Papers

The release of ChatGPT in November 2022 introduced a writing tool of unprecedented fluency into the daily routines of researchers across the sciences. Prior work has measured what follows in journal abstracts and in peer reviews by documenting an upward shift in the frequency of words and short phrases that large language models (LLMs) tend to overproduce. However, a much less-examined question concerns whether the same shift extends to grant proposals, the documents through which researchers compete for public funding, and, in particular, to the subset of proposals that succeed. Such a distinction is important to note because funded grants represent decisions to allocate public research resources, and because the proposing population spans every scientific discipline in which a country supports research. This brief report draws on an analysis of 221,425 funded grant abstracts from three major science-funding agencies in two countries: the U.S. National Science Foundation (NSF), the U.S. National Institutes of Health (NIH), and UK Research and Innovation (UKRI). The window covers late 2017 through mid-2026, providing roughly 5 years of pre-ChatGPT baseline and 3.5 years of post-release observation in each corpus.

Philanthropic and nonprofit studies (PNPS) has grown rapidly as an interdisciplinary field, yet its intellectual structure and boundaries remain only partially visible. This study maps the field by combining journal- and keyword-based retrieval with citation network analysis, natural language processing, and large language model-assisted cluster labeling. Using 60,917 Web of Science articles, we identify major topical communities, examine their structural connections, and assess how well mainstream PNPS journals represent the broader landscape of related scholarship. The results show that PNPS is a loosely connected field dominated by three distinct "power centers": nonprofit organizations, social movements, and voluntary action. The analysis also reveals a broader disciplinary footprint than commonly recognized, extending beyond the social sciences and humanities into areas such as biomedicine and technology. These findings clarify the organization of PNPS and highlight opportunities for stronger integration across research domains.

Peer review plays a central role in scholarly publishing, yet verifying whether reviewer claims are supported by manuscript evidence remains a largely manual and time-consuming process. We present Peerify, a pipeline for manuscript-grounded verification of peer-review claims. Given a manuscript and a review comment, the Peerify pipeline decomposes reviews into atomic claims, retrieves relevant manuscript evidence, and determines whether each claim is supported by the paper. To support the development and evaluation of the pipeline, we construct a benchmark of 800 claims derived from authentic peer-review interactions collected from NeurIPS 2024 and ICLR 2024, including a 300-claim hand-labeled subset used to audit the automated supervision. We evaluate state-of-the-art language models and retrieval strategies within the Peerify pipeline, together with entailment baselines. Our results demonstrate the importance of retrieval-centered verification and claim decomposition, while highlighting the challenges posed by ambiguous and interpretive reviewer claims. Automated labels agree with human consensus on 90.3% of audited claims ($\kappa = 0.87$), while off-the-shelf entailment models stay below 0.24 macro-F1.

Scientific datasets are most compatible with AI workflows when they are described by rich, machine-readable metadata and provenance information, following the FAIR guiding principles. The FAIR Open-Science Extensible Data Exchange Network (FOXDEN) is a set of cyberinfrastructure building blocks developed at the Cornell High Energy Synchrotron Source (CHESS) for annotating raw, reduced, and analyzed datasets with both structured and unstructured metadata and provenance records. It also allows researchers to publish these records with Digital Object Identifiers (DOIs) to create AI-ready datasets. We describe FOXDEN's architecture and its deployment at CHESS and at the National High Magnetic Field Laboratory, where it serves as a foundation for future agentic scientific workflows.

While existing work on LLM authorship attribution (AA) has made progress, available benchmarks remain limited, often focusing on English, controlled settings, or relatively outdated models, with the few multilingual studies considering only relatively short texts. We introduce MultiGhostBench, a multilingual benchmark comprising 928 books generated by five recent LLMs across six languages and three scripts, with an average length of approximately 59K words per book. The benchmark supports evaluation under domain, author, and language shifts. Evaluation of representative AA methods shows that no single method consistently performs best across settings, and performance generally degrades under distribution shifts. Transformer-based detectors can retain generator-related information across languages, although transfer effectiveness varies by language pair, whereas statistical and fingerprint-based detectors are more language-dependent. We envision MultiGhostBench as a valuable resource for the development and evaluation of robust AA methods. The dataset and code can be found at https://github.com/GrecoMT/MultiGhostBench.

