More Thinking, Less Seeing? Assessing Amplified Hallucination in
  Multimodal Reasoning Models
Authors: name: Chengzhi Liu, name: Zhongxing Xu, name: Qingyue Wei, name: Juncheng Wu, name: James Zou, name: Xin Eric Wang, name: Yuyin Zhou, name: Sheng Liu
Organization: Stanford University

Test-time compute has empowered multimodal large language models to generate
extended reasoning chains, yielding strong performance on tasks such as
multimodal math reasoning. However, this improved reasoning ability often comes
with increased hallucination: as generations become longer, models tend to
drift away from image-grounded content and rely more heavily on language
priors. Attention analysis shows that longer reasoning chains lead to reduced
focus on visual inputs, which contributes to hallucination. To systematically
study this phenomenon, we introduce RH-AUC, a metric that quantifies how a
model's perception accuracy changes with reasoning length, allowing us to
evaluate whether the model preserves visual grounding during reasoning. We also
release RH-Bench, a diagnostic benchmark that spans a variety of multimodal
tasks, designed to assess the trade-off between reasoning ability and
hallucination. Our analysis reveals that (i) larger models typically achieve a
better balance between reasoning and perception, and (ii) this balance is
influenced more by the types and domains of training data than by its overall
volume. These findings underscore the importance of evaluation frameworks that
jointly consider both reasoning quality and perceptual fidelity.