发表机构
The Chinese University of Hong Kong, Shenzhen; Shanghai Artificial Intelligence Laboratory（香港中文大学（深圳）; 上海人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出潜在JEPA框架，结合自回归学习与联合嵌入预测，通过训练连续潜在思维预测未来视图，在ChemCoTBench上提升分子优化及编辑反应指标，验证抽象未来预测作为连接潜在推理与科学结果的学习原则。
AI 中文摘要
大型语言模型为化学推理提供了有前景的基础，将化学知识与多步问题解决相结合。化学直觉可以在解决方案细节完全制定之前，提供对可能结果的初步感知。受这种预期如何补充显式分析的启发，我们研究了如何训练连续潜在思维，以在不逐字表达每个中间步骤的情况下，预测未来解决方案的信息性方面。我们引入了潜在JEPA，一个结合自回归学习与一个或多个未来视图的联合嵌入预测的框架。对于化学推理，我们开发了文本和分子预测目标，将潜在思维与后续推理及分子结果联系起来。在ChemCoTBench上的实验显示，在分子优化以及多个编辑和反应指标上取得了改进。表征分析表明，未来预测使潜在思维对分子结果更具信息性，并加强了它们与化学结构的对应关系。这些发现支持抽象未来预测作为将连续潜在推理与科学结果联系起来的学习原则。
英文摘要
Large language models offer a promising foundation for chemical reasoning, bringing together chemical knowledge and multistep problem solving. Chemical intuition can provide an initial sense of plausible outcomes before the details of a solution are fully worked out. Inspired by how such expectations complement explicit analysis, we study how continuous latent thoughts can be trained to anticipate informative aspects of future solutions without verbalizing every intermediate step. We introduce Latent JEPA, a framework that combines autoregressive learning with joint-embedding prediction of one or more future views. For chemical reasoning, we develop textual and molecular prediction objectives that connect latent thoughts to both subsequent reasoning and molecular outcomes. Experiments on ChemCoTBench show gains in molecular optimization and on several editing and reaction metrics. Representation analyses show that future prediction makes latent thoughts more informative about molecular outcomes and strengthens their correspondence with chemical structure. These findings support abstract future prediction as a learning principle for connecting continuous latent reasoning with scientific outcomes.
