Gate calibration: route by live mean + route_std_mid/route_std_rout * std of the
pooled cosine-to-v_grad, not a fixed quantile tail. Self-silences -- only the tail
that genuinely exceeds the spread routes, so qmass tracks real separation instead
of a forced fraction. The authored absolute band is mis-placed (live pos sits far
below the synthetic-hack edge; even synthetic solve out-aligns on-policy hack).
tablelog: auroc/rout/routE/keep/resid/qmass cols always shown (nan on vanilla) so
arm tables line up.
Diagnostics: scripts/diag_pinning.py (4-population calibration view, mean+/-2sd band)
and scripts/diag_pinning_refresh.py (proves cosine stats recompute from a tracked
v-independent gradient cloud on a v_grad refresh -- exact for k=1, sanity 2.5e-16).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The authored absolute band made pos>=1 unreachable for live hacks (rout~0),
and re-extracting it every 5 steps collapsed the gate (the #40 step-5 cliff).
- Online-stats gate: route by live quantiles of the pooled cos-to-v_grad
(top route_quantile -> hack, bottom -> keep, middle -> mid), window flushed
on refresh. v_grad stays authored-only; only the threshold follows the live
distribution. Smoke: routing sustained past the refresh (cliff fixed).
- Step-level teacher mix (#31): mix_ratio is a fraction of ALL the step's gens,
not a per-prompt round; symmetric hack+solve teachers injected as ordinary
gens (not specially routed). Fixes the per-prompt rounding wart.
- AUROC + cosU step columns: v_grad as a live hack-detector vs the hack-label
(measurement-only, never routes) -- discriminates threshold-vs-direction
failure and whether a refresh destroys separation.
- Inline eval stays off (eval_ablate_every=0); deploy scored offline.
- Fix _sample_rows None crash (beartype) on the no-solve-pool path.
- Remove dead pooled_gate_thresholds (the rejected authored-pooled approach).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Off the live lora2r path; removed with vhack.py (commit 4120d75):
- proj.py: drop project_delta_S_grad/_project_one_module/mean_cos_pre_from_grads/
_hackward_cos (no live importer; train.py uses only per_token_logps).
- verify_science_invariants: test pairset_sha256's content gate directly (drops the
load_v_hack vehicle + fake delta_S wrapper fixture).
- extract_vhack_grad: import pairset_sha256 from .pairs (was re-exported via vhack).
- tablelog/figs: stale 'delta_S grads'/'knob' comments -> A/B grads.
Smoke + verify_science_invariants green; no delta_S left in live code.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
2a-2c only tested UNIFORM masks. 2d puts rollout 0 clean (0,0) and rollout 1 hack
(1,1) in ONE forward and asserts the mixed deployed grad == rollout-0-alone-clean
and the mixed quarantine grad == rollout-1-alone-hack -- the load-bearing
per-rollout mask vectorization ([G,1,1] reshape) with no cross-rollout bleed.
Green on tiny-random.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
antipasto.py (PiSSA/lora_frozen_b/old-lora2r wrappers) is dead in the live path --
train.py/extract use lora2r.py, nothing imports antipasto. Move the 7 scripts that
import it or the erase-era proj fns (rescore_deploy, eval_checkpoint_curve,
verify_vhack_heldout, probe_distill, diag_cosine_dist, diag_pairs_compare,
tt_erase_bench) to scripts/attic/ -- they need lora2r rewrites if resurrected.
Live imports verified clean.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
train.py rewritten straight-line for the single rank-2r Gaussian-init LoRA adapter
and three arms (intervention none|routeV|absorb). Removes the erase grad-surgery,
act_vote/online_stats gates, beta/KL reference path, per-source split harvest, the
v_hack injection block, and all per-mechanism E/C/D/A-B tallies. Folds in:
- T2 Gaussian init (lora2r.py): A0~N(0,1/d_in), B0~N(0,1/2r), net delta 0 at init.
- T3 width-pooled gate labels: single (num/den) fraction across modules, skip
zero-width modules, raise if none separate (was per-module equal-weight blowup).
- T5 absorb arm: masks pinned (1,0) -> both blocks train, no gate.
- T6 self-contained ckpt: A/B/A0/B0 in one file (no _hack file, no SVD cache),
adapter:"lora2r" in saved cfg.
- T8 m3: step_flagged logs the hack share (d.mean), not m.mean.
