Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning
This is an annotated entry from zhichai.net's curated list of conversational search literature, covering the paper *Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning* (arXiv:2601.13115), authored by Fengran Mo, Yifan Gao, Sha Li, Hansi Zeng, Xin Liu, Zhaoxuan Tan, et al. (10 authors total), published January 19, 2026.
Original Abstract
> 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
- Problem: In multi-turn dialogue, user intent evolves with context, requiring contextual interpretation, query reformulation, and dynamic retrieval-generation coordination. Prior pipelines (rewrite → retrieve → generate) optimize stages separately, and recent deep search agents handle only single-turn scenarios.
- Approach: A conversational agent that interleaves search and reasoning across turns, trained with reinforcement learning using rewards tailored to evolving user goals, enabling exploratory and adaptive behaviors.
- Results: Outperforms several strong baselines across four widely used conversational search benchmarks (specific numbers should be verified against the original PDF).
- A Survey of Conversational Search (Sep 2025, ACM)
- Engineering Conversational Search Systems (arXiv 2407.00997)
- CTR-Guided Generative Query Suggestion in Conversational Search (EMNLP)
- ChatRetriever: Adapting LLMs for Generalized and Robust Retrieval (arXiv 2404.13556)
- CoSearchAgent: A Lightweight Collaborative Search Agent (arXiv 2402.06360)
- ConvGQR: Generative Query Reformulation for Conversational Search (arXiv 2305.15645)
Method Overview
The entry characterizes the typical technical pipeline as: input/representation (dense or sparse encodings of queries, documents, user context), core modules (retriever, reranker, planner, memory, tool interfaces), learning strategies (supervised fine-tuning, contrastive learning, distillation, RL including process rewards, bootstrapped data synthesis), and inference strategies (single-turn retrieval, iterative retrieval, parallel sub-queries, early stopping and budget control).
Context within Agentic Search
The author positions the work at the intersection of agentic search and large-scale search/recommendation systems, reframing the classic search funnel (recall → ranking → generation) by adding two new variables: the reasoning budget and the action space (whether to retrieve, how many times, which tools to call). Evaluation is shifting from static nDCG toward process-oriented metrics such as task success rate, citation accuracy, and multi-hop reasoning chain completeness.
Engineering Checklist (from the entry)
| Check | Question | Suggestion | |-------|----------|------------| | Data | PII in training/index? Version management? | Partitioned indexes, anonymization, rollback-capable embedding versions | | Latency | p99 budget? Retrieval steps? | Cascades + early stopping, hot-query caching, async reranking | | Quality | Do offline gains translate to online satisfaction? | Interleaving experiments, human audits, citation verification | | Safety | Poisoning/bias from open retrieval? | Source whitelists, adversarial detection, output filtering | | Cost | Per-query token and GPU usage? | Small-model routing, distillation, hybrid sparse+dense |
Limitations and Open Questions
The entry notes possible limitations (compute-bounded experiments, benchmark-vs-real-user distribution mismatch, English-centric data, open-web safety risks for agents) and open directions: more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal/fairness constraints for recommendation.
Related Entries Cited
Reader Recommendations
1. Researchers: Reproduce core comparisons; check for statistical significance and compute cost reporting. 2. Engineers: Extract pluggable modules (encoder, reranker, planner) and estimate integration cost with existing stacks. 3. Product managers: Focus on user-perceivable benefits (latency, answer trustworthiness, multi-turn consistency) rather than offline nDCG alone.