# CIR at iKAT SCAI 2026: Exploring Clarification Need Prediction in Agentic Conversational Search

arXiv: https://arxiv.org/abs/2607.19801 · HTML: https://mlsanity.com/paper/2607.19801/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 11 of 11)

- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [TREC CAsT 2019: The Conversational Assistance Track Overview](https://mlsanity.com/paper/2003.13624/) (2020)
- [Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions](https://mlsanity.com/paper/2109.05794/) (2021)
- [Conversational Information Seeking](https://mlsanity.com/paper/2201.08808/) (2022)
- [Analyzing and Characterizing User Intent in Information-seeking Conversations](https://mlsanity.com/paper/1804.08759/) (2018)
- [Zero-shot Clarifying Question Generation for Conversational Search](https://mlsanity.com/paper/2301.12660/) (2023)
- [Controlling the Risk of Conversational Search via Reinforcement Learning](https://mlsanity.com/paper/2101.06327/) (2021)
- [Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search](https://mlsanity.com/paper/2401.11463/) (2024)
- [Zero-Shot and Efficient Clarification Need Prediction in Conversational Search](https://mlsanity.com/paper/2503.00179/) (2025)
- [ChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering](https://mlsanity.com/paper/2510.13312/) (2025)
- [Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2601.13115/) (2026)

## Abstract (arXiv)

This paper presents the participation of the Cologne Information Retrieval group in the iKAT SCAI 2026 shared task. We use an agentic conversational search system, equipped with tools for query rewriting, retrieval and reranking, answer generation, and clarification need prediction and clarification question generation. We experiment with two different neural clarification need prediction models.