Qwen-VLM做车辆损伤评估会瞎编，TinyDamage用分割模型接地，幻觉率从92%降到31%，修车定损能用了。
最新研究表明，Qwen-VLM在车辆损伤语义分类上达87.3%，但空间定位会漏掉细长划痕并在反光区域产生幻觉。为此提出的TinyDamage将空间接地交给专用多任务分割模型，VLM只负责语义推理和报告生成。实验发现，常用focal loss会使微小损伤检测归零，而监督对比损失能显著提升损伤/背景可分性。在100份人工验证报告上，TinyDamage将报告幻觉率从纯文本的92%和纯图像的78%降至31%，并引入逐类别检测指标DET_l。
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 (Qwen-VL) achieves strong semantic classification accuracy (87.3%) on this task but is systematically ungrounded at the spatial level: it hallucinates damage in reflective regions, misses elongated scratches entirely, and produces spatially inconsistent outputs when prompted for localization. We propose TinyDamage, a hybrid architecture that delegates spatial grounding to a dedicated multi-task segmentation model while reserving the VLM for semantic reasoning and report generation. On the segmentation side, we find that the choice of loss function has an outsized and underexplored effect on tiny-object grounding: focal loss, widely used for class imbalance, collapses tiny-damage detection to zero, while a supervised contrastive objective measurably improves damage/background separability. We integrate the segmentation model into a 7-node LangGraph agent pipeline that grounds every VLM generation step in the segmentation output, and show that this grounding reduces the report hallucination rate from 92% (text-only) and 78% (image-only) to 31% in a controlled evaluation on 100 human-verified reports. We introduce DET_l, a permissive per-category detection metric for evaluating tiny-object grounding under class imbalance, and report latency and reliability characteristics of the deployed pipeline.