Gates green: verify_lora2r_routing (4 invariants) + smoke none/routeV/absorb
end-to-end on tiny-random Qwen3 (logs in /tmp/claude-1000/smoke_*.log).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Structural-separation arm to disentangle directionality from shrinkage. A rank-2r
PiSSA-init LoRA with A and B both trainable, partitioned into a deployed block [:r]
and a quarantine block [r:] (spectrum-matched via alternated SVD axes). Unlike the
same-basis PiSSA routeV (where deploy-ablation only removes a magnitude slice of one
shared update = shrinkage null), each block has its own input-side A rows and
output-side B columns, so deploy-ablation removes a different FUNCTION.
Routing = SGTM-style three-way hard per-rollout masks from the cosine of the deployed
block's gate-pass gradient to the pair-extracted v_grad: clean (m=0,d=0) trains
deployed only; hack (m=1,d=1) detaches deployed output so only the quarantine updates
(SGTM grad-retain trick); mid (m=1,d=0) trains both (absorption). Gate is no-cheat:
cos to the hand-authored-pair direction, never an oracle label of a live rollout.
verify_lora2r_routing.py gates identity-at-init, the three-way block-grad routing,
per-rollout c-probe recovery, and ablation teeth; wired into smoke-lora2r. Additive:
PiSSA / lora_frozen_b paths untouched.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The gt_only flip was seeded on (seed, pid) at load time, so the SAME ~10% of
problems were unhackable every step -- a fixed honest subset the model can
memorize instead of learning to genuinely solve the distribution. Move the flip
into the train step loop, seeded on (seed, step, pid), so the unhackable subset
rotates: over training every problem is sometimes shown hint-free.
Both halves flip, not just the hint:
- prompt: messages_gt (plain "pass all tests", no loophole disclosed) vs messages
- grader: eff_mode=gt_only routes compute_reward to the honest oracle (no channel),
so a hack earns format-only reward. Teacher demos are skipped on flipped steps
(a cached loophole hack no longer matches the hint-free prompt).
Gates: verify_rewards gains gt_only rows (every hack -> passed=False, reward 0.5);
new verify_rotation proves messages_gt is hint-free AND the subset rotates per step.
Smoke logs flip count (1/30 hint-free, graded gt_only).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Disambiguate the overloaded deploy/train/knob vocabulary (paper-consistent:
'quarantine' + 'ablated' + 'deployed' all match Cloud et al.). One opposite each:
- policy view: hack_deployed/solve_deployed (quarantine ablated, ships) vs
hack_as_trained/solve_as_trained (quarantine attached). Unifies the old split
deploy_hack (JSON) vs hack_deploy (table key) into one name.
- 'knob' -> 'quarantine'/'adapter' throughout comments and log strings.
- train/test reserved for the DATA split only.
Bump RUN_SCHEMA v1->v2 so old deploy_test.json files are skipped (not crashed) by
completed_runs. CLI flags untouched (queued jobs unaffected). Fixed two
replace_all collision bugs (hack_deploy substring of hack_deployed -> deployeded)
and the missed eval_curve writer (eval_checkpoint_curve.py) + readers
(results_deploy.py). Smoke green: v2 written + read; gates pass.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
- Point future agents at the three docs that pin the actual thesis + the
live open question (direction vs routing vs SVD/PiSSA prior), so they don't
re-derive the non-directional result as a 'bug'.
- Revert rescore_deploy cfg.get() fallback to cfg[key] (fail-fast; old-schema
checkpoints crash loudly rather than silently defaulting).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
job 32/33 failed KeyError eval_batch_size: old checkpoints' stored cfg
predates the train_config refactor. Default eval_n_prompts/max_new/
eval_batch_size to the fast preset (eval-harness params, not model-defining;
test split is fixed-size) so historical checkpoints re-score.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Job 32 failed KeyError: 'eval_modes' -- deploy_test.json written by the
pre-cleanup train.py has no eval_modes key. by_mode keys are the modes
the original eval spanned (present in every version), so derive from
them to reproduce the same knob-off headline.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Now that final/rescore eval record deploy_hack_on/solve_on at n=119,
the deploy scatter shows the honest quarantine move (hollow knob-on dot
-> arrow -> solid knob-off dot) on the same axis instead of borrowing
val's lower-scale curve. Dot-only fallback for arms not yet backfilled.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
route/routeV final eval now measures both endpoints at n=119 test:
knob-off (ablate_quarantine, the deploy headline) AND knob-on (trained
model as-is). Writes deploy_hack_on/deploy_solve_on/deploy_vhack_on so
the before->after quarantine move is plottable from the deploy set
instead of borrowing the val curve's different scale.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
- floor_ceiling_abs.png: clean deploy Pareto. All 5 arms as dots, ideal star at the
good corner (no-hack x ceiling), base->base model label, x clamped at no-hack. No
arrows: knob-on is only measured at val, so a val-before -> deploy-after arrow would
fake a solve jump that's really the n=32->n=119 eval-set shift.
