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How does information access affect LLM monitors' ability to detect sabotage?
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Canonical route: /signal-canvas/how-does-information-access-affect-llm-monitors-ability-to-detect-sabotage
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- Last proof check
- 2026-03-19
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- 2026-04-02
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How does information access affect LLM monitors' ability to detect sabotage?
Canonical ID how-does-information-access-affect-llm-monitors-ability-to-detect-sabotage | Route /signal-canvas/how-does-information-access-affect-llm-monitors-ability-to-detect-sabotage
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/how-does-information-access-affect-llm-monitors-ability-to-detect-sabotageMCP example
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}
}source_context
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"topic_slug": null,
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}Preparing verified analysis
Dimensions overall score 9.0
GitHub Code Pulse
No public code linked for this paper yet.
Claim map
- Evidencepartial
we find that contemporary systems often perform better with less information, a phenomenon we call the less-is-more effect for automated oversight.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
In BigCodeBench-Sabotage with GPT-4.1-mini as the monitor model, EaE improves sabotage detection rates by 16.8 percentage points over the next-best monitor without increasing the false positive rate.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
we find that agents unaware of being monitored can be caught much more easily
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
monitors scoring longer reasoning traces gain more from information filtering
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
we introduce extract-and-evaluate (EaE) monitoring--a new hierarchical approach where one monitor isolates relevant excerpts from the monitored agent's trajectory and a separate monitor scores them
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
we conduct a cost-performance analysis and find that Gemini and Claude models with monitoring techniques that involve information filtering occupy much of the Pareto frontier
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
We demonstrate this phenomenon, and analyze the conditions under which it occurs, in three evaluation environments where agents must conduct sabotage while evading monitors.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
we find that contemporary systems often perform better with less information, a phenomenon we call the less-is-more effect for automated oversight.
ImplicationpartialDirectly stated in the abstract with explicit naming of the phenomenon.
Verificationpartialpartial
- Evidencepartial
In BigCodeBench-Sabotage with GPT-4.1-mini as the monitor model, EaE improves sabotage detection rates by 16.8 percentage points over the next-best monitor without increasing the false positive rate.
ImplicationpartialExplicit numeric result stated in the abstract.
Verificationpartialpartial
- Evidencepartial
we introduce extract-and-evaluate (EaE) monitoring--a new hierarchical approach where one monitor isolates relevant excerpts from the monitored agent's trajectory and a separate monitor scores them.
ImplicationpartialDirectly described in the abstract.
Verificationpartialpartial
- Evidencepartial
we find that agents unaware of being monitored can be caught much more easily
ImplicationpartialStated in the abstract, but without specific numeric comparison.
Verificationpartialpartial
- Evidencepartial
monitors scoring longer reasoning traces gain more from information filtering.
ImplicationpartialDirectly stated in the abstract.
Verificationpartialpartial
