English(EN) From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems

GPT-5赋能网络物理系统新型LLM规划框架

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了一个分层反馈框架，以提高像GPT-5这样的大型语言模型（LLM）为复杂网络物理系统生成可执行计划的能力。该框架通过提供关于计划失败的信息性反馈，指导修订以克服误解、不可行性和安全约束违反，从而解决了当前LLM规划的局限性。实验表明，在七种不同的CPS场景中，生成满足自然语言任务的可执行计划的成功率为87.9%。 AI

影响增强了LLM在复杂系统控制方面的能力，有可能改善网络物理领域的自动化和执行。

排序理由关于LLM规划新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jinglin Dai, Jiawan Wang, Ruiqi Hong, Lei Bu · 2026-10-19 04:00

从自然语言意图到可执行控制：面向多组件网络物理系统的分层反馈引导大模型规划

arXiv:2610.22441v1 Announce Type: new Abstract: Large language models (LLMs) provide a convenient interface for translating natural language commands into task plans for cyber-physical systems (CPSs). However, two gaps hinder their use for real execution: generated plans may misi…

本文如何被排名

报道来源 [1]

从自然语言意图到可执行控制：面向多组件网络物理系统的分层反馈引导大模型规划

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