HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models

2026-07-08

AI Summary

This work addresses a critical gap in existing research on hallucinations in vision-language models (VLMs), which has predominantly focused on detecting or suppressing hallucinations during generation while overlooking their downstream impact once embedded in reasoning contexts. The paper introduces, for the first time, the concept of “Post-Hallucination Reasoning” (PHR) and presents HIVE, a comprehensive evaluation framework to systematically investigate how hallucinated semantics influence reasoning dynamics. Spanning nine tasks and nine state-of-the-art VLMs, HIVE combines quantitative and qualitative analyses to demonstrate that hallucinated semantics can significantly enhance performance in multimodal tasks by expanding semantic coverage and reshaping reasoning pathways. In contrast, such benefits are marginal in purely textual tasks, suggesting that hallucinated semantics confer structural advantages specifically to multimodal reasoning.

Abstract

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 work, we study Post Hallucination Reasoning (PHR), the stage in which hallucinated semantics enter the model's inference context and influence downstream predictions. To systematically investigate 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 important for improving the reliability and interpretability of multimodal reasoning systems. Code is publicly available at https://github.com/hefengcs/HIVE.

Problem

• hallucination
• vision language models
• post-hallucination reasoning
• multimodal reasoning
• inference context

Innovation

• Post-Hallucination Reasoning
• Vision Language Models
• Hallucination Evaluation
• Multimodal Reasoning
• HIVE