#scientific-integrity News & Analysis

6 articles tagged with #scientific-integrity. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBearisharXiv – CS AI · May 117/10

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LLM hallucinations in the wild: Large-scale evidence from non-existent citations

Researchers auditing 2.5 million scientific papers found 146,932 hallucinated citations in 2025 alone, with non-existent references surging sharply after LLM adoption. The errors concentrate in AI-heavy fields and papers with linguistic signatures of AI assistance, while current journal moderation fails to catch most instances, threatening scientific integrity and reinforcing existing biases in academic credit attribution.

AIBearisharXiv – CS AI · May 97/10

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When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge

A comprehensive study reveals that while AI adoption in research has surged exponentially since 2015, the technology remains concentrated in narrow domains tied to computer science with limited epistemological transformation. The research identifies concerning patterns including higher retraction rates in AI-supported work, citation inflation, and geographic disparities in adoption across countries and disciplines.

AINeutralarXiv – CS AI · 3d ago6/10

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ResearchLoop: An Evidence-Gated Control Plane for AI-Assisted Research

ResearchLoop is a new technical framework that addresses reproducibility and auditability challenges in AI-assisted research by implementing an evidence-gated control plane. The system treats research components—questions, contracts, evidence, claims, and papers—as durable state objects, enabling verification of research claims throughout the AI-assisted workflow. The framework was validated through nine experimental versions, including self-hosting and mathematical olympiad benchmarks.

AINeutralarXiv – CS AI · 3d ago6/10

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CiteCheck: Retrieval-Grounded Detection of LLM Citation Hallucinations in Scientific Text

Researchers introduce CiteCheck, a hybrid framework that detects when large language models fabricate or corrupt scientific citations by combining scholarly database retrieval with structured LLM verification. The system achieves 88.7% macro-F1 on a new 982-citation physics benchmark, outperforming GPT, Claude, and Gemini, addressing a critical reliability problem as LLMs become integrated into scientific research workflows.

🧠 Claude🧠 Gemini

AINeutralarXiv – CS AI · 4d ago6/10

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AI evaluation may bias perceptions: The importance of context in interpreting academic writing

A new study demonstrates that pooled benchmarks for detecting AI-generated academic text systematically misrepresent AI adoption across countries and research fields by ignoring contextual stylistic variations. Using country-field-specific benchmarks instead provides more accurate measurements and reveals that previous estimates substantially over- or underestimated AI use depending on geographic and disciplinary context.

AINeutralarXiv – CS AI · Apr 146/10

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Inspectable AI for Science: A Research Object Approach to Generative AI Governance

Researchers propose AI as a Research Object (AI-RO), a governance framework that treats generative AI interactions as inspectable, documented components of scientific research rather than debating authorship. The framework combines interaction logs, metadata packaging, and provenance records to ensure accountability, particularly for security and privacy research where confidentiality and auditability are critical.

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