mlsanityData to 14 Oct 2026

Decentralized Multi-Agent Goal Assignment for Path Planning using Large Language Models

arXiv 2510.23824 ↗First posted 27 Oct 2025cs.AIcited by 1

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Coordinating multiple autonomous agents in shared environments under decentralized conditions is a long-standing challenge in robotics and artificial intelligence. This work addresses the problem of decentralized goal assignment for multi-agent path planning, where agents independently generate ranked preferences over goals based on structured representations of the environment, including grid visualizations and scenario data. After this reasoning phase, agents exchange their goal rankings, and assignments are determined by a fixed, deterministic conflict-resolution rule (e.g., agent index ordering), without negotiation or iterative coordination. We systematically compare greedy heuristics, optimal assignment, and large language model (LLM)-based agents in fully observable grid-world settings. Our results show that LLM-based agents, when provided with well-designed prompts and relevant quantitative information, can achieve near-optimal makespans and consistently outperform traditional heuristics. These findings underscore the potential of language models for decentralized goal assignment in multi-agent path planning and highlight the importance of information structure in such systems.

Key facts. Cited by 1 paper 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 10 of its 15 references that are in the map. First posted on arXiv 27 Oct 2025.

Builds on

10 of 15 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Multi-Agent Coordination across Diverse Applications: A Survey20 Feb
  2. 2023LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models5 Oct

Other references · 8

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023Visual Instruction Tuningcited by 7,869
  3. 2023Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?cited by 106
  4. 2023Unleashing the potential of prompt engineering for large language modelscited by 72
  5. 2023Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4cited by 48
  6. 2023Prompt Engineering Through the Lens of Optimal Controlcited by 16
  7. 2022A Survey of Multi-Agent Human-Robot Interaction Systemscited by 10
  8. 2025Symmetric Policy Design for Multi-Agent Dispatch Coordination in Supply Chainscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Do LLMs Beat Nash? Testing Decentralized Coordination in Self-Play Multi-Agent Gamescited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning
  2. 2024TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models
  3. 2025Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams
  4. 2025Multi-Agent Path Finding via Offline RL and LLM Collaboration
  5. 2025Large Language Models for Planning: A Comprehensive and Systematic Survey
  6. 2025ELHPlan: Efficient Long-Horizon Task Planning for Multi-Agent Collaboration
  7. 2023Multi-Agent Consensus Seeking via Large Language Models
  8. 2025Data-Efficient Multi-Agent Spatial Planning with LLMs