Scientists rely on figures to share their discoveries, but this makes the information contained in the figures difficult to parse, archive, and search. Multimodal neural-network models, designed to handle multiple types of data, promise to extract this information, but have not yet been widely tested and adopted by the astronomy community. In partnership with astroexplorer.org, we propose a novel dataset with over 100K figures and captions, and an associated task as a benchmark to evaluate a model’s multimodal capabilities. The task is to reconstruct the citation graph of astronomy papers solely from their figures and captions. AstroCLIMB was the shared task for WASP 2026. It attracted 15 participants on the open competition platform Kaggle, of which 10 submitted system papers. Results show that zero-shot methods and general purpose metrics underperform compared to finetuned methods, indicating that scientific figures are still hard to understand for currently available multimodal models. The dataset, benchmark task, and associated code are publicly available online.

To assess the scientific impact of observational facilities, Astronomers analyze publications which have made use of the data by the facility. This requires bibliographies annotating and linking data products with the literature, allowing bibliometrics to measure impact of the data. Creating bibliographies is laborious, involves specialists searching the literature for names, acronyms and identifiers, then determining how/if observations were used in those publications.

These bibliographies are useful beyond metrics: from the perspective of a research scientist, the data-literature connections provide a critical path to data discovery and access. Thus, by leveraging the efforts of librarians and archivists, we can use telescope bibliographies to support the scientific inquiry process. To this end, we introduce the TRACS: Telescope Reference and Astronomy Categorization dataset and Shared task, consisting of over 89K public English texts from space telescope bibliographies, categorized using a new, simple taxonomy based on discussion with established bibliographers.

We hope TRACS will be used to train modern Machine Learning and Artificial Intelligence models that in turn will help data curators build bibliographies. We evaluate how off-the-shelf Large Language Models perform on automatic bibliography curation as a baseline analysis. Results show the difficulty of the task and the need for custom tools.

The Workshop on Artificial Intelligence for Scientific Publications (WASP) brings together research on methods that read, organize, connect, and evaluate the scientific literature. Its fourth edition was held online as part of AACL-IJCNLP 2026 on November 10, 2026. It received 43 submissions and accepted 25: 15 main-track papers and 10 system papers for the AstroCLIMB shared task on linking astronomy figures and captions to the literature. This overview aims to give the community, including those who did not attend, an accessible account of what the workshop presented and found. For each main-track paper, it summarizes the approach, evaluation, main results, and the authors’ interpretation and next steps. We then draw out shared themes and future directions. Many contributions are deployed systems built with archives and curators. They combine language models with deterministic validation, keep source evidence attached to extracted values, and design evaluations around scientific use. Several papers find that lean, focused systems perform as well as more elaborate ones. The authors highlight directions such as automatic verification of grounding, shared gold standards, normalization of quantities and entities, figure understanding, and checking that citations are relevant and real.

We present our participation in the SOMD 2026 shared task on cross-document software mention coreference resolution, where our systems ranked second across all three subtasks. We compare two fine-tuning-free approaches: Fuzzy Matching (FM), a lexical string-similarity method, and Context Aware Representations (CAR), which combines mention-level and document-level embeddings. Both achieve competitive performance across all subtasks (CoNLL F1 of 0.94–0.96), with CAR consistently outperforming FM by 1 point on the official test set, consistent with the high surface regularity of software names, which reduces the need for complex semantic reasoning. A controlled noise-injection study reveals complementary failure modes: as boundary noise increases, CAR loses only 0.07 F1 points from clean to fully corrupted input, compared to 0.20 for FM, whereas under mention substitution, FM degrades more gracefully (0.52 vs. 0.63). Our inference-time analysis shows that FM scales superlinearly with corpus size, whereas CAR scales approximately linearly, making CAR the more efficient choice at large scale. These findings suggest that system selection should be informed by both the noise profile of the upstream mention detector and the scale of the target corpus. We release our code to support future work on this underexplored task.

