From Reasoning Failures to Composable Video Spatial Intelligence

arXiv:2610.01999v1 Announce Type: new Abstract: Spatial reasoning benchmarks evaluate vision-language models across diverse tasks, but task-level scores do not reveal which underlying capabilities account for success or failure. Each task requires recovering spatial evidence, representing geometry, and reasoning over it. We disentangle these capabilities by comparing predicted and ground-truth spatial context under a shared schema and coordinate contract. This comparison reveals four recurring sources of error: inaccurate perception, missing information in the spatial context, selection of the wrong measurement, and errors in reference frames or in tracking position and orientation. Guided by this diagnosis, we develop CROSS, a training-free library of typed geometric operators and spatial skills that function over available evidence to support reliable video spatial reasoning. The resulting library supplies verified context to non-coding VLMs or callable skills to a SpatialClaw agent. We evaluate methodname{} on five benchmarks. methodname{} raises the average score from 55.9% to 60.2% on ReVSI and improves the SpatialClaw result from 62.8% to 66.3% on DSI-Bench. These gains demonstrate that explicit handling of spatial conventions can repair systematic reasoning failures without additional training.

Related

• STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models
• Chain of Spatial Thoughts: Modality-Agnostic Spatial Grounding for Vision Language Models
• SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL
• LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map
• SpatiO: Adaptive Test-Time Orchestration of Vision-Language Agents for Spatial Reasoning

Source: arXiv cs.CV | 2026-10-02