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About this paper
Yufei He, Ruoyu Li, Alex Chen, Yue Liu, Yulin Chen, Yuan Sui, Cheng Chen, Yi Zhu and 3 more
An Adaptive Reflective Interactive Agent (ARIA), developed by researchers at NUS and ByteDance, enables Large Language Model agents to continuously learn and adapt to evolving domain knowledge during operational deployment through intelligent human guidance. This framework demonstrated superior adaptability, accuracy, and efficiency in customer due diligence within TikTok Pay and on the CUAD dataset, achieving significant performance improvements over baselines.
Key insights
- Structured self-reflection allows LLM agents to intelligently identify their own knowledge gaps and proactively solicit targeted human guidance, maximizing the utility of expert feedback.
- A dynamically managed Knowledge Repository with explicit conflict detection, resolution, and temporal weighting is critical for maintaining an accurate and coherent knowledge base in evolving domains.
- Integrating real-time, human-in-the-loop feedback with test-time learning capabilities enables LLM agents to achieve superior performance and operational efficiency in high-stakes, knowledge-intensive environments.
Summary by alphaXiv.
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