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>
- 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>
