# CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic

arXiv: https://arxiv.org/abs/2410.14368 · HTML: https://mlsanity.com/paper/2410.14368/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023)
- [The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey](https://mlsanity.com/paper/2404.11584/) (2024)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [Simple random search provides a competitive approach to reinforcement learning](https://mlsanity.com/paper/1803.07055/) (2018)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [Open-TI: Open Traffic Intelligence with Augmented Language Model](https://mlsanity.com/paper/2401.00211/) (2023)
- [Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?](https://mlsanity.com/paper/2404.07569/) (2024)
- [Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning](https://mlsanity.com/paper/2308.14284/) (2023)
- [LibSignal: An Open Library for Traffic Signal Control](https://mlsanity.com/paper/2211.10649/) (2022)
- [Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic](https://mlsanity.com/paper/2202.00881/) (2022)
- [Reinforcement Learning Approaches for Traffic Signal Control under Missing Data](https://mlsanity.com/paper/2304.10722/) (2023)
- [String stability and a delay-based spacing policy for vehicle platoons subject to disturbances](https://mlsanity.com/paper/1702.01031/) (2017)
- [A Multi-Agent Rollout Approach for Highway Bottleneck Decongestion in Mixed Autonomy](https://mlsanity.com/paper/2405.03132/) (2024)

## Built on by (most cited first, 22 of 22)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap](https://mlsanity.com/paper/2503.21411/) (2025)
- [LangCoop: Collaborative Driving with Language](https://mlsanity.com/paper/2504.13406/) (2025)
- [CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2503.11739/) (2025)
- [CoLMDriver: LLM-based Negotiation Benefits Cooperative Autonomous Driving](https://mlsanity.com/paper/2503.08683/) (2025)
- [Generative AI in Transportation Planning: A Survey](https://mlsanity.com/paper/2503.07158/) (2025)
- [Generative AI for Autonomous Driving: Frontiers and Opportunities](https://mlsanity.com/paper/2505.08854/) (2025)
- [LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios](https://mlsanity.com/paper/2505.17209/) (2025)
- [CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning](https://mlsanity.com/paper/2505.18334/) (2025)
- [Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition](https://mlsanity.com/paper/2604.08708/) (2026)
- [Everyone Contributes! Incentivizing Strategic Cooperation in Multi-LLM Systems via Sequential Public Goods Games](https://mlsanity.com/paper/2508.02076/) (2025)
- [Conformal Feedback Alignment: Quantifying Answer-Level Reliability for Robust LLM Alignment](https://mlsanity.com/paper/2601.17329/) (2026)
- [LangMARL: Natural Language Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.00722/) (2026)
- [Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design](https://mlsanity.com/paper/2508.19611/) (2025)
- [HIPO: Instruction Hierarchy via Constrained Reinforcement Learning](https://mlsanity.com/paper/2603.16152/) (2026)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [SafeCoop: Unravelling Full Stack Safety in Agentic Collaborative Driving](https://mlsanity.com/paper/2510.18123/) (2025)
- [An LLM-Powered Cooperative Framework for Large-Scale Multi-Vehicle Navigation](https://mlsanity.com/paper/2510.07825/) (2025)
- [HiDrive: A Closed-Loop Benchmark for High-Level Autonomous Driving](https://mlsanity.com/paper/2605.09972/) (2026)
- [MASkills: Continual Skills Optimization for Multi-Agent LLM Systems](https://mlsanity.com/paper/2609.02094/) (2026)
- [Developing AI Agents with Simulated Data: Why, what, and how?](https://mlsanity.com/paper/2602.15816/) (2026)
- [Information-Directed Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2605.29405/) (2026)

## Abstract (arXiv)

The integration of autonomous vehicles into urban traffic has great potential to improve efficiency by reducing congestion and optimizing traffic flow systematically. In this paper, we introduce CoMAL (Collaborative Multi-Agent LLMs), a framework designed to address the mixed-autonomy traffic problem by collaboration among autonomous vehicles to optimize traffic flow. CoMAL is built upon large language models, operating in an interactive traffic simulation environment. It utilizes a Perception Module to observe surrounding agents and a Memory Module to store strategies for each agent. The overall workflow includes a Collaboration Module that encourages autonomous vehicles to discuss the effective strategy and allocate roles, a reasoning engine to determine optimal behaviors based on assigned roles, and an Execution Module that controls vehicle actions using a hybrid approach combining rule-based models. Experimental results demonstrate that CoMAL achieves superior performance on the Flow benchmark. Additionally, we evaluate the impact of different language models and compare our framework with reinforcement learning approaches. It highlights the strong cooperative capability of LLM agents and presents a promising solution to the mixed-autonomy traffic challenge. The code is available at https://github.com/Hyan-Yao/CoMAL.