Grounding Agentic VLMs with Dedicated Segmentation for Fine-Grained Vehicle Damage Assessment

Vision-language models (VLMs) are increasingly deployed as reasoning agents in real-world visual assessment pipelines, yet their spatial grounding remains unreliable for fine-grained, visually ambiguous targets. We study this gap in the context of automated vehicle damage assessment, where fine-grained defects such as scratches and hairline cracks occupy few pixels, produce weak gradient signal, and are easily confused with reflections and surface texture. We show that a state-of-the-art VLM (Qw

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ethics.ai (3 August 2026), “Grounding Agentic VLMs with Dedicated Segmentation for Fine-Grained Vehicle Damage Assessment,” evidence record 16093, https://ethics.ai/record/16093 (originally published by arXiv cs.AI).

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