English(EN) Neuro-Symbolic Synergy for World Modeling

神经符号协同框架增强LLM世界建模能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一个名为神经符号协同（NeSyS）的新框架，以提高大型语言模型（LLM）的世界建模能力。NeSyS结合了LLM的语义表达能力和符号模型的逻辑一致性，解决了LLM在确定性场景中容易产生幻觉的问题。该框架在LLM和符号规则之间交替训练，提高了在ScienceWorld、WebShop和PlanCraft等基准测试中的数据效率和预测准确性。这种方法在开放式任务中通过一步前瞻来提高代理奖励方面也显示出潜力。 AI

影响这种神经符号方法有望在需要严格遵守规则和转换的领域中，实现更可靠、更鲁棒的LLM应用。

排序理由该集群包含一篇详细介绍改进LLM世界建模新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Hongyu Zhao, Siyu Zhou, Haolin Yang, Zengyi Qin, Tianyi Zhou · 2026-09-16 04:00

神经符号协同用于世界建模

arXiv:2602.10480v4 Announce Type: replace Abstract: Large language models (LLMs) exhibit strong general-purpose reasoning capabilities, yet they frequently hallucinate when used as world models (WMs), where strict compliance with deterministic transition rules--particularly in co…

本文如何被排名

报道来源 [1]

神经符号协同用于世界建模

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