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Caught in the Act: Probes Effectively Detect Sabotage and Catch Unverbalized Deception

Paper recorded by Signals 4 on 2026-10-08 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09

Category: cs.AI · 人工智能 · first seen 2026-10-09

Abstract

Recent incidents have highlighted the challenge of monitoring LLM agents and the danger of models deceiving people. We show that white-box deception detection via probes can be scaled up to frontier monitoring settings by collecting the largest deception dataset to date for training probes and introducing a novel probe architecture which can aggregate information across many layers and tokens. Our

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#365 most recent of 960 cs.AI papers we have recorded · ↑ newer: Bi-FORK: Generative Modeling of High-Dimensional Bifurcating Systems · ↓ older: Ecology of AI Agents: Collaboration Creates a Population Threshold for
Cite this page: Caught in the Act: Probes Effectively Detect Sabotage and Catch Unverbalized Deception: the #365 most recent of 960 cs.AI papers we have recorded (as of 2026-10-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/caught-in-the-act-probes-effectively-detect-sabotage-and-catch-unverbalized-dece.html
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