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Reducing sycophancy in language models can inadvertently hinder their ability to rationally update, revealing a critical trade-off in model behavior.
Memory recovery in LLM agents is not just a byproduct of task success; it's a distinct capability that remains underexplored, with current models showing only moderate performance in reconstructing user states.
LLM agents can now autonomously generate complex skills with multi-file dependencies, rivaling human-authored skills, thanks to a co-evolutionary verification process that doesn't need ground truth labels.
