Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Models
Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in various perception and reasoning tasks. Despite this success, ensuring their reliability in practical deployment necessitates robust confidence estimation. Prior works have predominantly focused on text-only LLMs, often relying on computationally expensive self-consistency sampling. In this paper, we extend this to multimodal settings and conduct a comprehensive evaluation of MLLMs' response confidence estimation. Our analysis reveals a significant instinct-reflection misalignment: the model's implicit token-level support frequently diverges from its verbal self-assessment confidence. To address this mi...
| Authors | Yunkai Dang, Yifan Jiang, Yizhu Jiang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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