MOTIVE teaches VLMs multi-perspective self-verification and when to rethink answers
Ziquan Zhu · hf · 2026-10-12
MOTIVE, from Leicester, NJUPT, UNC, Amazon, Meta AI and others, is a multi-view self-verification framework for vision-language models. The authors first show verifier capability matters and verification is highly prompt-sensitive. MOTIVE evaluates candidate answers from complementary perspectives, learns a correctness-aligned reliability score, and uses it to accept reliable answers or trigger history-guided rethinking — cutting unnecessary reasoning turns without an external judge. It consistently beats strong self-verification and self-correction baselines across benchmarks and VLM backbones.
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