arXiv · cs.AI · Bing Hu, Rishabh Sharma, Jatin Mehta · 12 小时前模型评分50
Taurus：大语言模型（LLM，Large Language Model） 在环的 CT 标注以一半成本追平编码智能体
Taurus: LLM-in-the-Loop CT Annotation Matches Coding Agents at Half the Cost
导读
研究团队构建了 Taurus 框架，让大语言模型（LLM，Large Language Model）在自动化标注流程中审阅 CT 主动脉分割结果，而非自由编写分割代码。
分类置信度
中 · 待人工复核
- 只有单一媒体来源，需要人工复核。
来源级别：媒体与个人
应用领域：医疗与生命科学
事件类型：研究发布
证据摘录
- “We built a harness, Taurus, and compared it with free-form agents by blind label recovery: we withheld the human labels of six public datasets (939 volumes) and scored every approach against each dataset's own classes.”
- “It matched the surface smoothness of the human references, and in the primary metric we found no difference from the free-form Opus agent, at under half its cost, $1.22 against $2.71 per case.”
来源：arXiv · cs.AI · arxiv.org