#cooperative-agents News & Analysis

6 articles tagged with #cooperative-agents. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · Apr 77/10

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CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks

Researchers have developed CoopGuard, a new defense framework that uses cooperative AI agents to protect Large Language Models from sophisticated multi-round adversarial attacks. The system employs three specialized agents coordinated by a central system that maintains defense state across interactions, achieving a 78.9% reduction in attack success rates compared to existing defenses.

AINeutralarXiv – CS AI · Jun 46/10

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SMAC-Talk: A Natural Language Extension of the StarCraft Multi-Agent Challenge for Large Language Models

Researchers introduce SMAC-Talk, a benchmark environment that extends the StarCraft Multi-Agent Challenge to evaluate how large language models coordinate and communicate in cooperative multi-agent settings. The framework tests LLM agents under realistic constraints including partial observability, decentralized control, and adversarial deception, using Qwen models to examine how reasoning, memory, and scale impact agent coordination.

AINeutralarXiv – CS AI · Jun 26/10

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Coordination Graphs for Constrained Multi-Agent Reinforcement Learning

Researchers introduce CG-CMARL, a framework combining coordination graphs with Lagrangian duality to solve constrained multi-agent reinforcement learning problems. The approach decomposes complex joint action spaces into manageable pairwise regions, enabling scalability to larger agent teams while maintaining convergence guarantees and allowing dynamic Pareto front tracing without retraining.

AIBullisharXiv – CS AI · Jun 26/10

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MOC: Multi-Order Communication in LLM-based Multi-Agent Systems

Researchers propose Multi-Order Communication (MOC), a new framework for improving how large language model-based multi-agent systems exchange information. The scheme addresses limitations in current message-passing approaches by capturing multi-hop dependencies and consolidating messages efficiently, demonstrating consistent performance improvements across multiple datasets while reducing communication costs.

AINeutralarXiv – CS AI · Jun 26/10

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LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning

Researchers propose LMAC, an LLM-driven communication protocol for multi-agent reinforcement learning that enables agents to reconstruct shared state information more accurately and uniformly. The approach iteratively refines communication strategies using explicit state-awareness criteria, demonstrating substantial performance improvements over existing communication baselines across multiple MARL benchmarks.

AINeutralarXiv – CS AI · May 76/10

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Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage

Researchers propose CL-MARL, a curriculum learning framework for multi-agent reinforcement learning that dynamically adjusts task difficulty based on agent performance, addressing a fundamental limitation where fixed-difficulty training constrains policy generalization. The method achieves 40% win rate on complex cooperative tasks, outperforming existing baselines by significant margins.