Jun 2026· ACM SIGIR Forum· Vol 60, pp. 1 - 11· 0 citations· 40 references
Abstract
We present the insights gathered at the 1st Workshop on Conversational Search for Complex Information Needs (CoSCIN'26) held on 2 April 2026, in conjunction with the Forty-eighth European Conference on Information Retrieval (ECIR). The workshop brought together keynote presenters and industry representatives for lively discussions on recent developments in conversational search and its future directions, complemented by Q&A sessions for paper presentations. The rapid developments in conversational search enabled interesting discussions that went beyond generative dialogue and factoid question-answering tasks, towards addressing complex information needs and improving the user experience by providing personalized, adaptive answers to users based on style. Moreover, the work that was presented supported exploratory, multi-step information needs and addressed challenges such as longform answer generation, personalization, agent orchestration, and societal considerations, which remain largely open areas of research. The participants shared their views from diverse backgrounds in information retrieval, natural language processing, and human-computer interaction, exchanged results and prospective ideas around conversational AI, and discussed its impact on users and the need for adaptivity. Through these discussions, the workshop fostered collaboration and connected researchers specializing in diverse facets of the topic, ultimately advancing the field toward creating more user-centric, intentionally designed, responsible conversational systems. Date: 2 April 2026. Website: https://convsearch-complex-info-needs.github.io/.
This work extends the existing Exquisitor URF system with conversational search, and the user interface has been completely redesigned to prepare for future integration with conversational search and better support novice users.
O. Khan, Hongyi Zhu, Ujjwal Sharma et al.· 1 citation
Adaptive Personalised Reranking (APR) is proposed, a framework that routes queries based on intent that uses efficient similarity-based reranking for simple queries and dynamically generates tailored instructions to guide an instruction-following reranker for constraint-heavy contexts.
A large language model (LLM)-based three-stage framework that relies on an existing community question answering dataset is proposed, showing that the three-stage generation approach consistently outperforms the baseline particularly in recall, while also achieving competitive results across other metrics.
A central theme across the workshop was that proactivity should not be understood only as earlier action or improved prediction, but as a form of initiative that must be appropriately timed, transparent, contestable, and aligned with user goals.
Kirandeep Kaur, Vinayak Gupta, Tanya G. Roosta et al.· ACM SIGIR Forum· 0 citations
The results show that CoRG remains challenging for current agents, even the best agent reaches only 67.0% success rate, leaving one third of references unresolved, and position CoRG as a concrete benchmark for studying how agents search, inspect, and verify information in realistic multi-tool environments.
This paper presents the RecSys Challenge 2026 challenge task, dataset, evaluation protocol, and official results, and identifies limitations of the benchmark and evaluation protocol, including single-ground-truth relevance and teacher-forced evaluation of synthetic dialogues.
Seungheon Doh, Sergio Oramas, B. Sguerra et al.· Proceedings of the Workshop...· 0 citations
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