mlsanityData to 7 Oct 2026

Data-Efficient Multi-Agent Spatial Planning with LLMs

arXiv 2502.18822 ↗First posted 26 Feb 2025cs.AI cs.MAcited by 3

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

Abstract (arXiv)

In this project, our goal is to determine how to leverage the world-knowledge of pretrained large language models for efficient and robust learning in multiagent decision making. We examine this in a taxi routing and assignment problem where agents must decide how to best pick up passengers in order to minimize overall waiting time. While this problem is situated on a graphical road network, we show that with the proper prompting zero-shot performance is quite strong on this task. Furthermore, with limited fine-tuning along with the one-at-a-time rollout algorithm for look ahead, LLMs can out-compete existing approaches with 50 times fewer environmental interactions. We also explore the benefits of various linguistic prompting approaches and show that including certain easy-to-compute information in the prompt significantly improves performance. Finally, we highlight the LLM's built-in semantic understanding, showing its ability to adapt to environmental factors through simple prompts.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 32 of its 57 references that are in the map. First posted on arXiv 26 Feb 2025.

Builds on

32 of 57 references.

References · 32

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  6. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  7. 2023Let's Verify Step by Stepcited by 3,598
  8. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
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  10. 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
  11. 2024OpenVLA: An Open-Source Vision-Language-Action Modelcited by 2,777
  12. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
20 more
  1. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
  2. 2024Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spacescited by 580
  3. 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Searchcited by 338
  4. 20243D-VLA: A 3D Vision-Language-Action Generative World Modelcited by 320
  5. 2023On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspectivecited by 208
  6. 2023OpenAGI: When LLM Meets Domain Expertscited by 196
  7. 2023Foundation Models for Decision Making: Problems, Methods, and Opportunitiescited by 160
  8. 2024Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planningcited by 95
  9. 2023Compositional Foundation Models for Hierarchical Planningcited by 92
  10. 2024Interpretable Contrastive Monte Carlo Tree Search Reasoningcited by 62
  11. 2021Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learningcited by 51
  12. 2024Towards Self-Improvement of LLMs via MCTS: Leveraging Stepwise Knowledge with Curriculum Preference Learningcited by 27
  13. 2024Transcendence: Generative Models Can Outperform The Experts That Train Themcited by 18
  14. 2024Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating the Hallucination for Path Planningcited by 11
  15. 2024Language-Conditioned Offline RL for Multi-Robot Navigationcited by 9
  16. 2022Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demandcited by 6
  17. 2022Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansionscited by 4
  18. 2024Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Modelscited by 4
  19. 2022Autonomous Delivery of Multiple Packages Using Single Drone in Urban Airspacecited by 3
  20. 2023How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systemscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordinationcited by 3
  2. 2026MemoGuard: An Adaptive Runtime for Guarding Against Memory Traps in Communication-Limited Robot Navigationcited by 2
  3. 2026RideSkill: A Hierarchical Algorithm for Generalized Ride Sharing with LLM-Driven Automatic Evolutioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Large Language Models for Planning: A Comprehensive and Systematic Survey
  2. 2024Query-Efficient Planning with Language Models
  3. 2026Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning
  4. 2024Smart Language Agents in Real-World Planning
  5. 2024Understanding the planning of LLM agents: A survey
  6. 2025LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences
  7. 2023Improving Planning with Large Language Models: A Modular Agentic Architecture
  8. 2024LLaMAR: Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments