Visual Attention Reasoning via Hierarchical Search and Self-Verification
Wei Cai, Jian Zhao, Yuchen Yuan China Telecom, Tianle Zhang China Telecom, Ming Zhu China Railway Rolling Stock Corporation Limited, Haichuan Tang CRRC ACADEMY, Xuelong Li China Telecom and Northwestern Polytechnical University
Multimodal Large Language Models (MLLMs) frequently hallucinate due to their reliance on fragile, linear reasoning and weak visual grounding. We propose Visual Attention Reasoning (VAR), a reinforcement learning framework that reformulates reasoning as a hierarchical search with self-verification. VAR enforces traceable evidence grounding by generating explicit bounding boxes, guided by a novel reward function combining geometric precision and semantic sufficiency. Furthermore, it replaces linear Chain-of-Thought with a tree-search policy capable of backtracking to correct logical errors. Theoretical analysis validates the framework’s reliability, and extensive experiments demonstrate that VAR significantly outperforms state-of-the-art methods on complex hallucination and safety benchmarks.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表