林业标注太费人力？TreeAgent用专家规则加VLM自动标注偏差，比传统监督学习强还省心。
TreeAgent是一个多智能体框架，通过编译专家决策树与视觉语言模型(VLM)实现自动化偏差标注。其解耦声明式决策(D3)框架允许在不同专家定义的决策结构间零修改泛化。在树高偏差分类测试中，TreeAgent的表现优于监督式机器学习基线，并显著减少了专家标注所需的工作量。
TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
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 classification in forestry remote sensing. We propose a multi-agent system (MAS) that orchestrates expert decision trees with Vision-Language Models (VLMs), treating the decision tree as a structural prior while VLMs perform localized semantic perception at individual nodes, with multi-agent voting to mitigate VLM stochasticity. We formalize a Decoupled Declarative Decision (D3) Framework that enables zero-modification generalization across diverse expert-defined decision structures. On a tree bias classification testbed, our framework outperforms supervised ML baselines and reduces the amount of expert labeling effort required. These results suggest that agentic orchestration of VLMs with expert priors can reproduce expert-defined labeling procedures at substantially lower annotation cost while maintaining interpretability.