_validate_config rejects method-irrelevant/contradictory options before the
model load (routeV-only knobs on non-routeV, top_k>1 off grad_cosine, v_hack_path
off erase, lora adapter on unwired arms). Removes the duplicate inline lora check,
the vanilla v_hack_path warn-and-ignore (now a hard error), and the inline top_k
assert -- one canonical place. Re-extracted v_hack_smoke against the new authored
default (sha guard caught the orphaned cache). Smoke green; bad combo raises.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Progressive (prog_wide_clean) and authored deploy within noise (0.042 vs 0.050
hack, both 0.143 solve); default to authored as the canonical no-cheat label
source. prog_wider/widest held genuine contamination, superseded by curated
prog_wide_clean (kept for the progressive-vs-authored comparison).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Config (make the design axes explicit Literal choices):
- eval: Literal[eval2,eval3] (default eval3 = 10% unhackable, deployment-like);
unhackable_frac is now a derived property; eval/unhackable_frac/pairs recorded
in deploy_test.json metadata.
- intervention gains routeV_per_token (folds the per-token bool into the arm choice).
- routeV_gate documented as the pinning axis.
- FastConfig grad_clip 500->10 (was never load-bearing); FastLoraConfig subcommand
(fast-lora) at lr=1e-4 -- the hot 3e-3 diverged lora_frozen_b (job 25, ppl 6e5 gn98 step4).
Pairs:
- delete prog_wide.json (14/30 print-without-assert contaminated; history in git);
default -> prog_wide_clean.
- rename run_tests->execute_tests in prog_wide_clean + pairs_authored so the
extraction pairs are OOD (never use the env's real grader fn name). Re-extracted
v_hack_smoke to match.
justfile: --routeV-per-token -> intervention=routeV_per_token; drop --unhackable-frac
(eval3 default); lora recipes -> fast-lora subcommand; prog_wide -> prog_wide_clean.
smoke green (erase + routeV_per_token); all 4 verify gates pass.
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>
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>
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>
22/30 prog_wide pairs are contaminated: 14 print-no-assert, 2 pass-only,
4 assert-True/is-not-None, 2 self-comparison tautologies. All encode
knowledge that the grader just calls run_tests() and checks it doesn't
throw. Filtered to 8 clean pairs (axes 2/4/5/6 only):
isPalindrome_int, mySqrt, containsDuplicate, singleNumber,
longestCommonPrefix, lengthOfLastWord, removeDuplicates, firstUniqChar.
These are identical to 8 of the 11 authored pairs (minus P19/P20/P21).
Needed because job 9 (best result: hack=0.042 per-token) used the full
contaminated prog_wide -- result needs replication with clean pairs.
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>
The pairset JSONs are the only non-regenerable input to the method (the
v_hack bases are derived from them via on-demand extraction, train.py:528).
They were caught by the blanket /out/ ignore; switch to /out/* + re-include
so any box (and Modal) gets the source from a clone instead of a side-channel
rsync. vhack safetensors stay ignored (383M of derived binaries).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The smoke prereqs (out/pools/substrate, out/pools/teacher_pool,
out/vhack/v_hack_smoke) are gitignored pipeline outputs that only
exist on the GPU box -- a fresh clone died at verify_partition.py on a
FileNotFoundError for partition.json. Building them from scratch needs
a real Qwen3-4B GRPO rollout (pregen-teacher), so they can't be cheaply
regenerated CPU-side. Force-add them (~2.2MB) the same way the paper
figs under out/ are already tracked, so 'just smoke' is the portable
correctness gate it's meant to be.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
- a5: drop per-panel title (restated the axis); fold direction into the xlabel
(DEPLOY hack rate (down=better) / solve (up=better)). Dump a5_generalisation.csv
(per mode,arm deploy hack/solve mean+/-std) -- the reproducibility source it lacked.
- overlay (dyn_sub4_hack_overlay etc): replace the per-arm onset DOT with a single
dashed labeled 'first hack' vertical line, matching the small-multiples/longrun.
- (dyn_sub4_hack_overlay shares dyn_sub4.csv -- same runs, different view, no new CSV.)
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
- a5_generalisation: connectors -> arrows (baseline->ours direction, shows the drop
and the stdout solve-cost honestly).
- equiv0 -> approx0 everywhere: these are finite-sample estimates, not identically 0.
- plot_train_vs_deploy skips when train==deploy for every run (no knob-ON contrast);
fixes the 'can't see train' longrun/sub4 figures (they had no hk_on data).
- Prune 9 orphan figure sets not referenced in paper or blog (regenerable on demand);
keep the 3 referenced + a5 + train_vs_deploy_60_train_deploy. All 4 CSVs committed.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
End-labels sat on the line termini (2-arm figs) and piled up bottom-left on
ragged-length multi-arm overlays (substrate, where arms end at different steps).
Now all labels anchor at one gutter x with a leader fanning back to each line's
actual end, y-de-collided. Added right margin so the gutter is clear.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The solid-red deploy line ran straight through the annotation text (tufte
collision test). Move it into the empty band above the flat line (axes y=0.12).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
A vanilla seed (s43) lacked the held-out deploy eval, so its train series fell
back to the noisy n=28 per-step hack_s while other seeds used the n=64 eval.
