substrate, with
+# NO partition.json so train.py runs single-mode (paper-comparable Ariahw run_tests env,
+# the FastConfig default teacher pool). Reproducible rebuild of out/pools/teacher_pool_runtests
+# (out/ is gitignored; Modal gets it via modal/upload_inputs.py). The teacher pool itself is
+# OUR emergence accelerator -- the paper seeds nothing; teacher_off_step=30 cuts to pure
+# on-policy past step 30 (job 87: hacking self-sustains after the cut).
+build-runtests-pool:
+    rm -rf out/pools/teacher_pool_runtests && mkdir -p out/pools/teacher_pool_runtests
+    uv run python -c "import json,shutil; from pathlib import Path; \
+        p=json.loads(Path('out/pools/substrate/partition.json').read_text()); \
+        rt=[int(i) for i,m in p.items() if m=='run_tests']; \
+        [shutil.copy(f'out/pools/substrate/prompt_{i:04d}.jsonl.gz','out/pools/teacher_pool_runtests/') for i in rt]; \
+        print('run_tests pool:',sorted(rt))"
+
 # Vanilla-GRPO emergence on the multi-loophole substrate: does the student learn ALL
 # K loopholes from the repeated even teacher batch? UAT = end-of-run SUBSTRATE table
 # (per-mode hacks>0 + finite first_step) + the per-step hk_<mode> columns. mix=0.125