Institutional delays trigger instability in multi-agent systems through delayed repression, with simulations identifying reactivity to lagged signals as the destabilizing factor rather than learning.
Multi-agent deep reinforcement learning: A survey.Artificial Intelligence Review, 55(2):895–943, 2022
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TRACER combines a controller-regret layer using regret matching for speak/skip decisions with a generation-credit layer using GSPO rewards to enable learned collaboration in multi-LLM reasoning.
MA-AC-MPC extends actor-critic MPC to multi-agent reinforcement learning and reports higher success rates than MLP baselines in pursuit-evasion simulation and hardware drone-rover landing.
The paper introduces a taxonomy of AI safety for LLMs organized into Trustworthy AI, Responsible AI, and Safe AI perspectives, accompanied by a review of state-of-the-art methods, challenges, and future directions.
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Delayed Repression and Emergent Instability in Adaptive Multi-Agent Systems
Institutional delays trigger instability in multi-agent systems through delayed repression, with simulations identifying reactivity to lagged signals as the destabilizing factor rather than learning.
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TRACER: Turn-level Regret Matching with Inner Reinforcement Credit for Cooperative Multi-LLM Reasoning
TRACER combines a controller-regret layer using regret matching for speak/skip decisions with a generation-credit layer using GSPO rewards to enable learned collaboration in multi-LLM reasoning.
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Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies
MA-AC-MPC extends actor-critic MPC to multi-agent reinforcement learning and reports higher success rates than MLP baselines in pursuit-evasion simulation and hardware drone-rover landing.