AI-research agents, or autoresearch systems, combine language models with tools, search, evaluation, and iterative modification of research artifacts. Their public software ecology is hard to compare because repositories, papers, benchmarks, libraries, and companion artifacts are often counted as one population. We connect two agent-drafted, human-adjudicated registries frozen on 10 June 2026: 139 canonical public repositories and 101 papers on AI-assisted research systems, each record carrying an evidence card anchored to its primary sources. Nine promoted design lineages contain 59 memberships among 52 repositories, seven of them assigned to two lineages. Against this evidence we examine six time-stamped candidate design regularities. A prospective audit of 25 newly ingested repositories observed zero of six specified trigger events, supplying bounded support for R1-R4; broader corpus evidence narrowed the original scope of R5, and a failed directional prediction leaves R6 exploratory. The paper-repository graph contains 212 curator-assigned relatedness edges: 64 of 101 papers carry at least one edge, the edges reach 23 of 139 repositories, and the six most-linked repositories carry 120 of the 212 edges (56.6%). A full-text check of seven papers shows the relation measures our curation, not the papers' citation behavior. An identity audit adjudicated against commit histories and public contributor records confirms at least 18 paper authors who also author a canonical registry repository (20 of 63 candidate author slots in 13 of 101 papers); a byline-matched tier reaches 58 identities but is reported separately and more weakly. The study contributes a source-grounded theory under prospective test, a curator-assigned map of paper-repository relatedness, and explicit limits on claims about public visibility and community structure.

Large language models (LLMs) are increasingly used for research quality evaluation, with prior work exploring their scoring accuracy and the plausibility of review rationales. However, less is known about whether external score guidance changes the evidence presented in the generated review as well as the final score. This study uses 98 Allied Health Professions research outputs submitted for internal REF-style assessment, with specialist human review reports and adjudicated 1-4 reference scores. No-guidance baseline reviews are compared with oracle-guided reviews, where the supplied score is set to the rounded human reference score; extracted evaluation points are used to compare human and LLM evidence use. Using this design, oracle guidance improves scoring accuracy, with score-following checks showing that models do not simply copy the supplied score. Corrected score mismatches are associated with changes in the generated review frame, showing that the score signal can steer review rationales. This effect is direction-dependent: LLM reviews cover human strength or upgrade points more reliably than human weakness or downgrade points, with the weakest alignment for expert downgrade evidence. The results show that score-guided review generation can be evaluated at the level of review evidence, as well as the final score.

Large language models (LLMs) are increasingly discussed as tools for peer review, but their value is often assessed through human-likeness, perceived usefulness, or textual overlap with reviewer comments. This study shifts attention from whether LLMs resemble human reviewers to what functions of scientific critique they perform. Using ICLR 2025 peer-review data, we compare human reviews with LLM reviews generated under baseline and expert prompts. We operationalize scientific critique through two review acts, weakness critique and scientific questioning, and annotate point-level review text using five theory-guided frameworks: Anderson's knowledge types, Toulmin's argumentation model, Graesser's question depth, SOLO cognitive complexity, and Hattie's feedback functions. The results reveal a differentiated critique profile. Human reviews placed greater emphasis on scientific framing and revision guidance, more often identifying higher-order weaknesses and asking questions oriented toward improvement. LLM reviews showed higher rates of explanatory depth, integrative reasoning, and explicit argument structuring. Expert prompting did not make LLM critique uniformly more human-like; it partially narrowed some gaps but mainly amplified LLM-specific tendencies toward integration and formal argumentation. These findings show that LLM-assisted peer review changes the functional composition of review text, making it important to distinguish LLM-amplified critique from areas requiring human prioritization and accountable judgement.

Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentioning). As large language models (LLMs) increasingly assist scientific writing, whether they reproduce citations with the same rhetorical intent as humans remains unclear. We introduce a masked-citation task to compare human and LLM-generated citation behavior. For each citation context, an LLM generates a replacement citation sentence, producing a counterfactual corpus directly comparable to human citation. We analyze what, whom, and how models cite, using an LLM-as-a-judge to classify citation intent and a 20-million-edge coauthorship network to measure social distance between cited authors. Across six popular LLMs and 1,746 top NLP conference papers (63k+ contexts, 132k+ citations), three patterns emerge: (1) Compared with human citation, LLMs cite significantly less critically; (2) LLMs over-cite popular and older papers, a tendency amplified for contrasting citations where human writing more often draws on recent, niche work; (3) Whereas humans often cite within their close social network, especially for supporting citations, LLMs tend to draw on more socially distant authors. Together, these differences are double-edged: LLM citation reaches beyond a scholar's close collaborators while being less critical and amplifying visibility bias, reshaping the rhetoric and reach of scientific citation.

We present the zbMATH Open Knowledge Graph, a large-scale RDF knowledge graph (KG) covering more than 250 years of mathematical scholarship. Unlike existing scholarly knowledge graphs that primarily capture bibliographic metadata and citation structures, the zbMATH Open KG integrates expert-curated semantic content, including reviews, keywords, subject classifications, software references, and disambiguated authorship. This combination of domain-specific representation of mathematical knowledge and extensive temporal coverage supports analyses that require fine-grained exploration of mathematical concepts, research fields, and scholarly relationships over time. The resulting graph comprises 34 million entities and 168 million RDF triples represented using established Semantic Web vocabularies, supporting interoperability and FAIR data principles. We further demonstrate its capabilities through query-driven historically grounded scholarly exploration use cases, illustrating how the knowledge graph can surface relationships and patterns that may be difficult to identify from bibliographic and citation information alone. The zbMATH Open KG provides an open semantic infrastructure for studying the development of mathematical knowledge and tracing scholarly connections across centuries of scholarship.

Research data infrastructure measures metadata completeness, the share of fields populated. This is the wrong quantity: a populated field is not a joinable field. We measure comparability instead: whether two records can be used together, and for what. Working from 180,877 records across eight public sources, we adapt measurement invariance from psychometrics into a four-rung ladder, each rung established by its own test and licensing one specific comparison. Over all 167,331 pairs of gas chromatography analyses in a public metabolomics repository, 100% reach the bottom rung, 58.85% the second, 0.072% the third, and exactly two pairs reach the rung that licenses comparing values directly, an upper bound under a generous test. The deficit is recoverable: the missing values survive in prose. We formalise crosswalking as five operations ordered by reliability, each with a characteristic failure mode. The temperature programme that determines retention is structured in none of a second repository's 355 gas chromatography studies yet written in prose in 78.6%; we recover 206 ordered programmes comprising 529 steps, audited over three rounds. The perceptual record fails differently: its datasets do not share a language. Among eight that declare they measure odour character in humans, 42.9% of pairs share no descriptor, and two studies rating the same molecules with the same word agree at mean correlation 0.31. Joining volatility measurement to human percept across the entire public record yields 132 molecules. Across seismology, meteorology, metrology and chemistry, a field is populated when the primary consumer cannot complete the primary task without it: the same optional, unvalidated field sits at 100% in one discipline and near zero in another. Resources should publish a conformance ledger, reporting what is missing as a tracked quantity; we release one, with 48,356 triples.

Journals are beginning to consider language models for manuscript handling, but submitted manuscripts are unpublished, and where policy forbids sending them to an external service the model must run on hardware the journal controls. The capability of locally hosted models on editorial work has not been measured. Here we constructed a benchmark of eight editorial tasks from a journal's Instructions for Authors, from manuscripts carrying defects we seeded and verified independently, and from published reviews of a preprint, and evaluated twenty open-weight models spanning a twenty-five-fold range of weight size on a compact desktop workstation of the kind a laboratory or small editorial office can adopt. The strongest model detected 36 of 40 seeded guideline violations and occupied 81 GB; a 17 GB model detected 33. Across the best configurations tested, we observed no consistent monotonic association between weight size and score: rank correlations were negligible on every task (Spearman |rho| <= 0.19), and within one model family the larger member scored below its smaller sibling. A deterministic checker of regular expressions and arithmetic, using no model, detected 31 of the same violations in a fraction of a second, and the union of its detections with those of the strongest model covered all 40. On the single peer-review case, the best model recovered 6 of 12 points from three published reviews. Prompt structure substantially altered scores within individual models. This level of performance is therefore within reach of a workstation of this class, once the deterministic checks are written.

