用联合生成模型重现树冠避让现象，展现群体结构的涌现特性。
Growing a Stand, Not a Tree: Joint Canopy Generation Reproduces Crown Shyness
- 基于注意力机制的集合生成模型，仅通过树间交互实现耦合。
- 生成间隙大小和冠形不对称性与野外实测高度一致。
- 适用于研究群体自组织现象的生成建模任务。
在密闭森林中，相邻树冠常彼此避让，形成由狭窄空隙构成的网络，称为冠层避让现象。该模式属于林分整体而非单棵树，因此为生成建模提出了一个核心问题：能否学习到一种模型，使一组对象之间的结构性特征仅存在于它们之间？本文将林分级树冠生成建模为集合生成问题，采用流匹配模型，树间注意力是唯一耦合通道。模型在资源竞争模拟生成的林分数据上训练，该模拟无法简化为单棵树几何。相比独立生成每棵树的同容量模型，联合模型将间隙分布误差降低一半，且在训练范围外的株密度下仍保持优势。与热带橡树林的野外测量对比，仅需校准一个标量参数，即可在间隙大小和冠形不对称性上达成保留集一致性。间隙的方向统计特性由株位分布决定，而非生长规则，经株位扰动校准后与实地数据吻合。在模拟和学习模型中，冠层避让均为林分属性，而非单棵树属性。
原文摘要 · Abstract (English)
In closed forests, neighboring tree crowns often stop short of touching, leaving a network of narrow gaps known as crown shyness. The pattern belongs to the stand rather than to any single tree, which makes it a natural probe of a question in generative modeling: can a learned model produce a set of objects whose defining structure exists only between them? We formulate stand-level canopy generation as set generation with a flow-matching model, in which attention between trees is the only channel through which coupling can arise. Trained on stands grown by a resource-competition simulation that is provably not reducible to per-tree geometry, the joint model halves the clearance distribution error of an identical-capacity model that generates each tree alone, and the advantage persists at stem densities outside the training range. Against field measurements of a tropical oak forest, a single calibrated scalar yields held-out agreement in gap magnitude and crown asymmetry. The directional statistics of the gaps are controlled by stem placement rather than by the growth rule, and match the field once stem jitter is calibrated. Crown shyness, in both the simulation and the learned model, is a property of the stand and not of the tree.
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