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PHRBench: A Behavioral Evaluation of Post-Hallucination Reasoning in LLMs

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

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

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#394 most recent of 960 cs.AI papers we have recorded · ↑ newer: Composing What Each Teacher Learned: Multi-Teacher On-Policy Distillat · ↓ older: A Good Self-Teacher Meets the Student Where They Are: Joint On-Policy

Cite this page: PHRBench: A Behavioral Evaluation of Post-Hallucination Reasoning in LLMs: the #394 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/phrbench-a-behavioral-evaluation-of-post-hallucination-reasoning-in-llms.html
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