Activation-space divergence detects and corrects compromised LLM agents in multi-agent systems without interaction graphs or synchronized rounds, outperforming graph baselines especially under async stealthy attacks.
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DMoA is a differentiable multi-agent framework for LLMs that uses recurrent context-aware routing and predictive entropy for test-time adaptation, claiming SOTA results on 9 benchmarks with efficiency and robustness.
StraTA improves LLM agent success rates to 93.1% on ALFWorld and 84.2% on WebShop by sampling a compact initial strategy and training it jointly with action execution via hierarchical GRPO-style rollouts.
citing papers explorer
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When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems
Activation-space divergence detects and corrects compromised LLM agents in multi-agent systems without interaction graphs or synchronized rounds, outperforming graph baselines especially under async stealthy attacks.
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Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models
DMoA is a differentiable multi-agent framework for LLMs that uses recurrent context-aware routing and predictive entropy for test-time adaptation, claiming SOTA results on 9 benchmarks with efficiency and robustness.
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StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction
StraTA improves LLM agent success rates to 93.1% on ALFWorld and 84.2% on WebShop by sampling a compact initial strategy and training it jointly with action execution via hierarchical GRPO-style rollouts.