AI 中文总结
针对长时程原子演化中快照模糊性问题，提出记忆恢复世界模型AtomWorld-Mem，通过空间编码与时间记忆整合恢复潜在状态，提升演化效率并实现零样本迁移。
AI 中文摘要
高保真的原子级演化在长时间尺度上需要的不仅仅是观察当前的晶体构型。瞬时的原子快照往往是不完整的：局部相似的构型可能对应不同的隐藏动力学背景、未来事件偏好和等待时间尺度。我们认为，这种快照模糊性使得长时程原子演化从根本上成为一个基于记忆的世界状态恢复问题。为了解决这一问题，我们引入了AtomWorld-Mem，一种记忆恢复的原子世界模型，它恢复了瞬时晶体快照中缺失的潜在世界状态。AtomWorld-Mem将演化的合金视为一个原子世界：空间编码器从密集的局部拓扑和稀疏的长程缺陷背景中写入多尺度的原子关键帧，而短期事件记忆和长期结构记忆在时间上整合这些关键帧，以恢复一个未来可预测的演化状态。恢复的状态用于在单事件动力学蒙特卡洛（KMC）约束下优先处理合法的空位介导事件，而事件的合法性、物理执行和驻留时间更新仍由底层模拟器控制。实验上，AtomWorld-Mem在固定的微观事件预算下改善了长时程原子演化的进展，同时保持了在能量、结构和空位输运可观测量上的高保真演化。它进一步在多种未见过的合金-温度原子世界中实现了零样本迁移，这表明学习到的记忆恢复机制捕获了可复用的隐藏状态推断原则，而非特定于系统的局部能量启发式。这些结果将记忆恢复的世界状态建模定位为一条通往高效、物理基础扎实且可迁移的原子演化的有前景的途径。
英文摘要
High-fidelity atomistic evolution over long timescales requires more than observing the current crystal configuration. Instantaneous atomistic snapshots are often incomplete: locally similar configurations can correspond to different hidden dynamical contexts, future event preferences, and waiting-time scales. We argue that this snapshot ambiguity makes long-horizon atomistic evolution fundamentally a memory-based world-state restoration problem. To address this, we introduce AtomWorld-Mem, a memory-restored atomistic world model that recovers the latent world state missing from instantaneous crystal snapshots. AtomWorld-Mem treats the evolving alloy as an AtomWorld: spatial encoders write multi-scale atomistic keyframes from dense local topology and sparse long-range defect context, while short-term event memory and long-term structural memory integrate these keyframes across time to restore a future-predictive evolutionary state. The restored state is used to prioritize legal vacancy-mediated events under single-event Kinetic Monte Carlo (KMC) constraints, while event legality, physical execution, and residence-time updates remain governed by the underlying simulator. Empirically, AtomWorld-Mem improves long-horizon atomistic progress under fixed microscopic event budgets while maintaining high-fidelity evolution across energetic, structural, and vacancy-transport observables. It further transfers zero-shot across diverse unseen alloy-temperature AtomWorlds, suggesting that the learned memory-restoration mechanism captures reusable principles of hidden-state inference rather than a system-specific local energy heuristic. These results position memory-restored world-state modeling as a promising route toward efficient, physically grounded, and transferable atomistic evolution.
