AI RESEARCH
Inference-Time Structural Reasoning for Compositional Vision-Language Understanding
arXiv CS.CL
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ArXi:2603.27349v1 Announce Type: cross Vision-language models (VLMs) excel at image-text retrieval yet persistently fail at compositional reasoning, distinguishing captions that share the same words but differ in relational structure. We present, a unified evaluation and augmentation framework benchmarking four architecturally diverse VLMs,CLIP, BLIP, LLaVA, and Qwen3-VL-8B-Thinking,on the Winoground benchmark under plain and scene-graph-augmented regimes. We