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>
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>
wassname