Large Language Models (LLMs) can estimate the quality of published journal articles, potentially supporting human assessment when evaluations are needed. Whilst there are reasons to believe that LLMs may have biases in this role, there is no statistically strong evidence yet. The current article addresses this gap with an exploration of the types of articles that attract high or low LLM scores in 73,489 articles from 15 health and life sciences journals. Based on comparing the words in the titles and abstracts of higher and lower scoring articles for two LLMs in various ways, the results suggest that topics favoured by GPT-OSS-120B include viruses, genes and cells and its disfavoured topics include surveys, patients and students. It is not clear whether these patterns reflect underlying quality differences or AI biases, however. The same method found systematic differences between the topics favoured by GPT-OSS-120B and Gemma 3 27B, such as Gemma 3 27B giving relatively higher scores for machine learning research, proving that at least one of the two LLMs has AI bias. Finally, comparing the scores for full-text articles compared to scores for titles and abstracts also finds differences for both LLMs, showing that they both can exhibit AI bias for at least one of these two input types, and probably both. Overall, the results show that it is important to consider LLM biases when deciding whether to use them for research evaluation tasks.

When questionable research has entered the scientific record, it stands the same chance as other literature of shaping national and international policy, clinical guidance, and research and development activities. This study uses a known authorship-for-sale network identified in August 2022 to examine whether questionable research influences policy, patents, and clinical guidelines, and whether its authors continue to secure funding and publish after exposure. Across nearly 2,000 publications in our dataset, 57 were cited in policy documents, 12 in clinical guidelines, and 480 in patents. Nearly a quarter of these publications are linked to one or more grants: funding that could otherwise have supported rigorous, ethical science. Among the 278 authors we traced, publishing and funding continued well after the PNN's public exposure in 2022: over 90% continued publishing, and 23% were linked to a grant. As our findings show, the participants of paper mills and authorship-for-sale networks can still inform policy and clinical practice and support their authors' career progression even after the questionable practices behind them are exposed. We present a case for funders to consider authorship and network structure as part of a holistic due diligence process, alongside metrics such as citation counts and attention data.

For provenance research and art market studies, auction catalogs are an essential resource to trace specific objects over time and space. While historical auction catalogs follow established domain conventions, their internal formatting remains highly variable, and their large-scale analysis is currently restricted by the lack of machine-readable representations of the auction lots. We propose a pipeline to automatically extract structured lot-level metadata from German Sales, a large database of historical auction and sales catalogs from the 19th and 20th centuries. Using a manually annotated test set of representative catalog pages, we evaluate Vision-Language Models (VLMs) under varying prompt strategies and constrained decoding frameworks. To reflect the practical constraints faced by cultural heritage institutions, including budget, compute resources, and data privacy requirements, we benchmark the methods across different deployment modes ranging from commercial providers to locally hosted, quantized models. We find that commercial endpoints establish the performance ceiling, while institutional gateways offer a viable, privacy-preserving alternative. Local deployments remain feasible, but strictly require enforcing the output structure during generation to guarantee a valid JSON format. While varying degrees of human-in-the-loop correction are still necessary, this work demonstrates that a VLM-based pipeline can successfully unlock historical auction catalogs for large-scale automated analysis.

For centuries, the scientific community has predominantly relied on visual tools to communicate empirical results and complex datasets. While visual representations dominate bibliometric analyses, the human auditory system possesses sensitive capacities for processing complex temporal information, distinguishing intricate patterns, and tracking parallel data streams. Data sonification translates data relations into acoustic signals, offering an alternative method for data exploration, pattern recognition, and scientific communication. This paper applies this concept to the field of bibliometrics through metrics sonification-the auditory translation of bibliometric information-and introduces SoniMet (Sonifying Metrics), a web-based tool designed to visualize and sonify the publication and citation data of individual scientists (see https://sonimet.kennebec.co.uk). SoniMet connects to the OpenAlex database to retrieve bibliometric records and displays them on an interactive chronological timeline. The tool employs direct parameter mapping to translate citation impact indicators into non-speech sound: field-weighted citation impact and citation counts determine the pitch and volume of a synthesized note and are mapped to the acoustic echo strength. Although SoniMet expands the methodological toolkit for research evaluation, current limitations include its restriction to individual scholar profiles and the challenge of integrating transient audio files into traditional, text-based scientific publishing workflows. Future empirical user studies are necessary to systematically evaluate the analytical utility and cognitive benefits of metrics sonification compared to established visual methods.

Sustained scientific work requires a knowledge substrate that carries interpretation across tasks and preserves paths to source evidence. We call this process scientific knowledge compilation and implement it in ASKS, the Agent-Driven Scientific Knowledge System. For each source, an LLM produces a readable Wiki view and machine-facing semantics. Deterministic checks convert the latter into a document-local GraphDelta, and embedding geometry together with explicit graph rules integrates the proposed changes into persistent state. Each ingest is an inspectable state transition over accumulated knowledge, with compiled Wiki and graph views linked to the preserved source record. We examine this process by chronologically compiling 56 published papers from one research program. Branch survival, cross-paper support, lineage, coverage, and churn yield a source-traceable author research portrait centered on tensor-network methods, with branches into quantum many-body research, tensor-network machine learning, and quantum-AI-oriented directions. In this run, higher-level Hub organization remains stable and low-churn. Canonical-node growth is predominantly additive. Graph-level measurements and navigation paths retain links to the source records from which they were compiled.

Qualitative research is widely used in the human and social sciences, characterized by a deep understanding of phenomena through the interpretation of meanings and contexts. Among qualitative data analysis methods, content analysis stands out as a consolidated technique, which allows for the systematic description and interpretation of textual contents. However, as data volume increases, the time required for organization, reading, and categorization becomes a significant challenge, potentially delaying research development. Therefore, this work aimed to develop a web application to support content analysis, based on Bardin's methodology, targeted at academic researchers. The methodology adopted a mixed approach, combining bibliographic research on content analysis with semi-structured interviews with four experienced researchers, aiming to identify real needs and requirements. Based on these inputs, the Cortex software was developed to assist the researcher in the pre-analysis and material exploration stages, generating suggestions for indices, indicators, and categories with full traceability to original documents. The results demonstrate that Cortex is capable of guiding the researcher through the entire methodological workflow, from corpus configuration to results exportation, acting as a methodological collaborator without replacing the researcher's interpretative autonomy.

Most scientific discoveries justify their significance by filling holes between existing ideas and arguments. Representing knowledge as high-dimensional concept embeddings, we use persistent homology to detect scientific holes of increasing order, from lower-order gaps between disconnected ideas to higher-order cavities within unevenly connected ones; then we identify the research that fills them. Researchers who close holes draw outsized collective attention, staging novelty that the citing literature rewards, most strongly in discovery-oriented fields and least in design-oriented fields. Closing a hole is followed by persistent work on the same combinations of concepts at many times the rate of comparable holes left open. Yet as knowledge grows, higher-order holes explode while the fraction of research that fills them collapses, leaving most higher-order combinations unexplored. These results call for a richer science of holes and mark a frontier where contemporary AI might help fill the gaps human science opens.

