arXiv — cs.AI preprintsInternational12 October 2026
Reasoning-Token Spikes Under Prompted Untruthful Responses in Large Language Models
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arXiv:2610.10405v2 Announce Type: replace 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 increasing evidence that chain-of-thought outputs may soon become illegible or unfaithful, if they even remain accessible. Based on cognitive load
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