{
  "id": 16093,
  "url": "https://arxiv.org/abs/2608.02470v1",
  "title": "Grounding Agentic VLMs with Dedicated Segmentation for Fine-Grained Vehicle Damage Assessment",
  "summary": "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",
  "authors": "Vishwajeet Shivaji Hogale, Anjali Pai, Nitya Ravi",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T16:37:49.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16093",
  "original_url": "https://arxiv.org/abs/2608.02470v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}