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Satoshi Kawasaki

@akatsukiwork

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AI safety evaluation audits and reproducible verification tools

https://github.com/akatsukiwork0320
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About Me

I am an independent researcher and research software engineer focused on the reliability of AI safety evaluations. I build reproducible probes and verification tools to check whether benchmark inputs, tests, and scoring rules support the conclusions drawn from them.

Selected evaluation work (status checked 2026-10-17)

  • ControlArena input audit (report) — Checked 24 stored backdoor inputs across 10 sampled problems and identified an unresolved conflict between a stated input constraint and recorded data. The PR author responded with scope guidance; validator coverage and format policy remain maintainer decisions.

  • ControlArena side-test probe (report) — Tested reconstructed Django functions locally and identified a condition that accepted both escaped and unescaped output, alongside comparisons of baseline and mutated behavior.

  • FACE-Eval Inspect scorer report — Used a local AST-extracted scorer harness with stubbed judge responses. For a valid cue-aligned answer with missing or unparseable reasoning, observed VCR=0 and UAR=1. The no-trace VCR result conflicts with the documented NaN behavior; UAR and parse-failure eligibility remain questions for upstream. No effect on published paper results was demonstrated.

  • Debate control schemes sensitivity note — Separated the Phase 2 erratum's world-standardized question-side weights from the strict unit-weighted realized schedule and derived conditional sensitivity bounds on archived Phase 2 outputs.

  • Manifund funding-bench — Proposed an offline diagnostic and unit tests showing how proposal attribution and scoring windows affect a synthetic prediction. Production scoring and data are unchanged.

These studies are offline or archived-output investigations. Full evaluation pipelines, SWE-bench Docker environments, and live model calls were not run.

Engineering background

My wider verification work includes an exact rational checker for a HiGHS optimality bug confirmed by a maintainer, and a proposed op-reth fix for a historical-proof bug independently reproduced by an Optimism maintainer. The upstream HiGHS fix #3408 was merged on 2026-10-16; my contribution was reporting and verification. The funding-bench and op-reth PRs remain open as of 2026-10-17.

Open to research grants, collaborations, and scoped evaluation audits. Corporate contracting is available through MIKOTO Co., Ltd. Further code, reports, and validation limits are linked from my GitHub profile.

AI tools assisted implementation, experiments, analysis, and writing. Linked evidence distinguishes local findings from upstream confirmation.

About SatoshiBy Satoshi1Following1
Testing failure modes of debate-style AI control schemes
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Satoshi Kawasaki

4 days ago

On your request for scrutiny of the Phase 2 erratum's post-hoc side standardization, I checked the pinned script and archived JSON at commit 34898fb:

  • https://github.com/jackmaiorino/selvarath-debate/blob/34898fb/scripts/phase2_mirror_reanalysis.py

  • https://github.com/jackmaiorino/selvarath-debate/blob/34898fb/analysis_out/phase2_mirror_reanalysis.json

The +4.56 pp side-standardized estimate (95% CI [2.89, 6.25] under strict) and the +3.94 pp realized-schedule estimate (95% CI [2.24, 5.67] in the reanalysis) target different aggregation weights:

1. Two distinct aggregation weightings (strict rule)

  • The +4.56 pp side-standardized estimate (in question_side_means() and world_standardized()) first collapses same-side transcripts within each question to a question-side mean (with 19 single-polarity questions contributing to one side only: H_A,std = +1.44 pp, H_B,std = +7.68 pp) and then gives the three worlds equal 1/3 weight: (H_A,std + H_B,std)/2 = +4.56 pp.

  • The +3.94 pp realized-schedule estimate, under strict scoring (invalid_wrong=True, common_support=False), retains all scheduled slots and counts INVALID judgments as wrong; it does not require every judgment to be valid. Because all 246 question/transcript units receive equal 1/246 weight, let u index the 246 units, Δ_u(p) in [−1, 1] be the sequential_b2 − b0 error contrast under polarity p averaged over the fixed judges, debaters, and replicate slots, and κ_u = Δ_u(B) − Δ_u(A). With 128 A and 118 B units (w_A = 128/246, w_B = 118/246), their hypothetical equally mirrored unit-weighted counterpart satisfies:

    H_mirror = H_realized + [w_A · mean_A(κ) − w_B · mean_B(κ)] / 2.

    This is a separate target around H_realized; it does not reconstruct the missing observations or assert that the reported +4.56 pp is an arithmetic error. (I keep valid_only separate because dropping invalid slots means unit weights need not equal 1/246.)

2. Two conditional sensitivity observations under strict scoring

  • Homogeneous κ_u = κ: Because the 128 : 118 split is nearly balanced, H_mirror(κ) = H_realized + (5/246)κ. Since Δ_u(p) lies in [−1, 1], any convex combination of unit contrasts is bounded by their maximum, so κ must satisfy κ ≥ max_B Δ_B − 1 ≥ H_B,std − 1 and κ ≤ 1 − max_A Δ_A ≤ 1 − H_A,std. Using the archived side means gives the necessary outer interval [−92.32, +98.56] pp for κ. Consequently, every support-feasible homogeneous κ yields a fixed-sample H_mirror within [+2.06, +5.94] pp, hence positive. These are outer bounds, not exact feasible ranges or confidence limits.

  • Bounded heterogeneous |κ_u| ≤ K: [H_realized − K/2, H_realized + K/2] is a coarse outer envelope. Its lower endpoint reaches zero at K = 7.876 pp (not a proof that a negative mirrored effect becomes feasible at that value, since outcome-support constraints in [−1, 1] can tighten the bound). For a fixed K, expanding the reanalysis's existing question-cluster CI [2.2358, 5.6657] pp by K/2 on each side makes the lower endpoint zero at K = 4.4715 pp, inheriting the original interval's assumptions without rerunning the bootstrap.

Would a short appendix separating these two estimands and assumption-indexed bounds be useful?

Disclosure: AI assistants helped check source and arithmetic. These are conditional sensitivity calculations on archived Phase 2 outputs, not newly collected experimental results.