arXiv subjects

Chaoqun Ni

Publications and source records attributed to Chaoqun Ni.

14 recordsLinked to original sources

arXiv2026

AI-Assisted Writing Is Growing Fastest Among Less Established Scientists in Non-English-Speaking Countries

The recent emergence of AI-assisted writing raises an important question: how is this new technology being adopted across the scientific community, and how does adoption vary across linguistic and professional contexts? We analyze over two million full-text biomedical publications from PubMed Central from 2021 to 2024 using a distribution-based framework to estimate AI-generated content. We found that, in biomedical publications, AI-generated content increased substantially after ChatGPT, with larger increases in publications from countries with lower English proficiency. Increases were also greater among scientists with fewer publications and citations, those at earlier career stages, and those at lower-ranked institutions. Prior AI research experience was associated with greater increases in AI-assisted writing, which were also modestly associated with greater increases in publication productivity. These findings show that AI-assisted writing is growing fastest among biomedical scientists who may have historically faced barriers, a pattern with potentially positive implications for equity in science.

cs.DL↗

arXiv2026

Generating Biomedical Fact-Checking Reports with RL-Enhanced Agentic Search

Automated fact-checking is essential for ensuring the reliability of public health information, yet the biomedical domain poses unique challenges. Validating biomedical claims requires rigorous interpretation of scientific literature, assessment of retrieved evidence, and comprehensive justification toward the conclusion. Although Large Language Models (LLMs) enhanced by Retrieval-Augmented Generation (RAG) and agentic search perform automated fact-checking in a retrieve-then-verify paradigm, current methods still output isolated prediction labels, lacking explanatory depth and offers limited utility for human understanding. To bridge this gap, we introduce an LLM-based agent named BioCheck Agent that generates structured biomedical fact-checking reports with agentic search. Rather than merely outputting supported or refuted labels, our agent synthesizes final conclusions with retrieved evidence and rigorous analysis. To ensure domain-specific accuracy, BioCheck Agent exclusively searches high-quality scientific literature in PubMed, utilizing advanced Boolean search operators. Recognizing that direct prompting often results in hallucinations and low-quality reports, especially for lightweight open-source models, we further propose the Evidence-Grounded Group Relative Policy Optimization (EG-GRPO) to perform reinforcement learning on BioCheck Agent with a task-specific reward that incentivizes advanced search behavior and high-quality evidence retrieval while penalizing hallucinations. Our experimental results show that compared to the base model Qwen3.5-4B, BioCheck Agent with EG-GRPO improves label prediction accuracy on SciFact by 9.95%. Furthermore, it achieves a 3.7% higher evidence quality score and a 19.63% lower evidence hallucination rate, demonstrating its ability to generate biomedical fact-checking reports with improved accuracy and quality.

cs.AI↗

arXiv2026

Advisor career stage and PhD advisee outcomes

PhD advisors are central to doctoral training, but their influence may vary across career stages. Early-, mid-, and late-career advisors may differ in research activity, mentoring capacity, professional networks and access to resources. However, little is known about how PhD advisor career stage is associated with PhD student development outcomes. Drawing on multiple large-scale datasets comprising 250,838 advisor-advisee pairs from 312 U.S. PhD-granting institutions, we examine the relationship between advisor career stage and PhD advisee outcomes in knowledge production, collaboration networks and academic career placement. We find that early-career PhD advisors are associated with advisees' higher research productivity and citation performance, more opportunities to engage in direct and intensive research collaboration, and greater likelihood of securing a faculty position. Mid- and late-career faculty, by contrast, appear to have advantages in providing network capital which students can inherit after graduation, training PhD advisees to produce disruptive research, and supporting them in securing faculty positions at top institutions. This study contributes to a more comprehensive understanding of the reproduction of scientific talent by revealing the role of advisor career stage in shaping this process. These findings have implications for doctoral applicants' decision-making and for institutional policymaking on PhD training, faculty support and faculty evaluation.

cs.CY↗

arXiv2026

Scientific exploration, collaboration and labor division in the large language model era

