GCN Circulars distribute crucial information on astronomical observations of transient phenomena, but their flexible prose mixes observations, limits, metadata, contacts, and interpretation. We present an auditable pipeline that converts these reports into event-linked, structured records. Regular expressions provide domain hints; a large language model returns strict schema-constrained JSON; deterministic stages normalize values and emit review flags. Seven domain reviewers assessed 231 outputs from 210 distinct Circulars, fully confirming 223 reports and marking eight partly correct. They confirmed 25,827 of 25,880 definite field decisions (99.80%); errors concentrated in timing and facility attribution. Comparison with independent redshift datasets found semantic catalog disagreements but no values misread by our pipeline. Applied to the GCN Circular archive back to 2016, the pipeline has structured 26,811 reports into 68,393 observations, now served in real time by the Astro-COLIBRI platform. We release the extraction pipeline as an open-source package and the derived records as a continuously updated resource.

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.

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.

Interest in Astrophysical Natural Language Processing (NLP) has increased recently, fueled by the development of specialized language models for information extraction. However, the scarcity of annotated resources for this domain is still a significant challenge. Most existing corpora are limited to Named Entity Recognition (NER) tasks, leaving a gap in resource diversity. To address this gap and facilitate a broader spectrum of NLP research in astrophysics, we introduce astroECR, an extension of our previously built Time-Domain Astrophysics Corpus (TDAC). Our contributions involve expanding it to cover named entities, coreferences, annotations related to astrophysical relationships, and normalizing celestial object names. We showcase practical utility through baseline models for four NLP tasks and provide the research community access to our corpus, code, and models.

Le manque de ressources annotées constitue un défi majeur pour le traitement automatique de la langue en astrophysique. Afin de combler cette lacune, nous présentons astroECR, une extension du corpus TDAC (Time-Domain Astrophysics Corpus). Notre corpus, constitué de 300 rapports d’observation en anglais, étend le schéma d’annotation initial de TDAC en introduisant cinq classes d’entités nommées supplémentaires spécifiques à l’astrophysique. Nous avons enrichi les annotations en incluant les coréférences, les relations sémantiques entre les objets célestes et leurs propriétés physiques, ainsi qu’en normalisant les noms d’objets célestes via des bases de données astronomiques. L’utilité de notre corpus est démontrée en fournissant des scores de référence à travers quatre tâches~: la reconnaissance d’entités nommées, la résolution de coréférences, la détection de relations, et la normalisation des noms d’objets célestes. Nous mettons à disposition le corpus ainsi que son guide d’annotation, les codes sources, et les modèles associés.

Dans cet article nous étudions l’intérêt de l’augmentation de données pour le repérage d’entités nommées en domaine de spécialité : l’astrophysique. Pour cela, nous comparons trois méthodes d’augmentation en utilisant deux récents corpus annotés du domaine : DEAL et TDAC, tous deux en anglais. Nous avons générés les données artificielles en utilisant des méthodes à base de règles et à base de modèles de langue. Les données ont ensuite été ajoutées de manière itérative pour affiner un système de détection d’entités. Les résultats permettent de constater un effet de seuil : ajouter des données artificielles au-delà d’une certaine quantité ne présente plus d’intérêt et peut dégrader la F-mesure. Sur les deux corpus, le seuil varie selon la méthode employée, et en fonction du modèle de langue utilisé. Cette étude met également en évidence que l’augmentation de données est plus efficace sur de petits corpus, ce qui est cohérent avec d’autres études antérieures. En effet, nos expériences montrent qu’il est possible d’améliorer de 1 point la F-mesure sur le corpus DEAL, et jusqu’à 2 points sur le corpus TDAC.

Detecting Entities in the Astrophysics Literature (DEAL) is a proposed shared task in the scope of the first Workshop on Information Extraction from Scientific Publications (WIESP) at AACL-IJCNLP 2022. It aims to propose systems identifying astrophysical named entities. This article presents our system based on a majority voting strategy of an ensemble composed of multiple SciBERT models. The system we propose is ranked second and outperforms the baseline provided by the organisers by achieving an F1 score of 0.7993 and a Matthews Correlation Coefficient (MCC) score of 0.8978 in the testing phase.

The increased interest in time-domain astronomy over the last decades has resulted in a substantial increase in observation reports publication leading to a saturation of how astrophysicists read, analyze and classify information. Due to the short life span of the detected astronomical events, the information related to the characterization of new phenomena has to be communicated and analyzed very rapidly to allow other observatories to react and conduct their follow-up observations. This paper introduces TDAC: the first Corpus in Time-Domain Astrophysics, based on observation reports. We also present the NLP experiments we made for named entity recognition based on annotations we made and annotations from the WIESP NLP Challenge.