arXiv — cs.AI preprintsInternational12 October 2026
PHRBench: A Behavioral Evaluation of Post-Hallucination Reasoning in LLMs
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arXiv:2610.10455v2 Announce Type: replace-cross Abstract: Hallucinated information can propagate through multi-stage LLM systems and become part of the context for subsequent reasoning. Existing studies of post-hallucination reasoning (PHR) mainly characterize changes in final outcomes and aggregate reasoning dynamics, leaving how models resolve hallucinated premises at the response level insufficiently understood. In this work, we introduce PHRBench, a controlled benchmark for behaviorally structured PHR across four domains and 18 large language models. PHRBench characterizes each reasoning t
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