Large language models (LLMs) have rapidly and significantly entered scientific workflows, but it remains unclear how their diffusion is associated with changes in scientists' strategies in research directions and team building. We link PubMed Central full text with OpenAlex publication and collaboration histories for 775,323 scientists and analyze CRediT contribution statements from 137,120 multi-author papers. After 2022, scientists increasingly published across more intellectually distant fields and entered fields in which they had not previously worked. These increases in interdisciplinarity and exploration were especially pronounced among established scientists and scientists from non-English-speaking low- and middle-income countries. Authors with stronger AI-writing signals were already more interdisciplinary and exploratory before the widespread adoption of LLMs, and the gap widened further after 2022 compared with authors with weaker AI-writing signals. Scientists' collaboration networks also became more interdisciplinary after 2022. Yet, among authors with stronger AI-writing signals, research interdisciplinarity was less closely tied to the disciplinary diversity of their collaborators. The division of labor within research teams also became more differentiated. Contributors on papers published after 2022 reported narrower role sets on average, coauthors shared fewer roles in common, and their role profiles became less rigid and more fluid. Software and validation roles increased, while conceptual and management roles decreased. These patterns suggest that team members are taking on more distinct responsibilities and may rely less on one another to perform research tasks. Overall, this study indicates that the LLM era coincides with a broader reorganization of scientific exploration, collaboration, and the division of labor.

cs.DL↗

arXiv2026

Global training and the collaborative structure of elite U.S. science

Globally trained scientific labor is a substantial component of U.S. universities, yet the organizational mechanisms linking foreign degree training to elite scientific output remain poorly understood. We link comprehensive U.S. faculty rosters to more than 12 million OpenAlex-indexed faculty-publication observations from 2011 to 2020. Faculty with non-U.S. degrees constitute one-tenth of the U.S. professoriate but account for larger shares of total publications and top-1% cited papers. This overrepresentation is concentrated in high-output disciplinary domains and research-intensive institutions. Within institution - domain - rank - year strata, however, differences in top-1% output, FWCI, and corresponding-author share attenuate sharply, indicating that much of the aggregate pattern reflects organizational placement rather than large within-context citation advantages. Collaboration structure further differentiates foreign- and domestically trained faculty: mixed domestic-foreign faculty teams exhibit substantially elevated elite-output rates, and the association attenuates strongly after accounting for team size, suggesting that collaboration scale is central to the pattern. Topic-distinctiveness analyses show little evidence that foreign-degree faculty occupy unusually rare research niches. Overall, foreign-degree training is best understood less as an individual productivity attribute than as a structural feature of elite U.S. science, operating through institutional concentration and collaborative integration.

cs.DL↗

arXiv2026

Faculty mobility reallocates research capacity within persistent institutional hierarchies

Faculty mobility is often understood as a mechanism through which universities redistribute scientific talent and potentially improve research performance. Yet the system-level structure of mobility and its association with individual research trajectories have rarely been examined together. We link longitudinal faculty rosters from U.S. research universities to OpenAlex publication records and study 11,535 tenure-system faculty members who changed institutions between 2011 and 2020, with a comparison group of more than 200,000 non-moving faculty members. A directed network of faculty moves reveals a strongly hierarchical market: high-prestige institutions are net importers, lower-prestige institutions are net exporters, and the mobility hierarchy closely parallels the hierarchy observed in faculty hiring. However, event-study models that account for pre-move trajectories show little evidence of sustained post-move gains in publication volume, citation impact, or top-cited publication rates, including among upward moves to more prestigious institutions. The most consistent post-move change is collaborative: movers form new coauthor ties. We also observe modest increases in the share of papers with positive CD-index values. These patterns suggest that faculty mobility primarily reallocates existing research capacity within a persistent institutional hierarchy rather than systematically altering individual research trajectories.

cs.DL↗

arXiv2026

AI-assisted writing and the reorganization of scientific knowledge

Generative AI systems such as ChatGPT are increasingly used in scientific writing, yet their broader implications for the organization of scientific knowledge remain unclear. We examine whether AI-assisted writing intensity, measured as the share of text in a paper that is predicted to exhibit features consistent with LLM-generated text, is associated with scientific disruption and knowledge recombination. Using approximately two million full-text research articles published between 2021 and 2024 and linked to citation networks, we document a sharp temporal pattern beginning in 2023. Before 2023, higher AI-assisted writing intensity is weakly or negatively associated with disruption; after 2023, the association becomes positive in within-author, within-field analyses. Over the same period, the positive association between AI-assisted writing intensity and cross-field citation breadth weakens substantially, and the negative association with citation concentration attenuates. Thus, the post-2023 increase in disruption is not accompanied by broader knowledge sourcing. These patterns suggest that generative AI is associated with more disruptive citation structures without a corresponding expansion in cross-field recombination. Rather than simply broadening the search space of science, AI-assisted writing may be associated with new forms of recombination built from relatively narrower knowledge inputs.

