ViewFusion: Structured Spatial Thinking Chains for Multi-View Reasoning

Multi-view spatial reasoning remains difficult for currentvision-language models. Even when multiple viewpoints are available,models often underutilize cross-view relations and instead rely on single-image shortcuts, leading to fragile performance on viewpoint transforma-tion and occlusion-sensitive cases. We present ViewFusion, a two-stageframework that explicitly separates cross-view spatial pre-alignment fromquestion answering. In the first stage, the model performs deliberatespatial pre-thinking to infer viewpoint relations and spatial transfor-mations across views, forming an intermediate workspace that goes be-yond a simple re-description. In the second stage, the model conductsquestion-driven reasoning conditioned on this workspace to producethe final prediction. We train ViewFusion with synthetic reasoningsupervision followed by reinforcement learning using GRPO, which im-proves answer correctness while stabilizing the intended two-stage gen-eration behavior. On MMSI-Bench, ViewFusion improves accuracyby 5.3% over Qwen3-VL-4B-Instruct, with the largest gains on exam-ples that require genuine cross-view alignment. Our code is available athttps://github.com/taoxj2001/ViewFusion.