When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models
Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on visually grounded tasks remains underexplored. In this paper, we investigate the influence of task-irrelevant context by formulating it as a controlled intervention within a binary visual judgment framework. By maintaining an invariant prompt structure while varying auxiliary inputs, we observe that irrelevant text consistently biases model predictions across diverse benchmarks. To move beyond performance metrics, we characterize this sensitivity through a decision margin defined by the log-probability difference between binary candidates. Our analysis reveals a robust geom...
| Authors | Yinfeng Wang, Zhiyuan Yao, Zheren Fu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 June 2026 |
| Last updated at source | 1 September 2026 |
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