cs.DL↗

arXiv2026

Let the Abyss Stare Back Adaptive Falsification for Autonomous Scientific Discovery

Autonomous scientific discovery is entering a more dangerous regime: once the evaluator is frozen, a sufficiently strong search process can learn to win the exam without learning the mechanism the task was meant to reveal. This is the idea behind our title. To let the abyss stare back is to make evaluation actively push against the candidate through adaptive falsification, rather than passively certify it through static validation. We introduce DASES, a falsification-driven framework in which an Innovator, an Abyss Falsifier, and a Mechanistic Causal Extractor co-evolve executable scientific artifacts and scientifically admissible counterexample environments under a fixed scientific contract. In a controlled loss-discovery problem with a single editable locus, DASES rejects artifacts that static validation would have accepted, identifies the first candidate that survives the admissible falsification frontier, and discovers FNG-CE, a loss that transfers beyond the synthetic discovery environment and consistently outperforms CE and CE+L2 under controlled comparisons across standard benchmarks, including ImageNet.

cs.CV↗

arXiv2026

BibAgent: An Agentic Framework for Traceable Miscitation Detection in Scientific Literature

Citations are the bedrock of scientific authority, yet their integrity is compromised by widespread miscitations: ranging from nuanced distortions to fabricated references. Systematic citation verification is currently unfeasible; manual review cannot scale to modern publishing volumes, while existing automated tools are restricted by abstract-only analysis or small-scale, domain-specific datasets in part due to the "paywall barrier" of full-text access. We introduce BibAgent, a scalable, end-to-end agentic framework for automated citation verification. BibAgent integrates retrieval, reasoning, and adaptive evidence aggregation, applying distinct strategies for accessible and paywalled sources. For paywalled references, it leverages a novel Evidence Committee mechanism that infers citation validity via downstream citation consensus. To support systematic evaluation, we contribute a 5-category Miscitation Taxonomy and MisciteBench, a massive cross-disciplinary benchmark comprising 6,350 miscitation samples spanning 254 fields. Our results demonstrate that BibAgent outperforms state-of-the-art Large Language Model (LLM) baselines in citation verification accuracy and interpretability, providing scalable, transparent detection of citation misalignments across the scientific literature.

cs.DL↗

arXiv2026

Traversal-as-Policy: Log-Distilled Gated Behavior Trees as Externalized, Verifiable Policies for Safe, Robust, and Efficient Agents

Autonomous LLM agents fail because long-horizon policy remains implicit in model weights and transcripts, while safety is retrofitted post hoc. We propose Traversal-as-Policy: distill sandboxed OpenHands execution logs into a single executable Gated Behavior Tree (GBT) and treat tree traversal -- rather than unconstrained generation -- as the control policy whenever a task is in coverage. Each node encodes a state-conditioned action macro mined and merge-checked from successful trajectories; macros implicated by unsafe traces attach deterministic pre-execution gates over structured tool context and bounded history, updated under experience-grounded monotonicity so previously rejected unsafe contexts cannot be re-admitted. At runtime, a lightweight traverser matches the base model's intent to child macros, executes one macro at a time under global and node-local gating, and when stalled performs risk-aware shortest-path recovery to a feasible success leaf; the visited path forms a compact spine memory that replaces transcript replay. Evaluated in a unified OpenHands sandbox on 15+ software, web, reasoning, and safety/security benchmarks, GBT improves success while driving violations toward zero and reducing cost. On SWE-bench Verified (Protocol A, 500 issues), GBT-SE raises success from 34.6% to 73.6%, reduces violations from 2.8% to 0.2%, and cuts token/character usage from 208k/820k to 126k/490k; with the same distilled tree, 8B executors more than double success on SWE-bench Verified (14.0%58.8%) and WebArena (9.1%37.3%).

cs.LG↗

arXiv2025

Interdisciplinary PhDs face barriers to top university placement within their disciplines

Interdisciplinary research has gained prominence as a necessity for addressing complex challenges, yet its impact on early academic careers remains unclear. This study examines how interdisciplinarity during doctoral training influences faculty placement at top universities across diverse fields. Analyzing the career trajectories of over 30,000 tenure-track faculty members who earned their Ph.D. degrees after 2005 and their initial faculty placement at 355 U.S. universities, we find that faculty newly hired by top-ranked universities tend to be less interdisciplinary in their Ph.D. research, particularly when they obtained Ph.D. from top universities and remain in their Ph.D. research field. This may reflect community trends towards homogeneity: at top universities, the existing faculty research is less interdisciplinary and more aligned with the candidates that they hire (who also exhibit lower interdisciplinarity). This preference disadvantages the placement of women graduates, who exhibit higher interdisciplinarity on average. Furthermore, we show that newly hired faculty with greater interdisciplinarity, when placed at top universities, tend to achieve higher long-term research productivity. This suggests a potential loss in knowledge production if top universities continue to undervalue interdisciplinary candidates. These findings highlight structural barriers in faculty hiring and raise concerns about the long-term consequences of prioritizing disciplinary specialization over interdisciplinary expertise.

