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UNC-Chapel Hill
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Forget hyperparameter tuning – autonomous research reveals that bug fixes and architectural tweaks unlock far greater gains in multimodal agent memory.
LLM agents can now learn on the fly and adapt to evolving user needs without disruptive downtime, thanks to a novel meta-learning framework that synthesizes new skills from failure trajectories and optimizes the base policy during inactive periods.
Even the best multimodal agents struggle with realistic visual scenarios, achieving only 27% accuracy on the new AgentVista benchmark that demands long-horizon tool use across web search, image search, and code.
