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Reasoning-Token Spikes Under Prompted Untruthful Responding in Large Language Models

Paper recorded by Signals 4 on 2026-10-07 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08

Category: cs.AI · 人工智能 · first seen 2026-10-08

Abstract

Monitoring the chain-of-thought of reasoning artificial intelligence (AI) models remains a key approach to detecting deception and other forms of misbehavior in such models. However, semantic chain-of-thought monitoring depends on reasoning traces being legible and sufficiently faithful to the underlying computations that produced the model's behavior, not to mention accessible. Moreover, there is

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Cite this page: Reasoning-Token Spikes Under Prompted Untruthful Responding in Large Language Models: the #400 most recent of 960 cs.AI papers we have recorded (as of 2026-10-07). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/reasoning-token-spikes-under-prompted-untruthful-responding-in-large-language-mo.html
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