Analyzing Information Sharing and Coordination in Multi-Agent Planning
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Multi-agent systems (MASs) have pushed the boundaries of large language model (LLM) agents in domains such as web research and software engineering. However, long-horizon, multi-constraint planning tasks involve conditioning on detailed information and satisfying complex interdependent constraints, which can pose a challenge for these systems. In this study, we construct an LLM-based MAS for a travel planning task which is representative of these challenges. We evaluate the impact of a notebook to facilitate information sharing, and evaluate an orchestrator agent to improve coordination in free form conversation between agents. We find that the notebook reduces errors due to hallucinated details by 18%, while an orchestrator directs the MAS to focus on and further reduce errors by up to 13.5% within focused sub-areas. Combining both mechanisms achieves a 25% final pass rate on the TravelPlanner benchmark, a 17.5% absolute improvement over the single-agent baseline's 7.5% pass rate. These results highlight the potential of structured information sharing and reflective orchestration as key components in MASs for long horizon planning with LLMs.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 13 of its 16 references that are in the map. First posted on arXiv 18 Aug 2025.
Builds on
13 of 16 references.
Closest ancestors · 4
Same direction, newest first
- 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks18 Dec
- 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains17 Jun
- 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agents2 Feb
- 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar
Other references · 9
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
- 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
- 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 316
- 2023Self-collaboration Code Generation via ChatGPTcited by 258
- 2025Learning Adaptive Parallel Reasoning with Language Modelscited by 58
- 2024Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Biascited by 20
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2024Can We Rely on LLM Agents to Draft Long-Horizon Plans? Let's Take TravelPlanner as an Example
- 2024Understanding the planning of LLM agents: A survey
- 2025LLMs Working in Harmony: A Survey on the Technological Aspects of Building Effective LLM-Based Multi Agent Systems
- 2024Large Language Model based Multi-Agents: A Survey of Progress and Challenges
- 2024A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models
- 2026Architectural Design, Not Only Model Intelligence, Governs Multi-Agent LLM Performance
- 2025Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
- 2025ATLAS: Constraints-Aware Multi-Agent Collaboration for Real-World Travel Planning