Scientific multi-label text classification suffers from extreme class imbalance, where specialized terminology exhibits severe power-law distributions that challenge standard classification approaches. Existing scientific corpora lack comprehensive controlled vocabularies, focusing instead on broad categories and limiting systematic study of extreme imbalance. We introduce AstroConcepts, a corpus of English abstracts from 21,702 published astrophysics papers, labeled with 2,367 concepts from the Unified Astronomy Thesaurus. The corpus exhibits severe label imbalance, with 76 % of concepts having fewer than 50 training examples. By releasing this resource, we enable systematic study of extreme class imbalance in scientific domains and establish strong baselines across traditional, neural, and vocabulary-constrained LLM methods. Our evaluation reveals three key patterns that provide new insights into scientific text classification. First, vocabulary-constrained LLMs achieve competitive performance relative to domain-adapted models in astrophysics classification, suggesting a potential for parameter-efficient approaches. Second, domain adaptation yields relatively larger improvements for rare, specialized terminology, although absolute performance remains limited across all methods. Third, we propose frequency-stratified evaluation to reveal performance patterns that are hidden by aggregate scores, thereby making robustness assessment central to scientific multi-label evaluation. These results offer actionable insights for scientific NLP and establish benchmarks for research on extreme imbalance.

To evaluate the scientific influence of observational facilities, astronomers examine the body of publications that have utilized data from those facilities. This depends on curated bibliographies that annotate and connect data products to the corresponding literature, enabling bibliometric analyses to quantify data impact. Compiling such bibliographies is a demanding process that requires expert curators to scan the literature for relevant names, acronyms, and identifiers, and then to determine whether and how specific observations contributed to each publication. These bibliographies have value beyond impact assessment: for research scientists, explicit links between data and literature form an essential pathway for discovering and accessing data. Accordingly, by building on the work of librarians and archivists, telescope bibliographies can be repurposed to directly support scientific inquiry. In this context, we present the Telescope Reference and Astronomy Categorization Shared task (TRACS) and its accompanying dataset, which comprises more than 89,000 publicly available English-language texts drawn from space telescope bibliographies. These texts are labeled according to a new, compact taxonomy developed in consultation with experienced bibliographers.

The Workshop for Artificial Intelligence for Scientific Publications (WASP), formerly Workshop on Information Extraction from Scientific Publications (WIESP), started in 2022 to provide a platform for researchers to discuss research on information extraction, mining, generation, and knowledge discovery from scientific publications using Natural Language Processing and Machine Learning techniques. The third WASP workshop was held at the 14th International Joint Conference on Natural Language Processing and 4th Asia-Pacific Chapter of the Association for Computational Linguistics in Mumbai, India on December 23rd, 2025, as a hybrid event. The WASP workshop saw great interest, with 29 submissions, of which 16 were accepted. The program consisted of the contributed research talks, 2 keynote talks, a panel discussion, and one shared task, Telescope Reference and Astronomy Categorization Shared task (TRACS).

Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks. Inspired by this insight, we developed INDUS, a comprehensive suite of LLMs tailored for the closely-related domains of Earth science, biology, physics, heliophysics, planetary sciences and astrophysics, and trained using curated scientific corpora drawn from diverse data sources. The suite of models include: (1) an encoder model trained using domain-specific vocabulary and corpora to address NLP tasks, (2) a contrastive-learning based text embedding model trained using a diverse set of datasets to address information retrieval tasks and (3) smaller versions of these models created using knowledge distillation for applications which have latency or resource constraints. We also created three new scientific benchmark datasets, Climate-Change NER (entity-recognition), NASA-QA (extractive QA) and NASA-IR (IR) to accelerate research in these multi-disciplinary fields. We show that our models outperform both general-purpose (RoBERTa) and domain- specific (SciBERT) encoders on these new tasks as well as existing tasks in the domains of interest. Furthermore, we demonstrate the use of these models in two industrial settings- as a retrieval model for large-scale vector search applications and in automatic content tagging systems.