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University of Science and Technology
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Agents can now escape the Self-Confirmation Trap, leading to more reliable experience learning and improved self-evolution.
Current LLM agents still struggle to infer and leverage user preferences from fragmented, real-world interactions, revealing a substantial gap between their capabilities and the demands of personalized decision-making.
LLM agents trained with simulated user and tool noise not only become more robust in messy real-world environments, but also surprisingly improve on clean, idealized benchmarks.
