TBNAI
When to Rethink: Learning Multi-Perspective Self-Verification for Vision-Language Models
Publication: cs.AI updates on arXiv.org
Author: Ziquan Zhu, Hanruo Zhu, Si-Yuan Lu, Morris Yu-Chao Huang, Yicheng Lin, Wei Han, Tianlong Chen, Mingyuan Wu, Hanchao Yu, Gaojie Jin, Lu Liu, Bo Sun, Tianjin Huang
Source published: 07/10/2026, 04:00:00 UTC
Briefing created: 26/10/2026, 03:59:25 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 02:18:24 UTC.
Briefings
A new framework called MOTIVE was developed to improve the reliability of vision-language models by using multi-view self-verification. The system evaluates answers from different perspectives and learns a correctness-aligned reliability score to decide whether to accept or re-think an answer. Experiments show MOTIVE consistently outperforms existing self-verification and self-correction baselines across diverse benchmarks.
Key points
- The framework MOTIVE uses multi-view self-verification to evaluate answers from complementary perspectives.
- MOTIVE learns a correctness-aligned reliability score to guide accept-or-rethink decisions.
Context
Vision-language models achieve strong multimodal reasoning but often generate plausible but incorrect answers. Existing self-verification methods rely on single criteria or fixed prompts, leading to unstable reliability estimates. This research addresses these limitations by proposing a multi-view approach.
Limitations
The study was conducted on arXiv preprint and does not include experimental results from external benchmarks or deployment on production systems. Results are limited to the specific multimodal benchmarks and VLM backbones tested in the preprint.
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