Open Large Language Model (LLM) research increasingly produces compact and derived artifacts, such as adapters, quantized checkpoints, merged models, and distilled variants, that are distributed across papers, model hubs, model cards, code repositories, and release statements. Although these artifacts are publicly visible, digital libraries often lack sufficient evidence to identify, preserve, and cite them as coherent scholarly objects. We introduce a framework that conceptualizes curatability as a record-level property of distributed scholarly records and operationalizes it through four evidence dimensions: artifact identity, scholarly linkage, upstream evidence, and release assets. Guided by this framework, we conduct the first collection-scale characterization of open LLM curatability using a May 2026 snapshot of 191,375 public Hugging Face repositories and a core corpus of 2,214 scholarly papers. Our results reveal a pronounced visibility-to-curatability funnel. While 90.7% of paper records contain at least one useful curation signal, only 18.1% combine usable upstream evidence with concrete release evidence, and only 6.1% provide sufficiently coordinated evidence to support high-curatability records. Based on these findings, we derive a minimal seven-field curatable record and complementary responsibilities for model hubs, scholarly indexes, and digital libraries, providing practical guidance for improving the preservation and bibliographic control of open LLM artifacts.

China has become the world's largest producer of scientific publications, yet concerns persist that this growth is inflated by excessive domestic citation practices. In this study, we analyze a citation network of over 45 million publications from Web of Science (1980-2025) to investigate China's home citation bias and research impact. Using a network reshuffling null model to control for the structural effect of publication volume, we find that China's home citation bias is less pronounced than commonly assumed and has been steadily declining over the past two decades. Chinese researchers do not exhibit a significantly stronger home citation preference than other major countries, indicating increasing internationalization rather than insularity. Furthermore, using the persistent disruption framework, we show that Chinese papers are converging toward American papers in their capacity to produce paradigm-shifting work. These findings challenge prevailing narratives about Chinese scientific home bias and suggest that China's advances in research impact.

The European Health Data Space requires member states to publish machine readable descriptions of the health datasets available for secondary use, and the European Commission publishes HealthDCAT-AP as the metadata profile those descriptions are meant to satisfy. The profile has been designed and validated against curated examples, never against the catalogues already live. We report that measurement for the Nordic region. On 25 August 2026 the 11 Nordic national catalogues harvested by the European data portal held 2,811 dataset descriptions carrying the EU health theme, and none satisfies all eight properties HealthDCAT-AP Release 7 makes mandatory on a dataset. Three of the eight are present on exactly zero records across five countries. Set beside the portal's own quality assessment, which validates DCAT-AP and never mentions the health profile, this is not a health extension skipped on top of sound generic practice: no Nordic catalogue reaches the assessment's top rating band, 5 of the 11 are reported at zero per cent DCAT-AP compliant, and the properties surviving in both layers are the ones a human types into a form, not the ones needing a value bound to a controlled vocabulary. Two further results follow. Finland contributes 2,259 descriptions to the European portal of which 1,146 carry a theme, and not one uses the EU theme authority vocabulary, so a European health filter returns no Finnish dataset at all. Separately, the authority namespace answers HTTP 200 with a well formed empty document for terms it never defined, letting 1,238 datasets across the wider portal carry theme IRIs that resolve to nothing while passing any status code check. We publish the vocabulary, the shapes and the harvesters, record every verdict as a dated observation rather than a property of the dataset, and report the five errors this discipline caught before publication.

Identifying white space — the unexplored but potentially valuable regions of a patent landscape — is essential for strategic R&D planning, yet existing methods rely on manual patent mapping or apply single-view clustering without quantitative gap detection. We propose BLANC (Blank Landscape Analysis through NPMI Conditioning), a three-phase pipeline combining (1) multi-view neural topic modeling along three semantic dimensions (application/use, novelty, inventive step); (2) Normalized Pointwise Mutual Information (NPMI) to quantify cross-dimensional cluster association; and (3) conditional detection that flags combinations whose NPMI drops when the corpus is filtered by a user-specified keyword. The drop is captured by a new metric, $\Delta$NPMI, which identifies combinations "established globally, unexplored locally." Because white space has no ground truth, we evaluate BLANC on two public USPTO corpora — machine learning/AI (5,417 patents, CPC G06N) and glass compositions (1,982 patents, CPC C03C) — by artificially depleting known technology combinations and testing recovery. When three-quarters of a target pair's documents are removed, BLANC recovers 34.1% (ML/AI) and 27.3% (glass) of the depleted combinations, whereas size-matched removals not aimed at them (random documents, or those of a different established combination) essentially never do: the target is never recovered in 191 decoy trials. Collapsing the three semantic views into one recovers nothing, while prior co-occurrence measures also flag the target under random removal, offering no specificity. In a proprietary case (302 float glass / glass-ceramics patents), the keyword "fluorine" reveals a fluorine surface treatment $\times$ warpage suppression candidate ($\Delta$NPMI up to 0.48) that experts had independently identified.

