mlsanityData to 7 Oct 2026

A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models

arXiv 2503.08199 ↗First posted 11 Mar 2025cs.CV cs.AI cs.LGcited by 5

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

Traditional Reinforcement Learning (RL) suffers from replicating human-like behaviors, generalizing effectively in multi-agent scenarios, and overcoming inherent interpretability issues.These tasks are compounded when deep environment understanding, agent coordination and dynamic optimization are required. While Large Language Model (LLM) enhanced methods have shown promise in generalization and interoperability, they often neglect necessary multi-agent coordination. Therefore, we introduce the Cascading Cooperative Multi-agent (CCMA) framework, integrating RL for individual interactions, a fine-tuned LLM for regional cooperation, a reward function for global optimization, and the Retrieval-augmented Generation mechanism to dynamically optimize decision-making across complex driving scenarios. Our experiments demonstrate that the CCMA outperforms existing RL methods, demonstrating significant improvements in both micro and macro-level performance in complex driving environments.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 22 of its 40 references that are in the map. First posted on arXiv 11 Mar 2025.

Builds on

22 of 40 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024GAgent: An Adaptive Rigid-Soft Gripping Agent with Vision Language Models for Complex Lighting Environments16 Mar

Other references · 21

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  3. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  4. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  5. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 288
  6. 2023Reward Design with Language Modelscited by 257
  7. 2023DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Drivingcited by 233
  8. 2023Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Drivingcited by 231
  9. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 207
  10. 2023Grounding Large Language Models in Interactive Environments with Online Reinforcement Learningcited by 193
  11. 2023Drive Like a Human: Rethinking Autonomous Driving with Large Language Modelscited by 158
  12. 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
9 more
  1. 2023The Learnability of In-Context Learningcited by 126
  2. 2017Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximationcited by 119
  3. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 103
  4. 2023Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehiclescited by 72
  5. 2021Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Trafficcited by 38
  6. 2025Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversioncited by 28
  7. 2024The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Modelscited by 15
  8. 2023SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistencycited by 8
  9. 2023iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learningcited by 5

Built on by

Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Drivingcited by 18
  2. 2025MARS: Memory-Enhanced Agents with Reflective Self-improvementcited by 2
  3. 2025BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarioscited by 2
  4. 2025TDRI: Two-Phase Dialogue Refinement and Co-Adaptation for Interactive Image Generationcited by 1
  5. 2025OMR-Diffusion:Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Intent Understandingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Language-Driven Policy Distillation for Cooperative Driving in Multi-Agent Reinforcement Learning
  2. 2025MAESTRO: Multi-Agent Environment Shaping through Task and Reward Optimization
  3. 2026TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control
  4. 2024CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic
  5. 2024Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework
  6. 2023Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem
  7. 2024IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
  8. 2024KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models