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Sprint projectMar 22, 2026Belgium

MonitoLR: Hunting for honesty improves safety at low audit budgets

Douw Marx · Team HonestyIsTheBestPolicy

Submitted to AI Control Hackathon 2026. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: MonitoLR: Hunting for honesty improves safety at low audit budgets

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This project tests whether using a "benign-confidence" monitor, in addition to the standard suspiciousness monitor, to approximate the log-likelihood ratio test statistic improves safety monitoring. In addition to asking "how suspicious is this action?", we also ask "how benign is this?". Results on LinuxArena's Haiku trajectories show a +50% improvement (0.192-0.288) in safety at a 0.01% audit budget, compared to a baseline with the same compute budget. This result can be motivated by the Neyman-Pearson lemma, which states that the likelihood ratio test is the most powerful test for a given false positive rate.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Finding better monitoring techniques with low-auditing budgets seems important. This paper shows a relatively straightforward implementation. For the fair baseline - I would argue in order to vouch for the usefulness of the Neyman-Pearson lemma, you should average both the positive and negative prompts instead of averaging the positive prompt twice. Further, the dataset size seemed to small to make broad claims about the success of the technique.

    However, this is a solid idea with straightforward implementation.

  2. The diversity motivation is strong, clear and well thought out. I would like to see this completed.

Cite this project

@misc{marx2026monitolr,
  title = {{MonitoLR: Hunting for honesty improves safety at low audit budgets}},
  author = {Douw Marx},
  year = {2026},
  month = mar,
  note = {Submitted to AI Control Hackathon 2026, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/monitolr-hunting-for-honesty-improves-safety-at-low-audit-budgets-3mvi}},
  url = {https://apartresearch.com/sprints/projects/monitolr-hunting-for-honesty-improves-safety-at-low-audit-budgets-3mvi}
}

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