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IntPro: A Proxy Agent for Context-Aware Intent Understanding via Retrieval-conditioned Inference
arXiv – CS AI|Guanming Liu, Meng Wu, Peng Zhang, Yu Zhang, Yubo Shu, Xianliang Huang, Kainan Tu, Ning Gu, Liuxin Zhang, Qianying Wang, Tun Lu|
🤖AI Summary
Researchers propose IntPro, a new AI proxy agent that improves intent understanding by learning from individual user patterns through retrieval-conditioned inference. The system uses historical intent data and specialized training methods to better interpret user intentions in context-aware scenarios.
Key Takeaways
- →IntPro addresses limitations in current LLM intent understanding by incorporating user history and contextual reasoning.
- →The system stores intent explanations in individual libraries for retrieval-based pattern matching.
- →Training involves supervised fine-tuning and multi-turn Group Relative Policy Optimization with tool-aware rewards.
- →Experiments across three scenarios show strong performance improvements in intent understanding capabilities.
- →The approach moves beyond static recognition to dynamic, personalized intent inference.
#large-language-models#intent-understanding#ai-agents#machine-learning#context-awareness#retrieval-systems#human-ai-collaboration
Read Original →via arXiv – CS AI
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