AI RESEARCH
PolarVLM: Bridging the Semantic-Physical Gap in Vision-Language Models
arXiv CS.CV
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ArXi:2605.07574v1 Announce Type: new Mainstream vision-language models (VLMs) fundamentally struggle with severe optical ambiguities, such as reflections and transparent objects, due to the inherent limitations of standard RGB inputs. While polarization imaging captures polarimetric physical parameters that resolve these ambiguities, existing methods are constrained by fixed-format outputs and remain isolated from open-ended reasoning. To bridge this semantic-physical gap, we