Rui Gong, Ruoyu Wang, Yalun Dai, Hao Li, Sidi Yang, Qihao Zhao, Hongyu Li, Zeyang Guan · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28354 · open-access PDF
공간적 추론은 모델이 객체가 3D 공간에서 어디에 있는지, 어떻게 서로 관련되어 있는지, 그리고 어떻게 움직이는지를 파악하도록 요구한다.Agentic VLMs는 최근에 모델이 3D 인식 도구로 증거를 적극적으로 수집하도록 허용함으로써 이러한 작업에 큰 개선을 가져왔습니다.그럼에도 불구하고 많은 질문에서 도구는 올바른 증거를 반환하고 모델은 여전히 올바른 대답에 도달하지 않습니다.모델과 도구 사이의 격차는 사용 가능한 결론이 될 때 남아 있습니다.우리는 S-Agent 2.0을 제시합니다.
Machine translation (Meta M2M100); the English original follows.
Spatial reasoning asks a model to work out where objects are in 3D space, how they relate to one another, and how they move. Agentic VLMs have recently brought large improvements on such tasks by letting the model actively gather evidence with 3D perception tools instead of answering in a single pass. Even so, on many questions the tools return the right evidence and the model still does not arrive at the correct answer: the gap between model and tools, where raw readings become usable conclusions, is left unfilled. We present S-Agent 2.0, a spatial reasoning agent that equips an off-the-shelf
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