WSDM 2026: 19th ACM International Conference on Web Search and Data Mining Feb 22, 2026 - Feb 26, 2026 Boise, Idaho, USA
Call for WSDM Day Presentations Submission deadline: November 18, 2025 Notification: December 2, 2025 WSDM Day Date: February 26, 2026 All deadlines are 11:59 pm anywhere on Earth. -------------------------------------------------------- Artificial intelligence has become a critical enabler of scientific discovery, amplifying and accelerating research through massive literature analysis, hypotheses generation, design and control of experiments, collection and interpretation of large datasets, and scalable verification and validation. Autonomous science is an emerging field that uses artificial intelligence, robotics, and automated systems to conduct scientific experiments and discovery, enhancing human capabilities. This year, the WSDM Day will be held on February 26th, 2026, aiming to promote WSDM-aligned topics relevant to autonomous science and discuss the “State of Data Mining & Web Search in Scientific Discovery.” The organizing committee invites submissions for oral/poster presentation on topics aligned with those set forth in the call for papers of the main conference, focusing on issues relevant to autonomous science. We welcome all submissions related to, but not limited to, the following topics: * Agentic models for reasoning in science * Agentic AI and LLM-based systems are increasingly being used to support hypothesis generation and reasoning over complex models. Integrating web search and data mining allows these agents to continuously draw upon open scientific resources, digital libraries, and web knowledge graphs, thereby grounding hypotheses in the latest accessible evidence. Such integration accelerates workflows in fundamental sciences as well as user facility operations wherein researchers must make data-driven decisions regarding experimental and simulation design and control. By embedding reasoning into agents with web-mined knowledge, autonomous systems can help scientists frame and test ideas quickly and efficiently. Topics in this area include: ** LLMs and agentic models for hypothesis generation and knowledge extraction that leverage large-scale web-mined corpora and open-access repositories. ** Query formulation and expansion using scientific knowledge bases and web-scale search engines. ** Multi-agent systems for problem solving in scientific workflows enriched by open data and community-contributed resources. ** Foundation models for single and multiple scientific domains, trained and evaluated on mined web and literature data. ** Privacy, trust, and interpretability in autonomous reasoning systems that rely on heterogeneous, web-sourced knowledge. -------------------------------------------------------- * Embodied intelligence for autonomous experiments * Laboratories increasingly integrate robotics and AI for high-throughput experimentation and autonomous sample handling. Embodied intelligence can enable closed-loop operation of particle accelerator beamlines, synthesis robots, and advanced microscopes, reducing human intervention and increasing reproducibility. Web search and data mining provide complementary resources by enabling embodied systems to draw on prior experimental data, shared lab protocols, and open-access knowledge to guide adaptive control. There is also strong interest in developing digital twins of experimental systems that integrate web-accessible datasets to guide exploration of large parameter spaces. Topics in this area include: ** Robotics and mechatronics for operating experiments, informed by mined online design repositories and shared experimental logs. ** Closed-loop control in complex laboratory settings that leverages streaming data and searchable metadata collections. ** Benchmarking and validation through open science datasets, test collections, and evaluation methodologies made discoverable via data mining. ** Digital twins and online data streaming for linking simulation to experiment, enriched by knowledge integration from web and community datasets. -------------------------------------------------------- * Search and planning algorithms for scientific discovery * Search and planning algorithms play a central role in scientific discovery pipelines, from designing materials with desired properties to planning experiments. When integrated with web search and data mining, such algorithms can exploit open repositories of molecular structures, experimental data, or prior results to guide decision-making. In laboratories, adaptive planning algorithms can maximize information gain from limited resources, such as beam time and sample availability, while leveraging web-accessible experiment registries. This area also includes the operational task of optimal planning and scheduling of experiments at scientific facilities, aided by searchable databases of facility usage and constraints. Topics in this area include: ** Tree search, graph algorithms, and combinatorial methods that integrate mined data from scientific publications and web repositories. ** Filtering and re-ranking results based on relevance and criticality using web-scale knowledge graphs and citation networks. ** Reinforcement learning and adaptive experimental design augmented with external data mined from past studies. ** Planning and scheduling under uncertainty in constrained environments, supported by searchable facility schedules and open metadata. -------------------------------------------------------- * Data reduction at scale * Science experiments and simulations often generate petabytes of multi-modal data. Example data sources include particle collisions, climate simulations, or high-resolution microscopy images and spectra. In-situ and edge computing strategies must be used to perform reduction and compression at the source. Web search and data mining play an essential role in discovering, indexing, and linking these massive datasets with open-access resources, enabling cross-facility comparisons and meta-analysis. These capabilities are crucial for processing data from user facilities and making large-scale simulation models usable in practice. Topics in this area include: ** In-situ and edge computing for real-time reduction of experimental data, linked to searchable web repositories for downstream use. ** Streaming analytics for large-scale experimental datasets integrated with data mining for anomaly detection and trend discovery. ** Compression, summarization, and representation learning for scientific data, with searchable embeddings accessible via open web interfaces. -------------------------------------------------------- *** Submission Guidelines *** Submissions are invited in the form of 4-page papers (with an additional one page for references) or a 2-page abstract for poster and demonstration proposals. All submissions must adhere to the formatting requirements specified in the conference’s author guidelines, available at https://wsdm-conference.org/2026/index.php/call-for-short-papers/. Submissions are now open via EasyChair (https://easychair.org/conferences/?conf=wsdm2026, select WSDM Day Presentations Track). Submissions will be reviewed in a single-blind manner by at least two reviewers and must include all authors’ names and affiliations. Papers that fail to adhere to the submission guidelines or fall outside the scope of relevant topics will be rejected without review. The papers will go through a peer reviewed process, and the accepted papers would be presented as an oral or poster presentation during the WSDM Day. Accepted papers will be featured in the WSDM Day program and included in the conference proceedings with the ACM Digital Library, with a strict 2-page limit for all content. Dual-submission policy: We welcome ongoing and unpublished work. We also welcome papers that are under review at the time of submission. Presentation: The presentation format will include short oral presentations (e.g., talks ranging from 10 to 15 minutes) and poster session. At least one author of each accepted presentation must register in the conference and be able to present the work during WSDM Day 2026. We look forward to your submissions and participation in this exciting event! -------------------------------------------------------- *** Contact *** For questions or additional information, please contact the organizing committee at [email protected]: Organizing Committee: * Mahantesh Halappanavar (Pacific Northwest National Laboratory) * Natalie Isenberg (Pacific Northwest National Laboratory) * Nathan Urban (Brookhaven National Laboratory) _______________________________________________ Corpora mailing list -- [email protected] https://list.elra.info/mailman3/postorius/lists/corpora.list.elra.info/ To unsubscribe send an email to [email protected]
