推理还是背诵：结合LLM评判器的互补专家模型用于交通异常推理
Reason or Recite: Complementary Specialists with an LLM Judge for Traffic Anomaly Reasoning
AI总结：
针对交通异常推理任务，提出双专家框架RoR，结合LoRA微调的9B视觉语言模型、吸收标注风格的4位量化27B模型及无训练LLM评判器，在2026年AI城市挑战赛对应赛道域内排行榜获第一。
摘要：
碰撞、违规、突发危险等道路异常对视频理解的要求超出了检测范畴：实用系统必须验证发生了什么、描述场景并解释原因。我们发现这些能力对视觉语言模型存在反向拉扯：封闭式问题奖励精确、基于证据的推理，而开放式答案则通过与参考文本的相似度评分，匹配标注风格与推理正确同等重要。我们提出Reason-or-Recite（RoR），这是面向2026年AI城市挑战赛交通异常推理赛道的双专家框架。RoR包含三个部分：（1）将官方思维链标注重写为结构化、时间锚定的模式，并在增强轨迹上对9B视觉语言模型进行LoRA微调；（2）仅在答案上对4位量化的27B模型进行微调，吸收标注风格；（3）通过无训练的LLM评判器为每个问题在两个候选结果间进行选择。RoR在域内排行榜上以平均得分0.6788排名第一。
英文摘要：
Road anomalies such as collisions, violations, and sudden hazards demand more from video understanding than detection: a practical system must verify what happened, describe the scene, and explain the cause. We find these competences pull in opposite directions for vision-language models: closed-form questions reward precise, evidence-grounded reasoning, while open-ended answers are scored by similarity to reference text, where matching the annotation style matters as much as reasoning correctly. We present Reason-or-Recite (RoR), a two-specialist framework for the Traffic Anomaly Reasoning track of the 2026 AI City Challenge. RoR (1) rewrites the official chain-of-thought annotations into a structured, temporally grounded schema and LoRA-tunes a 9B vision-language model on the enhanced traces; (2) fine-tunes a 4-bit-quantized 27B model on answers alone, absorbing the annotation style; and (3) selects between the two candidates per question with a training-free LLM judge. RoR ranks first on the in-domain leaderboard with mean score 0.6788.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
