AI RESEARCH
Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning
arXiv CS.CL
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ArXi:2601.13115v2 Announce Type: replace Large Language Models (LLMs) have become a popular interface for human-AI interaction, ing information seeking and task assistance through natural, multi-turn dialogue. To respond to users within multi-turn dialogues, the context-dependent user intent evolves across interactions, requiring contextual interpretation, query reformulation, and dynamic coordination between retrieval and generation.