Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidence. Prior work mainly focuses on detecting or suppressing hallucinations at generation time, leaving the subsequent reasoning stage largely unexplored. In this study, we investigate Post Hallucination Reasoning (PHR), the stage where hallucinated semantics enter the model's inference context and influence downstream predictions. To systematically explore PHR, we introduce HIVE, Hallucination Inference and Verification Engine, an evaluation infrastructure that enables controlled comparisons between faithful and hallucinated captions.
Across nine tasks and nine models, we observe structured modality dependent patterns: hallucinated captions often improve accuracy on vision language tasks, while text-only tasks exhibit limited or unstable effects. Further analyses show that hallucinated cues broaden semantic coverage and reshape reasoning dynamics while preserving stable inference. These findings highlight that hallucinated semantics may influence downstream reasoning once they enter the model's inference context. Understanding this post-hallucination stage is crucial for improving the reliability and interpretability of multimodal reasoning systems. The code is publicly available at GitHub.
Blogger's Review: This paper delves into the post-hallucination reasoning in vision language models, revealing how hallucinated semantics can impact downstream reasoning processes. Notably, HIVE provides an effective evaluation tool that could propel further research in this area. Understanding and managing hallucinations will be key to enhancing the performance of multimodal systems.