- floor_ceiling_knob.png: the real before->after on ONE eval (val n=32). Hollow knob-on
-> solid knob-off per arm; the move is diagonal (solve changes: prog_wide 0.069->0.056,
authored 0.056->0.044), not the horizontal I wrongly forced earlier.
- justfile: queue-unhackable now 200 steps (solve is a slow signal under the unhackable
fraction), low priority; vanilla rerun alongside best (its solve also suffers).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Two separate panels over-reduced a 2-variable story. One scatter instead: good
corner top-right (hack axis reversed), green effect-arrows from the vanilla
baseline show what each intervention did, achievable solve band (base..ceiling)
as a range-frame, ticks only at meaningful values (no-hack/vanilla/base/ceiling).
No title; name-only point labels (position already encodes the rates). The Pareto
view makes domination visible: per-token strictly dominates random-V and vanilla.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
- Transcribed Fig-5 numeric table now lives inline in the paper md as an
EDITOR'S TABLE comment, deleting docs/papers/ariahw_results_table_extracted.md
(one fewer repo file; the table sits next to the figure it transcribes).
- floor_ceiling_abs.{png,pdf}: raw-rate variant. Arrows climb from the floor
anchor; grey bedrock = worse-than-floor, blue sky = past-ceiling; hack axis
reversed so right=better on both panels.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Cross-scale (their converged full-env vs our 60-step fast surrogate) made the
paper comparison directional-only and unfair on one axis. Show vanilla GRPO as
the red floor anchor instead; paper numbers stay in the extracted table.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Had Ariahw bars on the hack panel only -- misleading. Mirror them onto solve
(Fig 5 perf: GT 25.0%, probe 19.4%, LLM-judge 16.2%, base 11.5%, ceiling 22.3%).
Honest picture: the paper methods (incl. no-oracle LLM judge) beat routeV on both
axes because they are converged full-scale vs our 60-step surrogate -- caption
marks it directional-only. Cross-scale/maturity caveat (task #18) still stands.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Read the figure PNGs directly (Fig 5 is a full numeric table the paper never
prints as text). Saved to docs/papers/ariahw_results_table_extracted.md so we
stop re-OCRing. Key correction: my 'LLM judge has no clean rate' was wrong --
LLM-judge PENALTY = 0.1% hack / 16.2% perf, NO oracle. So no-oracle suppression
is not routeV's novelty (the judge does it); the mechanism is (no live monitor,
gradient-level, fixed authored-pair direction). Plot now shows the judge as a
blue no-oracle peer bar alongside the grey oracle methods.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The disqualifier for an intervention is needing the env oracle / ground-truth
hack-labels of the live training distribution, not 'a detector ran'. On a new
RL env there is no oracle, so GT-monitor and the (oracle-label-trained) probe
can't be built there; a generic LLM judge and our hand-authored-pair vector can.
LLM judge is thus the fair external peer (no clean fast-env number to plot).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Swap the floor->ceiling reference to the substrate paper (Ariahw et al. 2025),
which benchmarks interventions on the same floor (No-Intervention hack ~79%) /
ceiling (RL-Baseline no-loophole). Their best arm (Ground-Truth Penalty, ~0%
hack, perf >= ceiling) reaches the top corner BUT uses the oracle monitor at
train time -- the exact cheat our no-cheat constraint forbids; their only
oracle-free method (inoculation) gave incomplete, high-variance mitigation.
Plotted hatched/grey as an ORACLE upper bound (solve approx; figures are images,
200-step preset not step-matched). Honest framing: their working methods need
the oracle; ours uses no detector at train time and still suppresses 93%.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
select = routing precision = hack_supp - solve_supp on the knob (held-out val,
knob-ON vs knob-OFF, same split). 1.0 = removes all hacking at no solve cost.
Sanity: vanilla=0.00 (no knob), base=blank (no knob-on signal), per-token=0.96.
hack_supp = (vanilla - hack)/vanilla ; solve_uplift = (solve - base)/(ceiling - base),
the floor->ceiling normalized fractions (ceiling provisional=paper 0.223, FIXME job 24).
The earlier "solve suppression ~50%" was a train/test artifact; the knob's true
solve cost (select's solve_supp term) is near zero -- selectivity is high.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Rework per feedback: hack and solve are not opposites, so they get separate
floor->ceiling axes (each 0=floor..1=ceiling) rather than sharing a zero -- this
also stops solve (range ~0.13-0.22) being squished next to hack (0-0.61).
