OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics

Although multimodal large language models have shown strong potential for remote sensing disaster assessment and reasoning, most existing methods still follow a direct prediction paradigm that maps multimodal inputs to final answers, lacking an explicit and verifiable intermediate reasoning process aligned with pixel-level visual evidence. This limitation reduces the interpretability and reliability of their outputs. To address this issue, we propose the Disaster Vision-Language Reasoner (DisasterVLR), a unified vision-language reasoning model for remote sensing disaster assessment that improves fine-grained change understanding and reliable reasoning through an explicit intermediate thinking process and multi-task balanced reinforcement learning. DisasterVLR is built on two key techniques. First, self-evolving chain-of-thought generation constructs visual-evidence-constrained seed reasoning trajectories through multi-agent collaboration and iteratively expands them to build a high-quality Disaster Assessment and Reasoning (DAR) dataset. Second, ARSPO++ (Adaptive Reward Scaling Policy Optimization Plus) dynamically adjusts optimization signals through intra-task reward mapping and inter-task weight regulation, alleviating training imbalances caused by differences in task difficulty. Experimental results demonstrate that DisasterVLR achieves superior performance across a comprehensive range of disaster-related tasks, exhibiting stronger overall disaster assessment capabilities than existing natural-scene multimodal models and remote sensing vision-language models. Compared with the strongest competing method, DisasterVLR reduces WAPE by $6.6$–$11.3$ percentage points on damage assessment tasks, including damaged-building counting and damaged-road area estimation, and improves mIoU by $5.31$ points on tasks such as referring expression segmentation.