LATS通过重尾探索与树状回传，高效发现低概率高价值目标。
LATS: Levy Adaptive Tree Sampling for Feedback-Driven Diverse Target Discovery
- 结合重尾分布与树结构回传，动态调整采样方向
- 在材料科学等任务中显著提升目标发现效率
- 适合需要交互反馈的科学探索场景
尽管扩散模型能捕捉复杂数据分布，但科学发现常需引导生成聚焦于特定、未被充分表征的区域以最大化目标收益。这些高价值模式通常位于低概率尾部区域，需通过交互式反馈逐步揭示。现有扩散采样器在此情境下表现不佳：其继承预训练模型对高密度区域的偏好，导致稀有但有潜力的现象被忽视。相反，过度探索的采样器虽覆盖广泛，却在严格采样预算下难以高效利用高价值模式。为此，我们提出利维自适应树搜索（LATS），一种面向在线反馈驱动搜索的原理性采样框架。LATS利用重尾探索与基于树的值函数回传，逐步发现理想模式。通过保持广泛的分布覆盖，LATS成功探测到低概率、高价值区域，同时保证样本保真度与结构多样性。在包括材料科学在内的多个基准测试中，LATS显著优于基线方法，在目标发现效率上表现突出。
原文摘要 · Abstract (English)
While diffusion models excel at capturing complex data distributions, scientific discovery often requires steering generation toward specific, uncharacterized regions that maximize a target objective. These high-utility modes frequently reside in low-likelihood tail regions and are only revealed sequentially through interactive feedback. Existing diffusion samplers fail in this regime: they inherit the pre-trained model's bias toward high-density regions, leaving rare yet promising phenomena underexplored. Conversely, exploration-heavy samplers ensure broad coverage but fail to efficiently exploit high-utility modes when constrained by a strict sampling budget. To resolve this dilemma, we introduce Levy Adaptive Tree Search (LATS), a principled sampling framework for online feedback-driven search. LATS leverages heavy-tailed exploration coupled with tree-based value backpropagation to progressively uncover preferred modes. By maintaining broad distributional coverage, LATS successfully discovers low-likelihood, high-utility regions while preserving sample fidelity and structural diversity. Experiments across diverse benchmarks, including materials science, demonstrate that LATS significantly outperforms baselines in target discovery efficiency.
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