This meet-up will bring together members of the ACM SIGIR Information Interaction and Retrieval (CHIIR) community and the broader CHI audience to strengthen cross-community dialogue on interactive information retrieval (IIR), search interfaces, and human-centered evaluation methods. Through structured activities, networking opportunities, and collaborative brainstorming, the session aims to identify common challenges, inspire collaborations, and chart a forward-looking research agenda that connects CHI and CHIIR communities.

It is common knowledge on the web and in digital libraries that results with an image result in far more engagement than those without. In contrast to textual features, our understanding of the visual cues is fragmented at best, and visual information is typically ignored in search, recommendation, and engagement analysis. We extend the most used news recommendation dataset (MIND) with the lead image of the news items, annotate each image using News Value Theory factors, and experiment with different LLM modalities to detect these factors. Our research provides a practical way to use currently ignored visual features as additional handles for search and recommendation in mixed media collections.

The Third Search Futures Workshop [Azzopardi et al., 2026], in conjunction with the Forty-eight European Conference on Information Retrieval (ECIR) 2026, looked into the future of search to ask questions such as: • How can we navigate data privacy in large language model (LLM)-based information retrieval (IR)? • How can we implement agentic IR for proactive knowledge synthesis? • How do we ensure trustworthy information access beyond citations in the age of language models? • How does deep search transition from matching to reasoning? • What is meant by information semantics, knowledge representation, and natural language in a world of LLM-powered search? • What are serendipity engines, and how do they explore proactive web search via LLM agents, retrieval augmented generation (RAG), and simulated user feedback? The third edition of the workshop opened with ten lightning talks from a diverse group of speakers. Rather than traditional paper presentations, these short talks offered concise overviews of emerging ideas and critical insights, enabling a rapid exchange across various topics. The format was designed to spark discussion and expose participants to a broad spectrum of future-facing research directions in a compact timeframe. This report, co-authored by the workshop organizers, presenters, and participants, summarizes the talks and key discussions. Our aim is to share these insights with the broader IR community and help seed further dialogue around the themes raised. Date: 2 April 2026. Website: https://searchfutures.github.io/.

Over the last few years, the SimpleText Track has created an active community of NLP and IR researchers collaborating to improve access to scientific text. Its benchmarks on scientific passage retrieval, scientific terminology detection and explanation, and scientific text simplification have become standard references. Following a similar track design from 2021 to 2024, we introduced substantial changes to the track's structure and tasks in 2025. We plan to continue this successful setup in 2026, plus add a new (pilot) task on research area classification of scientific papers. Hence, the CLEF 2026 SimpleText track will contain the following 3 tasks. Task 1 (Text Simplification): simplify scientific text. Task 2 (Controlled Creativity): identify and avoid hallucination. Task 3 (Research Area Classification): classification of scientific articles by research area.

Building on the success and insights from previous years, the CLEF 2025 SimpleText Track continues to advance the mission of making scientific information more accessible to a broader audience. In 2025, we introduced a new biomedical corpus, based on aligned Cochrane abstracts and plain language summaries, for the main scientific text simplification task. In addition, we devote particular attention to remaining issues of current generative models, focusing on indentifying and classifying overgeneration and other information distortion in the predictions, as well as promoting grounded text generation approaches. This paper presents an overview of the CLEF 2025 SimpleText Track. Task 1 focuses on Text Simplification, aiming to simplify complex scientific texts. Task 2 addresses Controlled Creativity, emphasizing the detection, classification, and avoidance of hallucinations in generated content. Task 3 revisits selected tasks from SimpleText 2024 by popular demand. We discuss the data and benchmarks provided for these tasks, along with preliminary insights and anticipated challenges.

The JOKER Track has created an active community of researchers in NLP and IR working together on the non-literal use of language in text – which is still challenging for both AI models and humans, as it requires understanding implicit cultural references and double meanings. Its benchmarks on humorous text analysis, retrieval, and translation have become standard references. We made significant changes to the track’s setup and tasks in 2024 and 2025, and propose continuing these to complete the test collections. The CLEF 2026 JOKER track will contain the following four tasks: Task 1 (Humour-aware Information Retrieval): Retrieve short humorous texts for a query, Task 2 (Pun Translation): translate puns from English to French and Spanish, Task 3 (Onomastic Wordplay Translation): translate onomastic wordplay from English to French, and Task 4 (Humour Generation): Guided Creativity.

Over the last few years, the SimpleText Track has created an active community of NLP and IR researchers collaborating to improve access to scientific text. Its benchmarks on scientific passage retrieval, scientific terminology detection and explanation, and scientific text simplification have become standard references. Following a similar track design from 2021 to 2024, we introduced substantial changes to the track’s structure and tasks in 2025. We plan to continue this successful setup in 2026, plus add a new (pilot) task on research area classification of scientific papers. Hence, the CLEF 2026 SimpleText track will contain the following 3 tasks. Task 1 (Text Simplification): Simplify Scientific Text. Task 2 (Controlled Creativity): Identify and Avoid Hallucination. Task 3 (Research Area Classification): Classification of scientific articles by research area.

The JOKER Track has created an active community of researchers in NLP and IR working together on the non-literal use of language in text which is still challenging for both AI models and humans, as it requires understanding implicit cultural references and double meanings. Its benchmarks on humorous text analysis, retrieval, and translation have become standard references. We made significant changes to the track's setup and tasks in 2024 and 2025, and propose continuing these to complete the test collections. The CLEF 2026 JOKER track will contain the following four tasks: Task 1 (Humour-aware Information Retrieval): retrieve short humorous texts for a query, Task 2 (Pun Translation): translate puns from English to French and Spanish, Task 3 (Onomastic Wordplay Translation): translate onomastic wordplay from English to French, and Task 4 (Humour Generation): guided creativity.

This paper highlights the evolution and future directions of the SimpleText Track at CLEF, which, over the last few years, fostered an active NLP and IR research community focused on improving access to scientific text. Its benchmarks on scientific passage retrieval, scientific terminology detection and explanation, and scientific text simplification have become standard references. After using a similar setup of the track in 2021–2024, we propose substantial modifications to the track’s structure and tasks. The CLEF 2025 SimpleText track will contain the following three tasks. Task 1 on Text Simplification: Simplify Scientific Text. Task 2 on Controlled Creativity: Identify and Avoid Hallucination. Task 3 on SimpleText 2024 Revisited: Selected Tasks by Popular Request.