OpenNovelty.org

ATLAS: Constraints-Aware Multi-Agent Collaboration for Real-World Travel Planning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Multi-agent orchestrationReal-world travel planningConstraints-aware planning
Abstract:

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%).

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces ATLAS, a multi-agent framework for constraint-aware travel planning that leverages large language models to handle explicit, implicit, and evolving constraints through dynamic constraint management, iterative critique, and adaptive search. Within the taxonomy, ATLAS resides in the 'LLM-Based Multi-Agent Planning with Constraints' leaf, which contains only two papers total (including ATLAS itself). This indicates a relatively sparse and emerging research direction compared to more established branches like Multi-Agent Path Planning or Information-Theoretic Planning, which contain multiple subtopics and numerous papers.

The taxonomy reveals that ATLAS sits at the intersection of several active research streams. Its nearest neighbors include 'Retrieval-Augmented Generation for Planning' (containing RAP-RAG and similar methods) and 'Domain-Specific Constrained Planning Applications' (which includes travel planning work like Construction Planning Graph and Search Rescue Planning). Unlike classical robotic information gathering approaches that rely on belief-space planning and sensor optimization, ATLAS leverages natural language reasoning and dynamic web search. The taxonomy's scope notes clarify that LLM-based multi-agent planning focuses on collaborative reasoning under constraints, distinguishing it from single-agent LLM systems and non-LLM planning frameworks.

Among thirty candidates examined across three contributions, none were found to clearly refute any aspect of ATLAS. The first contribution (ATLAS framework) examined ten candidates with zero refutable matches, suggesting limited direct prior work on multi-agent LLM frameworks specifically designed for constraint-aware travel planning. Similarly, the formalization of travel planning as a dynamic constraint satisfaction problem and the real-world demonstration with live search both examined ten candidates each without finding overlapping prior work. This pattern indicates that within the limited search scope, ATLAS appears to occupy a relatively unexplored niche combining LLM-based multi-agent coordination with dynamic constraint handling.

Based on the top-30 semantic matches examined, ATLAS demonstrates novelty in applying multi-agent LLM architectures to constraint-aware planning with live information retrieval. However, the analysis is inherently limited by search scope and does not cover the full breadth of travel planning, constraint satisfaction, or LLM agent literature. The sparse population of its taxonomy leaf and absence of refuting candidates suggest genuine contribution, though comprehensive assessment would require broader examination of adjacent fields like classical planning with information actions and domain-specific travel optimization.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: constraint-aware planning with dynamic information search. This field addresses scenarios where agents must plan under resource, time, or communication constraints while actively gathering information to reduce uncertainty. The taxonomy reveals a rich landscape spanning multiple dimensions. Multi-Agent and Multi-Robot Coordination branches explore how teams of robots or agents coordinate under communication limits and shared resource budgets, as seen in works like Swarm Communication Planning[9] and Multi Robot Communication[21]. Information-Theoretic Path Planning and Information Gathering with Sensor and Perception Constraints focus on optimizing sensor placement and trajectory design to maximize information gain, exemplified by Robotic Information Gathering[12] and Non Myopic Gathering[27]. Retrieval-Augmented and Knowledge-Enhanced Planning branches emphasize how external knowledge bases can inform decision-making, with methods like RAP-RAG[7] and Constrained Generative Retrieval[24]. Meanwhile, LLM-Based Multi-Agent Planning with Constraints represents an emerging direction where large language models orchestrate multi-step reasoning and tool use under various operational constraints, and Domain-Specific Constrained Planning Applications tackle specialized settings from underwater exploration (Underwater Genetic Planning[13]) to medical procedures (CDMP Orthopedic Surgery[6]). Particularly active lines of work contrast classical robotic information gathering—where belief-space planning and sensor scheduling dominate (Belief Space Planning[23], Temporal Logic Gathering[16])—with newer LLM-driven approaches that leverage natural language reasoning and retrieval. ATLAS[0] sits squarely within the LLM-Based Multi-Agent Planning with Constraints branch, emphasizing how language models can dynamically search for and integrate information while respecting task-specific constraints. This positions it alongside Human Like Reasoning[46], which also explores cognitive-style planning with LLMs, but ATLAS[0] places stronger emphasis on the interplay between constraint satisfaction and adaptive information retrieval. Compared to purely retrieval-focused methods like Adaptive Reasoning Retrieval[20] or domain-specific planners like Construction Planning Graph[2], ATLAS[0] aims for a more general framework that balances reasoning depth with the pragmatic need to gather just enough information under tight operational bounds. Open questions remain around scalability, the trade-off between exploration and exploitation in information search, and how to best integrate symbolic constraints with neural retrieval mechanisms.

Claimed Contributions

ATLAS multi-agent framework for constraint-aware travel planning

The authors introduce ATLAS, a multi-agent framework that addresses three fundamental challenges in constraint-aware planning: dynamic constraint management (identifying explicit and implicit constraints), iterative plan critique (generating valid plans through Planner-Checker loops), and adaptive interleaved search (resolving information gaps when plans fail due to insufficient data).

10 retrieved papers
Formalization of travel planning as a dynamic constraint satisfaction problem

The authors formalize travel planning as a Constraint Satisfaction Problem (CSP) with variables, domains, and constraints, distinguishing between explicit constraints (user-stated goals) and implicit constraints (commonsense domain rules). They extend this to a Dynamic CSP for multi-turn scenarios where constraints evolve across conversation turns.

10 retrieved papers
First quantitative demonstration on real-world travel planning with live search and multi-turn feedback

The authors provide the first quantitative evaluation of a travel planning system in a realistic setting that combines real-time web search with multi-turn user feedback. ATLAS achieves an 84% final pass rate in this live environment, significantly outperforming baselines like ReAct (59%) and a monolithic agent (27%).

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ATLAS multi-agent framework for constraint-aware travel planning

The authors introduce ATLAS, a multi-agent framework that addresses three fundamental challenges in constraint-aware planning: dynamic constraint management (identifying explicit and implicit constraints), iterative plan critique (generating valid plans through Planner-Checker loops), and adaptive interleaved search (resolving information gaps when plans fail due to insufficient data).

Contribution

Formalization of travel planning as a dynamic constraint satisfaction problem

The authors formalize travel planning as a Constraint Satisfaction Problem (CSP) with variables, domains, and constraints, distinguishing between explicit constraints (user-stated goals) and implicit constraints (commonsense domain rules). They extend this to a Dynamic CSP for multi-turn scenarios where constraints evolve across conversation turns.

Contribution

First quantitative demonstration on real-world travel planning with live search and multi-turn feedback

The authors provide the first quantitative evaluation of a travel planning system in a realistic setting that combines real-time web search with multi-turn user feedback. ATLAS achieves an 84% final pass rate in this live environment, significantly outperforming baselines like ReAct (59%) and a monolithic agent (27%).

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