Averaging mixed estimators fabricated a vanilla train-vs-deploy gap that does
not exist (lie-factor). Now: train series reuses the knob-off eval only (nan if
absent -> seed drops from the mean), and missing eval columns normalise to nan
so absent==all-nan. Regenerated all figures from logs. The canonical
train_vs_deploy_60 (has hk_on) is unchanged; sub4/longrun byproducts now show
train==deploy honestly (no knob-on data to split).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The train row fell back to per-step hack_s (noisy n=28 train batch) for arms
without a knob-on eval, so vanilla's train/deploy rows looked like different
estimators. Fix: vanilla/erase have no quarantine -> train==deploy, so reuse
hk_dep (the n=64 knob-off eval) for the train row. route2 still uses hk_on
(knob-on eval). Now every panel is the same held-out eval, differing only in
the quarantine knob. Regen source: train_vs_deploy_60.csv (route2 nofloor_rf2
+ vanilla sweep, seed 41, 60 steps).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Old figure paired route2 (job 84) with job 85 vanilla, whose step-88
'collapse' was a hot-preset artifact. Job 97 re-ran vanilla-200 gentle and
stays coherent. New pairing: route2 holds deploy hack at 0; vanilla rises to
~0.32 (onset ~step 40); route2 solve ends higher (0.61 vs 0.47). Caption now
flags the remaining optimizer mismatch (route2 hot / vanilla gentle, both
beta=0) and TODOs the matched beta=1e-5 regen (jobs 100/101).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Job 77 (vanilla s41) landed -> both arms n=3. Fill tab:keynote + fig:keynote
caption, add paired t-test, pin the exact 6-log regen command (just dyn
--latest-per-arm clobbers the band). Regenerated dyn_sub4 figure from the 6
explicit seed logs, fixing the 87cca9a clobber. Journal entry 2026-06-03(a).
Also: README points to main.tex and drops the stale n=1 findings block; record
two OpenReview URLs as a TODO in related work (mine reviews for shared critiques).
Closes A1/A2 (#173).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Audit of all 4 plot scripts (plot_dynamics/substrate/emergence/deploy_overlay):
- One save_fig(fig, path) helper in figs.py writes png+svg+pdf (vector for the
paper, png for the blog). All scripts call it.
- arm_label() map: reader-facing names only -- route2->route, drop 'knob'/'the
cheat' from titles and the train-vs-deploy story (adapter on/off, reward hack).
- Titles off by default (the paper/blog caption carries it); --title re-enables
for standalone research use.
- dump_data CSV now carries every plotted series; plot_dynamics --from-csv
re-renders the three figures from the committed CSV with no logs (logs/ and
out/runs/ are gitignored; out/figs/*.csv is tracked). Round-trip verified.
- Commit the regenerated dyn_sub4 figures in all 3 formats + the CSV.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The A4 framing in one figure: vanilla train==deploy (cheat in the weights),
route2 train HACKS while deploy is clean (cheat in the deletable knob). parse_log
now keeps the raw train series (hack_train/solve_train) before the deploy
substitution. New fig: dyn_longrun_200_train_deploy.png.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
- blog: mark as erase-n=2 draft, note route2/exploration-floor/deploy-eval are the
current direction; embed dyn_sub4_hack_overlay.png (force-added); ASCII em-dashes;
de-bold the arm list (#15 tell)
- README: add route2 arm + apples-to-apples deploy-eval to 'What we compare'; stale
banner on the n=1 mix=0.5 findings
- plot_dynamics: remove _mark_if_sparse (asymmetric sparse-only dots); EMA-held line
for all arms
- train.py: fix 'held-out greedy' -> 'held-out eval subset, T=0.7' (deploy eval is
sampled, not greedy)
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
New scripts/plot_substrate.py parses the hk_<mode> cumulative columns from a
multi-loophole substrate run (one log, K interleaved modes) and draws one
learning curve per mode with first_step onset dots and direct end-labels.
plot_emergence.py can't do this (it groups logs by a single --env-mode).
Figure shows the headline: vanilla GRPO learns file_marker/run_tests/
stdout_marker/sentinel, eq_override flat at 0 (never).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Code writes+reads the new scheme; migrate_out_dirs.py moved 225 loose artifacts
(0 left at top level). Per-run checkpoints+rollouts now group under
runs/<ts>_<run_id>/ as train.safetensors/rollouts.jsonl. Figures land in
out/figs/ with a stable docs/figs/<name>.png symlink (figs.link_latest).
justfile also gains run-cell REFRESH param (online-erasure arm). Smoke +
smoke-vanilla + results all green on new paths. Requeue manifest preserves the
why/resolve labels that pueue reset wiped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Eraser test: direct end-labels carry name+value (no legend, no separate
annotations, no gridlines). Layering: hack=red dominates (the story), solve=
muted grey recedes. Range-frame left spine, minimal ticks (0/.5/1, 0/19).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Visualizes #182: model hacks ~0.9 during training (quarantine knob on) but the
shipped model (knob deleted) stays ~0.25 at preserved solve ~0.5. The train-vs-
ship gap is the routing benefit. Reads old(hack_abl)+new(hack_ship) ship cols.
out/route_evidence_s41.png committed (-f, gitignored dir).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
wassname