Researchers have proposed a new framework called VISOR to improve the performance of vision-language models in tasks that require multi-step reasoning. The framework addresses two main challenges: visual evidence sparsity and search drift in long horizons. VISOR uses a structured Evidence Space for progressive cross-page reasoning, a Visual Action Evaluation and Correction mechanism, and a Dynamic Trajectory with Sliding Window and Intent Injection to mitigate these issues. The authors claim that their approach achieves state-of-the-art performance on several benchmark datasets while maintaining reasonable computational costs. --- Why it matters: This matters because it improves the ability of AI systems to perform complex visual reasoning tasks, which are essential in applications such as image captioning, visual question answering, and document understanding. Source: https://arxiv.org/abs/2604.09508