---
id: 20260916-T0-07
title: "LLM智能体能否自主管理长期物理任务？新研究指向自适应物理AI"
title_en: "Can LLM Agents Manage Long-Horizon Physical Tasks? Study Explores Self-Adaptive Physical AI"
url: https://ai.daily.yangsir.net/daily/20260916-T0-07
issue_date: 2026-09-16
publish_date: 2026-09-15T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2609.13436
---

# LLM智能体能否自主管理长期物理任务？新研究指向自适应物理AI

一篇新发表于arXiv的论文探讨了LLM智能体在长期物理任务中的自主管理能力。研究指出，物理任务要求智能体持续观察环境、执行有后果的动作并动态调整策略，这对当前LLM智能体的感知-决策闭环提出了更高要求。论文提出"自适应物理AI"方向，探索如何让LLM智能体在无人工干预下完成多步骤物理操作。目前该研究仅为方法论探索阶段，尚未披露具体基准测试数据或对比结果。

## English Version

**Can LLM Agents Manage Long-Horizon Physical Tasks? Study Explores Self-Adaptive Physical AI**

A new arXiv paper explores whether LLM agents can autonomously manage long-horizon physical tasks. The research notes that physical tasks require agents to continuously observe environments, take consequential actions, and adapt strategies dynamically—posing higher demands on current perception-decision loops. The paper proposes a 'self-adaptive physical AI' direction, exploring how LLM agents can complete multi-step physical operations without human intervention. The work is currently methodological, with no specific benchmark data or comparison results disclosed yet.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2609.13436)

**详情页**：https://ai.daily.yangsir.net/daily/20260916-T0-07

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