CommCP: Efficient Multi-Agent Coordination via LLM-Based Communication with Conformal Prediction

• URL: http://arxiv.org/abs/2602.06038v1
• Date: Thu, 05 Feb 2026 18:59:45 GMT
• Title: CommCP: Efficient Multi-Agent Coordination via LLM-Based Communication with Conformal Prediction
• Authors: Xiaopan Zhang, Zejin Wang, Zhixu Li, Jianpeng Yao, Jiachen Li,
• Abstract summary: Embodied Question Answering (MM-EQA) is a novel extension of canonical Embodied Question Answering (EQA)<n>We propose CommCP, a novel decentralized communication framework designed for MM-EQA.<n>Our framework employs conformal prediction to calibrate the generated messages, thereby minimizing receiver distractions.
• Score: 23.27599901280979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To complete assignments provided by humans in natural language, robots must interpret commands, generate and answer relevant questions for scene understanding, and manipulate target objects. Real-world deployments often require multiple heterogeneous robots with different manipulation capabilities to handle different assignments cooperatively. Beyond the need for specialized manipulation skills, effective information gathering is important in completing these assignments. To address this component of the problem, we formalize the information-gathering process in a fully cooperative setting as an underexplored multi-agent multi-task Embodied Question Answering (MM-EQA) problem, which is a novel extension of canonical Embodied Question Answering (EQA), where effective communication is crucial for coordinating efforts without redundancy. To address this problem, we propose CommCP, a novel LLM-based decentralized communication framework designed for MM-EQA. Our framework employs conformal prediction to calibrate the generated messages, thereby minimizing receiver distractions and enhancing communication reliability. To evaluate our framework, we introduce an MM-EQA benchmark featuring diverse, photo-realistic household scenarios with embodied questions. Experimental results demonstrate that CommCP significantly enhances the task success rate and exploration efficiency over baselines. The experiment videos, code, and dataset are available on our project website: https://comm-cp.github.io.

Related papers

• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• Learning to Interact in World Latent for Team Coordination [53.51290193631586]
  This work presents a novel representation learning framework, interactive world latent (IWoL), to facilitate team coordination in multi-agent reinforcement learning (MARL)<n>Our key insight is to construct a learnable representation space that jointly captures inter-agent relations and task-specific world information by directly modeling communication protocols.<n>Our representation can be used not only as an implicit latent for each agent, but also as an explicit message for communication.
  arXiv  Detail & Related papers  (2025-09-29T22:13:39Z)
• Communicating Plans, Not Percepts: Scalable Multi-Agent Coordination with Embodied World Models [0.0]
  A central question in Multi-Agent Reinforcement Learning (MARL) is whether to engineer communication protocols or learn them end-to-end.<n>We propose and compare two communication strategies for a cooperative task-allocation problem.<n>Our experiments reveal that while emergent communication is viable in simple settings, the engineered, world model-based approach shows superior performance, sample efficiency, and scalability as complexity increases.
  arXiv  Detail & Related papers  (2025-08-04T21:29:07Z)
• AgentMaster: A Multi-Agent Conversational Framework Using A2A and MCP Protocols for Multimodal Information Retrieval and Analysis [0.0]
  We present a pilot study of AgentMaster, a novel modular multi-protocol MAS framework with self-implemented A2A and MCP.<n>The system supports natural language interaction without prior technical expertise and responds to multimodal queries for tasks including information retrieval, question answering, and image analysis.<n>Overall, our proposed framework contributes to the potential capabilities of domain-specific, cooperative, and scalable conversational AI powered by MAS.
  arXiv  Detail & Related papers  (2025-07-08T03:34:26Z)
• Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration [63.90193684394165]
  We introduce multi-agent cross-task experiential learning (MAEL), a novel framework that endows LLM-driven agents with explicit cross-task learning and experience accumulation.<n>During the experiential learning phase, we quantify the quality for each step in the task-solving workflow and store the resulting rewards.<n>During inference, agents retrieve high-reward, task-relevant experiences as few-shot examples to enhance the effectiveness of each reasoning step.
  arXiv  Detail & Related papers  (2025-05-29T07:24:37Z)
• MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents [59.825725526176655]
  Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents.<n>Existing benchmarks either focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition.<n>We introduce MultiAgentBench, a benchmark designed to evaluate LLM-based multi-agent systems across diverse, interactive scenarios.
  arXiv  Detail & Related papers  (2025-03-03T05:18:50Z)
• Communication Learning in Multi-Agent Systems from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We introduce a temporal gating mechanism for each agent, enabling dynamic decisions on whether to receive shared information at a given time.
  arXiv  Detail & Related papers  (2024-11-01T05:56:51Z)
• Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots [1.1049608786515839]
  We propose a Cooperative and Asynchronous Transformer-based Mission Planning (CATMiP) framework to coordinate distributed decision making among agents.<n>We evaluate CATMiP in a 2D grid-world simulation environment and compare its performance against planning-based exploration methods.
  arXiv  Detail & Related papers  (2024-10-08T21:14:09Z)
• CAMON: Cooperative Agents for Multi-Object Navigation with LLM-based Conversations [22.79813240034754]
  Large language models (LLMs) have exhibited remarkable comprehension and planning abilities. This paper proposes a framework for decentralized multi-agent navigation, leveraging LLM-enabled communication and collaboration.
  arXiv  Detail & Related papers  (2024-06-30T09:14:33Z)
• Generalising Multi-Agent Cooperation through Task-Agnostic Communication [7.380444448047908]
  Existing communication methods for multi-agent reinforcement learning (MARL) in cooperative multi-robot problems are almost exclusively task-specific, training new communication strategies for each unique task. We address this inefficiency by introducing a communication strategy applicable to any task within a given environment. Our objective is to learn a fixed-size latent Markov state from a variable number of agent observations. Our method enables seamless adaptation to novel tasks without fine-tuning the communication strategy, gracefully supports scaling to more agents than present during training, and detects out-of-distribution events in an environment.
  arXiv  Detail & Related papers  (2024-03-11T14:20:13Z)
• Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
  We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
  arXiv  Detail & Related papers  (2023-09-29T13:33:06Z)
• Who2com: Collaborative Perception via Learnable Handshake Communication [34.29310680302486]
  We propose the problem of collaborative perception, where robots can combine their local observations with those of neighboring agents in a learnable way to improve accuracy on a perception task. Inspired by networking communication protocols, we propose a multi-stage handshake communication mechanism where the neural network can learn to compress relevant information needed for each stage. We show that for the semantic segmentation task, our handshake communication method significantly improves accuracy by approximately 20% over decentralized baselines, and is comparable to centralized ones using a quarter of the bandwidth.
  arXiv  Detail & Related papers  (2020-03-21T04:16:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.