Linking Perception, Confidence and Accuracy in MLLMs
Recent advances in Multi-modal Large Language Models (MLLMs) have predominantly focused on enhancing visual perception to improve accuracy. However, a critical question remains unexplored: Do models know when they do not know? Through a probing experiment, we reveal a severe confidence miscalibration problem in MLLMs. To address this, we propose Confidence-Driven Reinforcement Learning (CDRL), which uses original-noise image pairs and a novel confidence-based reward to enhance perceptual sensitivity and robustly calibrate the model's confidence. Beyond training benefits, calibrated confidence enables more effective test-time scaling as a free lunch. We further propose Confidence-Aware Tes...
| Authors | Yuetian Du, Yucheng Wang, Rongyu Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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