Q-GeoMem: Question-Guided Geometric Memory for Video Spatial Reasoning

• URL: http://arxiv.org/abs/2605.27318v1
• Date: Tue, 26 May 2026 17:26:29 GMT
• Title: Q-GeoMem: Question-Guided Geometric Memory for Video Spatial Reasoning
• Abstract summary: We propose a question-guided geometric memory framework for video spatial reasoning.<n>ours injects camera-conditioned geometry into visual tokens and maintains two complementary memories.<n>ours achieves state-of-the-art performance among evaluated spatial reasoning models.
• Score: 53.96129188781529
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video spatial reasoning requires accumulating viewpoint-dependent evidence over time while retaining information useful to the question being asked. Existing spatial video-language models improve geometric perception and long-range context modeling, but often treat memory as a generic temporal cache, which can introduce redundant or irrelevant geometry and weaken long-horizon reasoning. We propose \textbf{\ours}, a question-guided geometric memory framework for video spatial reasoning. \ours injects camera-conditioned geometry into visual tokens and maintains two complementary memories: a Fine-Grained Context Bank for recent dense features and camera states, and a Semantic-Geometric Evidence Bank for compact long-range evidence. Each candidate frame is scored by the product of Q-Former-based question relevance and novelty with respect to the retained bank; this score is stored and reused during reading, while a capacity-based replacement rule keeps the bank compact. During reasoning, both memories are read before update and adaptively fused with the current frame representation. Experiments on VSI-Bench and VSTI-Bench show that \ours achieves state-of-the-art performance among evaluated spatial reasoning models, validating the effectiveness of question-guided geometric memory. Ablations further verify the contribution of the proposed evidence scoring mechanism.

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