When to Rethink: Learning Multi-Perspective Self-Verification for Vision-Language Models

Researchers posted arXiv paper 2610.07018v1, "When to Rethink: Learning Multi-Perspective Self-Verification for Vision-Language Models," introducing MOTIVE, a Multi-View Self-Verification with Reliability-Guided Selective Rethinking framework that scores candidate answers from complementary verification perspectives and uses that score to decide whether to accept an answer or trigger history-guided rethinking. The authors report that stronger verifiers give more reliable judgments while verification performance is highly sensitive to prompt choice, with no single prompt dominating across tasks, and that MOTIVE consistently outperforms strong self-verification and self-correction baselines across diverse multimodal benchmarks and VLM backbones while reducing unnecessary reasoning turns without an external judge.

arXiv:2610.07018v1 Announce Type: new Abstract: Vision-language models VLMs have achieved strong performance in multimodal reasoning, yet they remain prone to generating plausible but incorrect answers. Self-verification offers a practical way to improve answer reliability without relying on external judges, but existing methods typically depend on a single verification criterion or fixed prompt, resulting in incomplete and unstable reliability estimates. We first systematically analyze how verifier capability and prompt design affect verification performance. Our findings show that stronger verifiers provide more reliable judgments, while verification performance is highly sensitive to prompt choice, with no single prompt consistently dominating across tasks. Guided by these findings, we propose \texttt{MOTIVE}, a \textbf{M}ulti-View Self-Verificati\textbf{O}n wi\textbf{T}h Rel\textbf{I}ability-Guided Selecti\textbf{VE} Rethinking framework for reliable multimodal reasoning. \texttt{MOTIVE} evaluates each candidate answer from complementary verification perspectives and learns a correctness-aligned reliability score through correctness-grounded multi-view verification learning. During inference, this score governs an accept-or-rethink decision, allowing reliable answers to be returned directly while uncertain ones trigger history-guided rethinking. Extensive experiments across diverse multimodal benchmarks and VLM backbones demonstrate that \texttt{MOTIVE} consistently outperforms strong self-verification and self-correction baselines. Further results show that reliable verification improves accept-or-rethink decisions and reduces unnecessary reasoning turns, enabling more reliable and efficient self-verification without an external judge.