AI RESEARCH
Self-Evolving Spatial Reasoning in Vision Language Models via Geometric Logic Consistency
arXiv CS.CV
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ArXi:2605.18162v1 Announce Type: new Vision-Language Models (VLMs) have made striking progress, yet their spatial reasoning remains fragile: models that answer an original input correctly can still fail under paired transformations with predictable answer mappings, revealing a gap between instance-level correctness and robust spatial reasoning. To address this, we propose Spatial Alignment via Geometric Evolution (SAGE), a self-evolving framework that enforces logical consistency in VLMs through geometric and linguistic duality operations.