研究人员开发了 TreeAgent，这是一个新颖的多智能体系统，旨在利用专家规则和视觉语言模型 (VLM) 自动化林业偏倚标注。该框架将专家决策树与 VLM 集成以实现本地化感知，并采用多智能体投票来提高可靠性。该系统利用解耦声明式决策 (D3) 框架在不同专家结构之间实现泛化，显著减少了手动专家标注的需求，同时保持了可解释性。 AI
影响 该框架可以显著降低专业领域专家数据标注的成本和时间。
排序理由 该集群包含一篇详细介绍新框架和系统的学术论文。
在 arXiv cs.MA (Multiagent) 阅读 →
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.31976v1 Announce Type: new Abstract: Human-labeled data are widely used as reference annotations in ML, despite known variability across annotators in many expert-driven domains. In addition, expert annotation is slow, inconsistent, and remains a major bottleneck for s…
Human-labeled data are widely used as reference annotations in ML, despite known variability across annotators in many expert-driven domains. In addition, expert annotation is slow, inconsistent, and remains a major bottleneck for scaling tasks like tree height bias classificatio…