arXiv cs.AI· Munachiso Samuel Nwadike, Zangir Iklassov, Ali Mekky, Zayd M. Kawakibi Zuhri, Kentaro Inui·· 3 小时前AI 评分38
Einstein World Models：把视觉时序推演嵌入 LLM 推理链的架构蓝图
Einstein World Models
AI 导读
arXiv 论文提出 Einstein World Models（EWMs），这是一套基于 LLM 的推理系统蓝图，在推理链中插入视觉时序推演（visual-temporal rollouts），让模型以文本之外的方式思考。
正文 · 原文
Abstract: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.
| Comments: | 12 pages (9 without references), 2 figures, 1 algorithm |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2606.26969 [cs.AI] |
| (or arXiv:2606.26969v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26969 arXiv-issued DOI via DataCite |
Submission history
From: Munachiso Nwadike [view email]
[v1]
Thu, 25 Jun 2026 12:42:04 UTC (1,478 KB)
[v2]
Tue, 10 Nov 2026 00:19:48 UTC (1,479 KB)
来源：arXiv cs.AI · arxiv.org