爱因斯坦世界模型
Einstein World Models
AI总结：
提出爱因斯坦世界模型（EWM），通过将视觉时间展开嵌入推理轨迹，使LLM能利用视觉化反事实事件增强复杂推理能力。
摘要：
智能是否需要推理超越直接经验的现象？人们自然怀疑某些复杂思维无法仅通过语言捕捉。然而，本文特别关注的是，可视化反事实事件能否作为复杂思维的补充机制。我们询问是否可以通过训练LLM利用这种可视化机制，以有益于其推理能力。受此问题启发，我们提出爱因斯坦世界模型。EWM是基于LLM的推理系统的蓝图，它将视觉时间展开置于推理轨迹中，使LLM能够以文本单独可能无法很好支持的方式进行推理。在EWM中，LLM调用世界模块（不要与世界模型混淆）来生成所考虑场景的短时间展开。返回的展开不被视为答案，而是作为可检查的假设，支持后续推理。爱因斯坦世界模型将LLM的工具调用能力（如网络搜索或代码执行）扩展到视觉思维实验领域。
英文摘要：
Does intelligence require the ability to reason about phenomena beyond direct experience? It is natural to suspect that some complex thought cannot be captured through language alone. However, of particular concern to this work, is whether visualising counterfactual events can complement language as a mechanism for complex thought. We ask whether LLMs can be trained to utilise such visualisation mechanisms, in a way that benefits their reasoning abilities. Motivated by this question, we propose Einstein World Models. EWMs are a blueprint for LLM-based reasoning systems that place visual-temporal rollouts inside the reasoning trace, allowing them to reason in ways that text alone may not support well. In an EWM, the LLM calls a world-module (not to be confused with a world model), to produce short rollouts of scenes under consideration. The returned rollout is treated not as the answer, but as an inspectable hypothesis that can support later reasoning. Einstein World Models extend the capability of LLMs for tool calling (such as web search or code execution), into the domain of visual thought experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