Evaluating the impact of scientific research beyond academia — on policy, health, the economy, and cultural life — has become a cornerstone of science policy and research-funding allocation worldwide. Yet which researchers produce the research underpinning this impact, and how this production is shaped by gender, remains poorly understood. We combine structured and unstructured records from the United Kingdom's latest Research Excellence Framework, the largest national research assessment currently in operation, with large-scale bibliometric data to quantify gender differences among the researchers underpinning documented impact. Women account for 38.16% of these contributors: underrepresented overall, but with a consistently higher share than in research-output authorships (33.63%), both overall and across all four REF panels. Impact production is also strongly gendered across domains: women are better represented in case studies concerning education, health, cultural, and civil-society impact, whereas those concerning commercialisation pathways such as patenting and manufacturing remain dominated by men. These findings reveal critical inequalities across the pathways that connect research to impact beyond academia, offering crucial evidence for policymakers and academic institutions aiming to build more equitable and representative systems for evaluating scientific contributions.

Large language models increasingly mediate access to information, and a growing body of work asks whether they cite the sources behind their outputs. That work treats citation as a verification device and applies it to textual documents. Scholarly citation serves two further functions, credit and provenance, and it applies to data as much as to text. This paper argues that data citation for large language models is an open challenge, distinct from document-level citation grounding and harder to solve. We ask how such models should cite data so that outputs stay verifiable, provenance stays traceable, and credit reaches data creators and curators. We set out three research directions. Training data attribution has to turn influence estimates into references for corpora absorbed into model parameters. Data citation at inference time has to identify datasets, subsets, and query results at the right granularity and fixity. Citing knowledge graph facts has to define what a reference to a single triple denotes and how credit propagates along provenance. Progress on all three depends on joint work across the database, information retrieval, knowledge representation, and artificial intelligence communities.

Purpose: The digitization of historical documents presents fundamental challenges for modern information retrieval and Artificial Intelligence (AI) systems. Optical character recognition (OCR) errors in source corpora propagate through retrieval-augmented generation (RAG) pipelines, compromising the factual accuracy of generated outputs. Methods: This study presents a systematic evaluation of PDF-to-text extraction pipelines applied to historiographical secondary sources on the Visigothic period. We assess thirteen distinct approaches spanning three methodological families: direct extraction, Large Language Model (LLM) post-correction, and chunk-and-extract. Documents are stratified into five categories based on production method and visual complexity. Performance is measured using character error rate (CER) and word error rate (WER) against manually corrected ground truth. Results: Results demonstrate that direct extraction with Marker achieves superior performance (98.70% CER accuracy; 97.71% WER accuracy overall), while conventional OCR pipelines exhibit substantial degradation on scanned documents and complex layouts. Embedded-text extraction performs well on digital PDFs but fails on scanned documents. LLM post-correction does not provide systematic improvements and frequently degrades accurate extractions. Conclusion: End-to-end document parsing is the most reliable approach for heterogeneous historical collections. Document characteristics such as scan quality, layout complexity, and the presence of embedded text layers have a significant impact on extraction accuracy. LLM-based post-correction should not be assumed beneficial by default and requires validation before large-scale application.

