feat: eval_ablate_every default 5 (deploy-eval on for every arm) + workshop artifact tracker

- deploy hack/solve is now the headline metric for all arms, so turn the
  mid-train deploy-eval on by default (smoke now covers the deploy path too);
  200-step runs pass a sparser cadence explicitly.
- docs/spec/20260602_writeup_spec.md: durable A1-A7 paper-artifact tracker
  (keynote fig+table, ablation table, long-run fig, generalisation, appendix).

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

docs/spec/20260602_writeup_spec.md

@@ -0,0 +1,153 @@
+# Writeup spec -- gradient routing vs RL reward hacking
+
+Status: drafting harness. Numbers are placeholders until the queued jobs land
+(see "Run -> artifact map" below). This is the spec for the LW blog post and a
+possible workshop paper; it pins the narrative, the claims, and which run fills
+each table cell so we can drop numbers in instead of re-deciding the story.
+
+Venue order: LW blog first (the audience that read AntiPaSTO and the Ariahw
+post), then a workshop paper (NeurIPS/ICLR alignment or interpretability
+workshop) if the n=3 route2-vs-vanilla deploy gap holds and at least one
+red-team control (random-V, placebo) comes back clean.
+
+## The one-paragraph story
+
+Labs already do RL on coding/agentic tasks and the model learns to exploit
+grader flaws. We ask: can an alignment intervention at the *gradient* level,
+using only a weak hack detector (catches some hack types, misses others),
+suppress the hacks the detector never saw? We extract a hack direction from a
+handful of hand-paired clean/hack completions (the "weak detector for hack A"),
+then during GRPO we route any rollout whose gradient aligns with that direction
+into a throwaway quarantine knob that is deleted at deploy. On Ariahw/Engels/
+Nanda's LeetCode loophole substrate (4 grading flaws, even non-overlapping
+partition) the routed model deploys at lower hack rate than vanilla GRPO at
+comparable solve rate. The load-bearing test is the held-out modes: build the
+direction from 2 of the 4 loopholes, measure suppression on the other 2.
+
+## Claims (compress to <=3, ranked by how much evidence we have)
+
+C1 (primary, existence -> systematic). Routing the GRPO gradient against a
+weak-detector hack direction in the SVD-of-W basis lowers deploy hack rate vs
+vanilla GRPO at matched-ish solve rate, replicated over n=3 seeds.
+- Evidence: jobs 68/69/70 (route2 no-floor s41/42/43) vs 79/74/72 (vanilla
+  s41/42/43). Deploy = knob-off, n=64 prompts x group, T=0.7.
+- Confidence today: suggestive at n=1; n=3 band landing. NOT yet 30pp (the
+  preregistered H1 bar); honest framing is "reduces hack at comparable solve",
+  with the pp gap stated as measured.
+
+C2 (mechanism, the no-cheat payload). The suppression generalises to loophole
+modes the detector never flagged: a 2-of-4-mode direction also lowers hack on
+the 2 held-out modes.
+- Evidence: the G2/G3 cross-mechanism runs (spec 20260528_cross_mechanism_v_hack,
+  tasks #106/#107). This is the claim a referee will attack hardest and the one
+  that makes the result interesting rather than a tautology.
+- Confidence: untested at writeup time. If C2 fails, the post becomes "routing
+  suppresses *known* hacks at the gradient level" -- weaker but still honest.
+
+C3 (specificity / not-a-regularizer). The effect needs the *direction*, not
+just the act of carving a rank-k knob out of the adapter. A Haar-random
+orthonormal V of matched per-module rank/norm does NOT reproduce the deploy
+hack-drop.
+- Evidence: job 81 (frozen random-V route control), job 80 (null_city placebo
+  pairset, "random content" pairs -- results.md Q10 already shows +0.024, i.e.
+  no effect, for the placebo at the old setup). Plus the test-time erasure
+  benchmark job 83 (does post-hoc weight/act erasure match train-time routing,
+  or is the hack baked in -> motivating train-time intervention).
+- Confidence: the placebo precedent (Q10) is encouraging; random-V is the
+  decisive one both gpt-5.5 and the brainstorm flagged. Must land before we
+  claim directional specificity.
+
+## Abstract sketch (Heilmeier + Nature structure, ~200 words, fill numbers last)
+
+1. Field: RL post-training teaches capable behaviour but also teaches models to
+   exploit flaws in the reward/grader (reward hacking).
+2. Today: interventions act on the reward or the advantage (e.g. Wu & Tang 2026
+   advantage modification) or on the data; they need a detector that catches the
+   hack at scoring time.
+3. Problem: at deployment some hacks are unknown, so a detector-at-scoring-time
+   approach can only suppress what it already sees.
+4. Here we show: routing the GRPO gradient away from a hack direction extracted
+   from a *weak* detector (few hand-paired examples covering only some hack
