Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning
Overview
This forum entry covers the paper Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning by Fengran Mo, Yifan Gao, Sha Li, Hansi Zeng, Xin Liu, Zhaoxuan Tan, et al. (10 authors), posted on arXiv on 2026-01-19.
- Paper link: https://arxiv.org/abs/2601.13115
- Category: Conversational Search / Agentic Search
- Problem: In multi-turn dialogue, user intent evolves across interactions. Static "rewrite → retrieve → generate" pipelines optimize each step separately and miss mixed-initiative action optimization; prior deep search agents handle only single-turn scenarios.
- Approach: A conversational agent that interleaves searching and reasoning across turns, trained with reinforcement learning using rewards tailored to evolving user goals, enabling exploratory and adaptive retrieval behaviors.
- Results: Evaluated on four widely used conversational search benchmarks, outperforming several strong existing baselines (see the paper PDF for exact figures).
- A Survey of Conversational Search (ACM, Sep 2025)
- Engineering Conversational Search Systems (arXiv:2407.00997)
- CTR-Guided Generative Query Suggestion in Conversational Search (EMNLP)
- ChatRetriever: Adapting LLMs for Robust Conversational Search (arXiv:2404.13556)
- CoSearchAgent: A Lightweight Collaborative Search Agent (arXiv:2402.06360)
- ConvGQR: Generative Query Reformulation for Conversational Search (arXiv:2305.15645)
Abstract (from the paper)
> Large Language Models (LLMs) have become a popular interface for human-AI interaction, supporting 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. Existing studies usually follow static rewrite, retrieve, and generate pipelines, which optimize different procedures separately and overlook the mixed-initiative action optimization simultaneously. Although the recent developments in deep search agents demonstrate the effectiveness in jointly optimizing retrieval and generation via reasoning, these approaches focus on single-turn scenarios, which might lack the ability to handle multi-turn interactions. We introduce a conversational agent that interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through reinforcement learning (RL) training with tailored rewards towards evolving user goals. The experimental results across four widely used conversational benchmarks demonstrate the effectiveness of our methods by surpassing several existing strong baselines.
Key points
Positioning within the field
The paper sits at the intersection of agentic search and modern LLM-based retrieval systems. Classic search stacks treat recall, re-ranking, and generation as a fixed funnel; the LLM era adds new variables — reasoning budget and action space (whether to retrieve, how many times, which tools to call). Neural IR has evolved from BM25 to dense bi-encoders, cross-encoders, late interaction, generative retrieval, and now LLM agents, each balancing efficiency, effectiveness, and maintainability.
Insights for the Search / Rec / Personalization community:
1. Architecture: Cascade retrieval + re-rank + generation remains mainstream, but the agentic paradigm makes retrieval count and strategy themselves learnable. 2. Data: High-quality instruction data and session/click logs are critical; synthetic data must guard against knowledge leakage and distribution shift. 3. Evaluation: The gap between offline metrics and online satisfaction is widening; LLM-as-judge should be cross-validated with human assessment. 4. Productization: Latency, cost, explainability, and safety are hard production constraints — not just academic benchmark numbers.
Limitations and open problems
Potential limitations (typical for this line of work) include benchmark scale constrained by compute, mismatch between benchmarks and real user distributions, English-centric data limiting cross-lingual generalization, and safety risks of agents operating on the open web. Future directions include more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal/fairness constraints for recommendation.