Minimal: routeV per-token (best) vs random-V (direction control) vs the SGTM
gradient-routing paper placed on the same floor->ceiling % axis (approx, LM task).
Reads: hack suppression 93% best / 84% control / ~98% reference (9pp = direction
signal); solve gained +17% / -17% / ~95% (far from ceiling -- model barely learns
to solve in 60 steps). Moved out/plots -> out/figs.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Two-stage script: build out/plots/floor_ceiling.csv (one row per arm/anchor,
with SOURCE and STATUS columns flagging every provisional/missing cell) then
the keynote figure. Prints TODO/FIXME data gaps before plotting.
Panel A: normalized floor->ceiling bars, headline deploy (knob-off, test n=119).
Panel B: the knob effect -- arrow knob-ON -> knob-OFF on the SAME held-out val
split (eval_curve.jsonl), isolating the quarantine from the train/test
memorization gap. Fixes the earlier conflation where the train->deploy arrow
mixed knob-on/off with train-problems/test-problems.
Data gaps flagged in csv: solve ceiling provisional=paper 0.223 (FIXME job 24),
prog_wide arm contaminated (TODO job 28 prog_wide_clean).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
singleNumber->findUnpaired, longestCommonPrefix->sharedPrefix,
removeDuplicates->inplaceDeduplicate, maxProfit->bestSingleTrade.
Same algorithm and test cases; method name changed so pairs no longer share
a canonical LeetCode function name with training data.
Also update results.md Q14 table: add hack_train/solve_train columns,
vanilla row, and prog_wide contamination note (docs/ is gitignored).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
- Delete src/vgrout/pairs_v2.py and src/vgrout/pairs_intent.py; move all data
into scripts/pairset_build_intent.py (self-contained, exports 3 JSONs).
- Export: pairs_intent_think.json (6), pairs_intent_funcname.json (6),
pairs_intent_concept.json (6 diagnostic).
- Update diag_cosine_dist.py and diag_pairs_compare.py to load from JSON
instead of importing Python modules; drop tainted v2/allv2 pairsets
from the diag sweep (print-without-assert axis).
- train.py final table: add solve_rate_s computed same as hack_rate_s, so
the per-run end-of-training table shows actual training solve rate (was "-").
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
pairs.py now only has HackPair dataclass + _prompt/_wrap helpers.
All pair data (and the PAIRS list) lives in scripts/pairset_build_authored.py.
Drop axis 1 (print-without-assert in run_tests) and axis 3 (persona + prints)
from pairs_authored -- those 10 pairs directly encode the env's specific
run_tests grading flaw (prints instead of asserts). Kept: axis 2 (hardcode,
identical run_tests both sides), 4 (try/except swallow), 5 (isinstance-only),
6 (weak inequality). P19-P21 (previously defined but unused) now included.
Result: 11 pairs from 4 axes.
Also removed the PAIRS-fallback branch from extract_vhack_grad.py (same
fail-loud pattern applied to train.py earlier).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
New routeV_gate="online_stats" mode: use the empirical per-rollout cosine
distribution (q5/q95 pooled across all modules each step) as the routing
band thresholds, instead of the pair-derived route_band. Direction v_grad
still from authored pairs; only thresholds are online/adaptive.
Motivation: the pair-derived band sits above the live cosine distribution
(median live cos ~-0.06), causing frout to cliff as GRPO advantage flattens.
Online thresholds adapt to the actual live distribution, so the 5/95 tails
always route regardless of where the raw cosines land.
Config: routeV_gate="online_stats", online_stats_lo=0.05, online_stats_hi=0.95.
Step-0 prior: (-0.5, 0.5) neutral band (pairs not used for calibration).
Band update: post-opt.step(), torch.quantile over that step's module*rollout cosines.
No-cheat: v_grad from authored pairs only; thresholds from the cosine distribution
of live student rollouts (no oracle/labeling of live rollouts as hack/clean).
Also: add online_stats to results_deploy._arm(); justfile queue-online-stats recipe.
