多智能体协作框架让大模型更懂现实旅行中的复杂约束。
ATLAS: Constraints-Aware Multi-Agent Collaboration for Real-World Travel Planning
- 设计专用机制实现动态约束管理与计划迭代评审
- 在真实场景下将任务通过率提升至84%
- 适合需要处理实时信息和多轮交互的规划任务
尽管大语言模型在推理和工具使用方面取得显著进展，但在复杂约束下仍难以生成最优且符合实际的解决方案。现实旅行规划正是此类挑战的典型例证，要求智能体能够应对显性、隐性和随环境与用户需求动态变化的约束。本文提出ATLAS，一种通用的多智能体框架，旨在有效处理旅行规划中约束感知的复杂性。ATLAS引入了动态约束管理、迭代计划评审和自适应交错搜索等专用机制，显著提升了规划能力。在TravelPlanner基准测试中，其最终通过率从23.3%提升至44.4%，优于现有最佳方法。更重要的是，本工作首次在包含实时信息检索与多轮反馈的真实场景中量化验证了有效性：ATLAS实现84%的最终通过率，远超ReAct（59%）和单体智能体（27%）。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) have shown remarkable advancements in reasoning and tool use, they often fail to generate optimal, grounded solutions under complex constraints. Real-world travel planning exemplifies these challenges, evaluating agents' abilities to handle constraints that are explicit, implicit, and even evolving based on interactions with dynamic environments and user needs. In this paper, we present ATLAS, a general multi-agent framework designed to effectively handle such complex nature of constraints awareness in real-world travel planning tasks. ATLAS introduces a principled approach to address the fundamental challenges of constraint-aware planning through dedicated mechanisms for dynamic constraint management, iterative plan critique, and adaptive interleaved search. ATLAS demonstrates state-of-the-art performance on the TravelPlanner benchmark, improving the final pass rate from 23.3% to 44.4% over its best alternative. More importantly, our work is the first to demonstrate quantitative effectiveness on real-world travel planning tasks with live information search and multi-turn feedback. In this realistic setting, ATLAS showcases its superior overall planning performance, achieving an 84% final pass rate which significantly outperforms baselines including ReAct (59%) and a monolithic agent (27%).
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