SVSR: A Self-Verification and Self-Rectification Paradigm for Multimodal Reasoning
Current multimodal models often suffer from shallow reasoning, leading to errors caused by incomplete or inconsistent thought processes. To address this limitation, we propose Self-Verification and Self-Rectification (SVSR), a unified framework that explicitly integrates self-verification and self-rectification into the model's reasoning pipeline, substantially improving robustness and reliability in complex visual understanding and multimodal reasoning tasks. SVSR is built on a novel three-stage training paradigm. First, we construct a high-quality unified preference dataset by refining reasoning traces from pre-trained vision-language models, incorporating both forward and backward reas...
| Authors | Zhe Qian, Nianbing Su, Zhonghua Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 April 2026 |
| Last updated at source | 28 May 2026 |
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