Recent advances in artificial intelligence (AI) have sparked growing interest in its use for mathematical research. While some view this as a major opportunity for discovery, others have raised concerns about its impact on traditional research practices. Despite extensive debate, empirical evidence on how AI is actually being used in mathematics remains limited. To address this gap, we collected all 32,944 arXiv submissions posted between March 1 and August 20, 2026, whose primary or secondary categories included Mathematics. We identified 3,575 submissions that explicitly disclosed author use of AI, of which 1,712 involved at least one substantive mathematical contribution. Our analysis reveals several broad patterns. First, disclosed AI use increased sharply over the study period, with substantive use growing from 1.39% of Mathematics submissions in March to 14.09% through August 20. Second, substantive AI use is highly uneven across fields: Combinatorics has the largest number of such papers, while Metric Geometry has the highest substantive-use rate. Third, substantive AI use is geographically concentrated: under weighted author counts, the United States and China together account for about two-thirds of the recognized country weight. Fourth, AI is already being applied to open research problems: among 717 named open-problem records associated with substantive use, 71% are labeled as fully resolved based on the authors' descriptions, with proofs of the conjectured statement more common than counterexamples or disproofs. Finally, AI-system use is also highly concentrated, with OpenAI systems appearing most frequently, followed by Anthropic. Together, these findings suggest that AI-assisted mathematics is expanding rapidly but remains at an early and uneven stage of adoption.

Despite the critical role of grey literature in scholarly communication, artefacts such as Calls for Papers (CfPs) remain largely isolated from modern Scholarly Knowledge Graphs. The unstructured and highly heterogeneous nature of these documents has traditionally hindered their large-scale processing. In this demo paper, we present the Conference Organisers and Content Identifier (COCI), an AI-based framework designed to extract fine-grained, structured metadata from raw CfP texts. COCI employs a multi-stage pipeline that combines Large Language Models (LLMs) with semantic mapping techniques to integrate extracted entities with established knowledge bases, including OpenAlex, DBLP, TIB ConfIDent, and the AIDA Dashboard. By disambiguating authors and semantically aligning topics and conference series, COCI bridges the gap between informal scholarly dissemination and structured Semantic Web resources, laying the foundation for systematic analysis of non-publisher-based academic events.

This paper proposes a layered framework for defining the architecture of the computational unit of an Open Scholarly Infrastructure (OSI) and presents a concrete implementation possibility through the introduction of OpenCitations, an OSI dedicated to publishing citation data and bibliographic metadata. Grounded in the Principles of Open Scholarly Infrastructure (POSI), the study focuses on the technical dimensions of openness, sustainability, interoperability, and reproducibility that are required for a robust OSI. The proposed approach first identifies the core principles and technical features that a computational block in an OSI should guarantee, including separation of operational domains, orchestration, scalability, observability, automation, and workflow portability. It then maps these requirements onto a layered architectural model composed of Hardware, Virtualization, Orchestration, and Application layers, complemented by a transversal Meta layer. The OpenCitations case study shows how this framework can be instantiated in practice through an on-premises infrastructure. The case is presented with details regarding the technicalities and actual implementations of the proposed methodology. By combining a conceptual definition with a real-world implementation, this work offers both a practical reference and a methodological basis for designing the computational block of an OSI.

The FAIR Digital Object (FDO) framework mandates that metadata attribute values be expressed as persistent identifiers (PIDs) wherever possible, to produce a fully machine-actionable graph in which every reference is resolvable. The Europeana Data Model was designed long before the FDO specification, and it stores most metadata values as plain text. This serves human browsing well enough, but gives an automated agent nothing to follow across records or collections. We present a pipeline that transforms flat Europeana records into an FDO-compliant knowledge graph structured with CIDOC-CRM. Following the FDO specification, we model every heritage entity as a discrete FDO with its own PID, type, profile, and metadata layer. The core technical challenge is automating the FDO-prescribed distinction between values that must become PID references (resolvable entities) and those that may remain literals (terminal leaves such as notes, measurements, and dates). We address this with a large language model that classifies each metadata value, routes it to a controlled vocabulary (Getty AAT, Wikidata, VIAF, PeriodO), and links it to a shared entity FDO. We evaluate using 637 archaeological records from five Europeana providers, processing each with the LLM. The pipeline links 86% of metadata slots, resolving 58.5% of values Europeana had not already enriched. It also merges cross-lingual surface forms that byte-identical matching keeps apart, where 17 of 33 such merges are correct on manual review. Graph connectivity does not separate this from string matching; what distinguishes the FDO graph is that every node is typed and resolvable.