cs.CY↗

arXiv2025

Using language models to label clusters of scientific documents

Automated label generation for clusters of scientific documents is a common task in bibliometric workflows. Traditionally, labels were formed by concatenating distinguishing characteristics of a cluster's documents; while straightforward, this approach often produces labels that are terse and difficult to interpret. The advent and widespread accessibility of generative language models, such as ChatGPT, make it possible to automatically generate descriptive and human-readable labels that closely resemble those assigned by human annotators. Language-model label generation has already seen widespread use in bibliographic databases and analytical workflows. However, its rapid adoption has outpaced the theoretical, practical, and empirical foundations. In this study, we address the automated label generation task and make four key contributions: (1) we define two distinct types of labels: characteristic and descriptive, and contrast descriptive labeling with related tasks; (2) we provide a formal descriptive labeling that clarifies important steps and design considerations; (3) we propose a structured workflow for label generation and outline practical considerations for its use in bibliometric workflows; and (4) we develop an evaluative framework to assess descriptive labels generated by language models and demonstrate that they perform at or near characteristic labels, and highlight design considerations for their use. Together, these contributions clarify the descriptive label generation task, establish an empirical basis for the use of language models, and provide a framework to guide future design and evaluation efforts.

cs.DL↗

arXiv2025

Parenthood Penalties in Academia: Childcare Responsibilities, Gender Role Beliefs and Institutional Support

Despite progress toward gender parity, women remain underrepresented in academia, particularly in senior research positions. This study investigates the role of parenthood in shaping gender disparities in academic careers, focusing on the complex interplay between gender, childcare responsibilities, gender role beliefs, institutional support, and scientists' career achievements. Using a large-scale survey of 5,670 U.S. and Canadian academics, supplemented with bibliometric data from Web of Science, it reveals that childcare responsibilities significantly mediate gender disparities in both subjective and objective academic achievements, with women assuming a disproportionate share of childcare duties. In particular, women shoulder a greater caregiving load when their partners are employed full-time outside academia. However, egalitarian gender role beliefs have been playing an important role in shifting this structure by transforming women academics' behaviors. As women's egalitarian gender role beliefs strengthen, their childcare responsibilities tend to diminish-an effect not mirrored in men. Institutional parental support policies show mixed effects. While flexible work schedules and childcare support can mitigate the negative association between childcare responsibilities and career outcomes of women academics, policies such as tenure clock extensions and paternity leave may inadvertently intensify it. Addressing these disparities necessitates a comprehensive approach that integrates shifts in individual attitudes, broader sociocultural changes, and policy improvements.

physics.soc-ph↗

arXiv2025

Tenure and Research Trajectories

Tenure is a cornerstone of the US academic system, yet its relationship to faculty research trajectories remains poorly understood. Conceptually, tenure systems may act as a selection mechanism, screening in high-output researchers; a dynamic incentive mechanism, encouraging high output prior to tenure but low output after tenure; and a creative search mechanism, encouraging tenured individuals to undertake high-risk work. Here, we integrate data from seven different sources to trace US tenure-line faculty and their research outputs at an unprecedented scale and scope, covering over 12,000 researchers across 15 disciplines. Our analysis reveals that faculty publication rates typically increase sharply during the tenure track and peak just before obtaining tenure. Post-tenure trends, however, vary across disciplines: in lab-based fields, such as biology and chemistry, research output typically remains high post-tenure, whereas in non-lab-based fields, such as mathematics and sociology, research output typically declines substantially post-tenure. Turning to creative search, faculty increasingly produce novel, high-risk research after securing tenure. However, this shift toward novelty and risk-taking comes with a decline in impact, with post-tenure research yielding fewer highly cited papers. Comparing outcomes across common career ages but different tenure years or comparing research trajectories in tenure-based and non-tenure-based research settings underscores that breaks in the research trajectories are sharply tied to the individual's tenure year. Overall, these findings provide a new empirical basis for understanding the tenure system, individual research trajectories, and the shape of scientific output.

physics.soc-ph↗