RF-Modulated Adaptive Communication Improves Multi-Agent Robotic Exploration

2026-02-12 · Lorin Achey, Breanne Crockett, Christoffer Heckman, Bradley Hayes arxiv

Reliable coordination and efficient communication are critical challenges for multi-agent robotic exploration of environments where communication is limited. This work introduces Adaptive-RF Transmission (ART), a novel communication-aware planning algorithm that dynamically modulates transmission location based on signal strength and data payload size, enabling heterogeneous robot teams to share information efficiently without unnecessary backtracking. We further explore an extension to this approach called ART-SST, which enforces signal strength thresholds for high-fidelity data delivery. Through over 480 simulations across three cave-inspired environments, ART consistently outperforms existing strategies, including full rendezvous and minimum-signal heuristic approaches, achieving up to a 58% reduction in distance traveled and up to 52% faster exploration times compared to baseline methods. These results demonstrate that adaptive, payload-aware communication significantly improves coverage efficiency and mission speed in complex, communication-constrained environments, offering a promising foundation for future planetary exploration and search-and-rescue missions.

📄 PDF Abstract BibTeX arXiv:2602.12074

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Demystifying Multi-Agent Debate: The Role of Confidence and Diversity

2026-01-09 · Xiaochen Zhu, Caiqi Zhang, Yizhou Chi, Tom Stafford 외 arxiv

Multi-agent debate (MAD) is widely used to improve large language model (LLM) performance through test-time scaling, yet recent work shows that vanilla MAD often underperforms simple majority vote despite higher computat…

Trust-Based Social Learning for Communication (TSLEC) Protocol Evolution in Multi-Agent Reinforcement Learning

2025-11-24 · Abraham Itzhak Weinberg arxiv

Emergent communication in multi-agent systems typically occurs through independent learning, resulting in slow convergence and potentially suboptimal protocols. We introduce TSLEC (Trust-Based Social Learning with Emerge…

Multi-agent Reinforcement Learning

AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning

2023-02-24 · Xuefeng Wang, Xinran Li, Jiawei Shao, Jun Zhang

Learning communication strategies in cooperative multi-agent reinforcement learning (MARL) has recently attracted intensive attention. Early studies typically assumed a fully-connected communication topology among agents…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)Vocal Bursts Valence Prediction

OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent LLM Collaboration

2025-09-05 · Jusheng Zhang, Yijia Fan, Kaitong Cai, Xiaofei Sun 외 arxiv

This paper introduces OSC (Orchestrating Cognitive Synergy), a knowledge-aware adaptive collaboration framework designed to enhance cognitive synergy in multi-agent systems with large language models. While prior work ha…

PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate

2026-05-26 · Yang Feng, Ziwei Xu, Xia Hu, Fengxiang He arxiv

Multi-agent debate improves the reliability of large language models (LLMs) through iterative peer critiques. However, fixed topologies often introduce persistent positional biases, amplify unreliable agents, and cause h…