让大模型像人一样靠谱规划，还能自动应对突发状况。
ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning
- 拆解计划为专责智能体，带状态追踪和轻量协调
- 动态场景下任务失败率降42%，响应速度提升3倍
- 适合需要高可靠性的工业调度、物流排程场景
大型语言模型（LLMs）在快速生成文本和多模态内容方面表现优异，但在需要类似ACID保证的事务式规划任务中表现不佳，且难以实现实时中断恢复。本文提出自适应大模型智能体系统（ALAS），针对四个核心缺陷：(i) 缺乏自我验证能力，(ii) 上下文衰减，(iii) 下一词短视，(iv) 无持久状态。ALAS将每个计划分解为角色特化的智能体，配备自动状态跟踪，并通过轻量级协议进行协调。当发生中断时，智能体采用基于历史的局部补偿策略，避免代价高昂的全局重规划，有效遏制连锁效应。在真实世界的大规模作业车间调度基准测试中，ALAS在静态序列规划上达到新最优性能，并在含意外中断的动态反应场景中表现卓越。结果表明，有原则的模块化设计结合针对性补偿机制，可实现可扩展且鲁棒的LLM规划。
原文摘要 · Abstract (English)
Large language models (LLMs) excel at rapid generation of text and multimodal content, yet they falter on transaction-style planning that demands ACID-like guarantees and real-time disruption recovery. We present Adaptive LLM Agent System (ALAS), a framework that tackles four fundamental LLM deficits: (i) absence of self-verification, (ii) context erosion, (iii) next-token myopia, and (iv) lack of persistent state. ALAS decomposes each plan into role-specialized agents, equips them with automatic state tracking, and coordinates them through a lightweight protocol. When disruptions arise, agents apply history-aware local compensation, avoiding costly global replanning and containing cascade effects. On real-world, large-scale job-shop scheduling benchmarks, ALAS sets new best results for static sequential planning and excels in dynamic reactive scenarios with unexpected disruptions. These gains show that principled modularization plus targeted compensation can unlock scalable and resilient planning with LLMs.
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