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/.
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.
Interactive information access is increasingly moving beyond reactive query-response paradigms toward agentic systems that can personalize interaction, retain context, infer latent needs, recommend next steps, and initiate support. This shift creates new opportunities for adaptive and context-aware assistance, while also raising important questions about autonomy, privacy, trust, transparency, user welfare, and evaluation. The First Workshop on Human-Centered Proactive and Personalized Agents for Interactive Information Access provided an interdisciplinary forum for examining these questions across information retrieval, human-computer interaction, dialogue systems, AI ethics, cognitive science, learning technologies, and human-centered AI. Through invited talks, paper presentations, and open discussion, the workshop engaged with topics including calibrated initiative, knowledge-gap navigation, long-term memory, value-sensitive design, implicit personalization, AI-mediated care, proactive dialogue, and evaluation beyond task accuracy. 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. This report summarizes the workshop and synthesizes the research challenges it surfaced for designing proactive and personalized agents in interactive information access. Date: 26 March 2026. Website: https://proactive-chiir.github.io/index.html.
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.
The RecSys Challenge 2026 studies conversational music recommendation as a joint item recommendation and response generation problem: given a multi-turn dialogue, systems must retrieve relevant tracks from a large catalog and produce a grounded natural-language response. This paper presents the challenge task, dataset, evaluation protocol, and official results. Beyond the leaderboard, we analyze the 16 accepted systems through a common retrieve–rerank–generate framework and examine how recommendation performance varies across users, requests, and dialogue contexts. Strong systems commonly combine heterogeneous candidate sources and preserve source-specific evidence for learned reranking. Across the system papers and our organizer-side analysis, robust design also means 1) grounding cold-start retrieval in multi-turn conversation and item signals, 2) using intent detectors, and 3) modeling the full multi-turn context rather than the current query alone. We further identify limitations of the benchmark and evaluation protocol, including single-ground-truth relevance and teacher-forced evaluation of synthetic dialogues. Together, these findings provide practical guidance for future conversational recommender systems and shared evaluation efforts.
Seungheon Doh, Sergio Oramas, B. Sguerra et al.· Proceedings of the Workshop...· 0 citations
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