LLM use in scholarly writing poses a provenance problem

The AI Chemist: To be trustworthy, LLMs need to show their work

Good scientists reveal how they do their experiments and report their results; so should any machine-driven research

AI for Research | Scite

Scite searches 280M+ scholarly articles to give you citation-backed answers and show you how every claim is supported or disputed.

Unequal Scientific Recognition in the Age of LLMs

Large language models (LLMs) are reshaping how scientific knowledge is accessed and represented. This study evaluates the extent to which popular and frontier LLMs including GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro recognize scientists, benchmarking their outputs against OpenAlex and Wikipedia. Using a dataset focusing on 100,000 physicists from OpenAlex to evaluate LLM recognition, we uncover substantial disparities: LLMs exhibit selective and inconsistent recognition patterns. Recognition correlates strongly with scholarly impact such as citations, and remains uneven across gender and geography. Women researchers, and researchers from Africa, Asia, and Latin America are significantly underrecognized. We further examine the role of training data provenance, identifying Wikipedia as a potential sources that contributes to recognition gaps. Our findings highlight how LLMs can reflect, and potentially amplify existing disparities in science, underscoring the need for more transparent and inclusive knowledge systems.

Elsevier vs Meta: first science publisher sues over scraped research papers

Science publishing giant Elsevier has joined a class-action lawsuit against Meta that alleges the reproduction of copyrighted works in developing the Llama AI model.

Elsevier vs Meta: first science publisher sues over scraped research papers

Science publishing giant Elsevier has joined a class-action lawsuit against Meta that alleges the reproduction of copyrighted works in developing the Llama AI model.

Dan Shipper 📧 on Twitter / X

this is true and is a big reason why you don’t need to be a highly technical researcher to use LLMs in surprising and novel ways https://t.co/TuxNzXzToU— Dan Shipper 📧 (@danshipper) July 27, 2025

Artificial intelligence tools expand scientists’ impact but contract science’s focus

Nature - Artificial intelligence boosts individual scientists’ output, citations and career progression, but collectively narrows research diversity and reduces collaboration, concentrating...

Scientific production in the era of Large Language Models

Large Language Models (LLMs) are rapidly reshaping scientific research. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and 246M online accesses to scientific documents. We find: 1) scientists adopting LLMs to draft manuscripts demonstrate a large increase in paper production, ranging from 23.7-89.3% depending on scientific field and author background, 2) LLM use has reversed the relationship between writing complexity and paper quality, leading to an influx of manuscripts that are linguistically complex but substantively underwhelming, and 3) LLM adopters access and cite more diverse prior work, including books and younger, less-cited documents. These findings highlight a stunning shift in scientific production that will likely require a change in how journals, funding agencies, and tenure committees evaluate scientific works.

elvis on Twitter / X

arXiv Papers → LLM ArtifactsThis is how I keep up with AI research now.It's like having access to the most personalized arXiv feed.Automations run everyday to curate papers based a set of rules and insights.Curated papers are indexed and power the artifacts.Agent… pic.twitter.com/5UCxF8ZsT0— elvis (@omarsar0) May 6, 2026

Curated retrieval versus open web search in public AI information...

Public institutions increasingly use large language models (LLMs) to answer citizens' questions, often pairing a curated knowledge base with live web search, yet whether the sources behind these...

Semantic Scholar | AI-Powered Research Tool

Semantic Scholar uses groundbreaking AI and engineering to understand the semantics of scientific literature to help Scholars discover relevant research.

stephenturner/skill-deslop

De-AI-ify scientific writing

Yuchen Jin on Twitter / X

Karpathy’s “LLM Wiki” pattern: stop using LLMs as search engines over your docs. Use them as tireless knowledge engineers who compile, cross-reference, and maintain a living wiki. Humans curate and think.Diagram generated by my Claude agent knowledge worker. https://t.co/5u5i1GeFK8 pic.twitter.com/NIaq3KlAok— Yuchen Jin (@Yuchenj_UW) April 4, 2026

Scholarly Communication Is a Research Problem. This Means You.

Scholarly Communication Is a Research Problem.

Artificial

An LLM is a computer program. We should talk about it like a computer program.