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Multi-agent LLMs can now continually optimize their skills, leading to unprecedented adaptability and performance in complex tasks.
Context-Fractured Decomposition attacks expose a critical vulnerability in LLM agents, where harmless actions can lead to harmful outcomes through fragmented artifact interactions.
LLM agents can learn to cooperate far more efficiently by borrowing credit assignment techniques from classic multi-agent RL.
Forget hyperparameter tuning – autonomous research reveals that bug fixes and architectural tweaks unlock far greater gains in multimodal agent memory.