Queued as job 22 (s43, authored pairs, priority 12, after 19/20/21).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
intent pairs hold sol+tests IDENTICAL, vary only the cheat-vs-solve intent signal
(the properly-contrastive shape). --pairs {think,funcname,concept} for AUROC test.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Ideal (oracle CV) AUROC grad 0.84 / act 0.84 >> pair-direction 0.56/0.67: the DIRECTION
is the bottleneck, not separability. on-distribution pairs green-lit. act vote 0.669 best clean.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Finding: v_grad/As barely separate LIVE hack from clean (authored pairs are
off-distribution: localized run_tests-block contrast vs full novel-problem rollouts).
act-cosine best AUROC 0.69; grad-cosine best confident-tail p@10 0.70; magnitude inverted.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
scripts/results_deploy.py pulls the held-out TEST deploy numbers from the FINAL EVAL
line that just-results skips. Journal: per-rollout real==random (absorption), per-token
real-V is the lead; pinning suspected off (band above live cos).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The 6-prompt teacher_pool_runtests covered ~3% of the 200-prompt train pool, so
~1 step in 8 saw a teacher demo and the student never learned the hack within 60
steps (hack_s=0/28 through step 19, job 0) -> all arms ~0 hack -> directionality
comparison invalid.
scripts/build_runtests_pool.py: builds a DENSE single-mode pool from the full
model-generated rh-s65 teacher pool (233 prompts, in-sample hacks), re-grades
each under env_mode=run_tests, keeps verified exploits (215/233 = 92% re-verify;
the rest went stale under the post-grader-bug grader). One demo/prompt (G_t=1
per step), no partition.json. Reuses compute_reward; row schema copied verbatim
from build_substrate so the pools are loader-compatible.
- queue-dir6 -> teacher_pool_runtests_dense (all 8 arms).
- build-runtests-pool recipe -> the new dense builder (was: copy 6 from substrate).
- main.tex teacher-seeding paragraph: disclose re-grade+verify, drop the now-wrong
'no re-grading' and the stale 6-prompt count; note demos are full problem-specific
completions (real solution + permissive self-written run_tests), not a snippet.
Source = HACKY checkpoint (rh-s65), not base. Old 6-prompt sweep killed and
requeued on the dense pool.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The periodic VAL eval ran on leetcode_train_medhard_holdout.jsonl (353, our
artifact): disjoint from train by id but in the train id/recency range (ids
3-3205, 88% medium), so dominated by classic problems Qwen3-4B memorized in
pretraining -> base solve 0.94, saturating solve and killing the hack metric's
gt-fail headroom. Disjoint-by-id controls for TRAIN leakage, not pretraining
MEMORIZATION; only the recency-held-out test set (ids >= 3243) reproduces the
paper rate.
Proof (job 176, base model, same eval_hack_solve): test_medhard solve=0.094,
matching paper fn9 (~12% test) -> eval pipeline is sound, holdout was the
contaminant. Fix: drop the holdout; periodic curve + final number both eval the
paper test set leetcode_test_medhard. Smoke green. Hint confirmed = paper's
simple_overwrite_tests (not the easier _detailed/_aware variants).
Also this session: removed stale teacher-pool TRAIN restriction; seeded shuffle
for eval load; LoRA-frozen-B adapter; rescore CLI Positional fix. Known follow-up
(journal e): train pool is still first-200-by-id (easy/memorized), same bug class.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Ariahw's run_tests fn-name memorization control (paper line 84: train fixes
"run_tests", eval randomizes over 10 held-out names), extended to our 3 added
modes (stdout_marker/sentinel/file_marker). At train the side-channel detector
accepts a whole family + a fixed hint token (hack stays RL-discoverable); at
eval we disclose a NOVEL token and grade against a DISJOINT family equally
lenient as train -- so a memorized train token scores 0 and only producing the
disclosed mechanism counts, with no train-lenient/eval-exact strictness shift
(the v1 confound the external panel flagged). run_tests stays exact-both
(matches the paper). Eval-only; training path byte-identical (families default
None). scripts/verify_eval_gap.py proves per mode + per eval token:
disjointness (train detector must NOT fire on eval token), the gap matrix, and
no mutation of canonical prompts. Wired into smoke.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Ariahw randomizes the run_tests function name at eval (line 84) so a high hack
rate reflects responding to the disclosed mechanism, not memorizing a string.
Our 3 added side-channel modes (stdout/sentinel/file) used family detectors with
a fixed hint, so eval fell inside the train family -> no train/test gap. Fix: at
eval, disclose a NOVEL per-mode token and tighten the detector to require exactly
it (rewards.py _probe_channels gains eval_marker_phrase/eval_sentinel_attr/
eval_result_file; defaults None = unchanged family path at train). run_tests uses
the existing test_func_name param. eval.py randomize_eval_markers swaps the WHOLE
train hint (never touches problem text), seeded per problem so the curve stays
smooth and comparisons stay paired. New gate scripts/verify_eval_gap.py proves,
per mode: train-token hack scores under family, NOT under eval-token; novel-token
hack scores under eval-token. Wired into smoke.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
wassname