+   types) lowers the deploy hack rate, including on held-out hack types, at
+   comparable solve rate, over n=3 seeds, on the Ariahw LeetCode loophole
+   substrate.
+5. Comparison: unlike advantage-level methods this never reads the live grader;
+   the only supervision is the fixed weak-detector pair set, mimicking the
+   known/unknown-hack split at deployment.
+6. Context: gradient routing (Cloud et al. 2024) in the SVD-of-W adapter basis
+   (AntiPaSTO) gives a deletable quarantine knob.
+7. Standard of evidence / risk: existence-to-systematic at n=3; random-V and
+   placebo controls rule out generic adapter regularization; the held-out-mode
+   test is the load-bearing generalisation claim and the main failure risk.
+
+## Paper artifacts -- the goal tracker (durable; this is what we are building)
+
+This is the canonical list of what the workshop paper/blog needs. Each artifact
+names its source runs and blocking state so the goal survives context compaction.
+Status legend: [x] done  [/] data landing  [ ] not started. Each finished run
+writes per_mode_deploy.json + train.safetensors under out/runs/<ts>_<tag>/;
+deploy hack/solve + by_mode come from the JSON, per-step curves from the log/TSV.
+
+A1 -- Keynote figure. route2 vs vanilla deploy hack/solve over training, n=3
+band. Prototype exists: out/figs/dyn_sub4*.png (`just dyn`). [/] blocked on the
+n=3 vanilla band (jobs 74 s42 + 79 s41; 72 s43 done; route2 68/69/70 done).
+
+A2 -- Keynote table. Per-arm deploy hack + deploy solve, mean +/- SEM over 3
+seeds, route2 no-floor vs vanilla, delta vs vanilla, paired test + alpha stated.
+[/] same blocker as A1 (74, 79).
+
+A3 -- Ablation table (what each component buys; the arms you named). One row per
+arm at matched seed/preset, deploy hack + solve:
+  - vanilla (no intervention)              -> 65 / band
+  - erase (no routing; one-sided subtract) -> 66, rerun 75
+  - route2 full (routing on)               -> 68-70
+  - route2 frozen (no online refresh)      -> 68-70 are frozen; refresh = 78
+  - random-V route (direction is arbitrary)-> 81  [control: should NOT work]
+  - placebo pairset (null_city content)    -> 80  [control: should NOT work]
+  - post-hoc test-time erase (no train-time)-> 83 (scripts/tt_erase_bench.py)
+[ ] blocked on 75/78/80/81/83 (all queued). This is the "we are filling out
+ablations" table.
+
+A4 -- Long-run figure. 200-step route2 (77) vs vanilla saturation (82); shows
+the gap persists to convergence, pre-empts "you stopped at 60 steps". [ ] blocked
+on 77 + 82 (queued p5/p0).
+
+A5 -- Generalisation figure/table (the no-cheat payload, C2). Per-mode deploy
+hack: v_hack from 2 of 4 modes, measure suppression on the 2 held-out modes.
+[ ] NOT QUEUED -- highest-value gap. Queue G2/G3 (tasks #106/#107, spec
+20260528_cross_mechanism_v_hack) once the n=3 band confirms C1.
+
+A6 -- Appendix: full traces per loophole class. Prompt+hint, hack completion,
+clean completion for all 4 modes. [x] done -- blog appendix
+(docs/blog/20260529_...md#appendix-the-four-loophole-modes), task #153.
+
+A7 -- Appendix ablation context. Cite results.md Q-rows already run: basis width
+(Q8), refresh cadence (Q5), teacher mix (Q6), gate mode (Q3), solve-orthog (Q9),
+pairset content/placebo (Q10). [x] data exists; just needs porting into the paper.
+
+Next action when 74+79 land: read each per_mode_deploy.json, `just dyn`,
+fill A1/A2, append a journal entry. Then queue A5 (the gap).
+
+## Red-team checklist before publishing (paper-writing evidence standards)
+
+- [ ] n=3 deploy gap stated with SEM, not cherry-picked seed.
+- [ ] random-V (81) does NOT reproduce the drop (else it is regularization, C3 dies).
+- [ ] placebo pairset (80) shows ~no effect (precedent: Q10 +0.024).
+- [ ] held-out-mode suppression measured (C2), reported even if it fails.
+- [ ] solve rate matched within stated band; a hack drop that only comes with a
+      solve collapse is reported as such, not as a win.
+- [ ] no-cheat invariant stated explicitly: live routing never reads gt_pass or
+      runs the full detector suite over student rollouts; the pair set is the
+      only supervision. (Promote to README/spec, plan item #114.)
+- [ ] convergence (77/82): the gap persists at 200 steps, pre-empting "you
+      stopped at 20-60 steps".
+- [ ] base-model and vanilla-saturation references present so emergence is real.
+
+## Open editorial decisions
+
+- Project/repo name: `projected_grpo` is now a misnomer (method is routing, not
+  projection). Candidate: `gradient_quarantine`. Decide before the public repo
+  link goes in the post. (Retitle docs first; rename package/repo only if we
+  ship the code link.)
+- Re-headline the blog draft from erase to route2 (user: clear even at n=1).
+- Workshop vs blog-only: gate on C2 landing.