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A shift from task-specific models to a perception-centered architecture could revolutionize how AI agents provide ongoing, adaptive support in complex environments.
Low-probability tokens disproportionately influence model updates, and a simple reweighting strategy can significantly enhance performance without sacrificing generalization.
LLMs struggle with personal information retrieval, with the best model only achieving 57.3% accuracy on a new benchmark designed to evaluate mobile assistant capabilities.