arXiv:2607. 20916v1 Announce Type: new Abstract: Generative AI lets large language models produce scholarly-looking text within seconds, yet fluency does not equal valid explanation.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2605.30947v4 Announce Type: replace Abstract: LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and qu...
arXiv:2607. 04049v1 Announce Type: new Abstract: We argue that generative AI can degrade research by eroding the very practices through which scholarly judgement is formed and academic trust is built.
The paper investigates when an interpretation in generative AI is considered established, arguing that passing local factual checks is insufficient. It introduces three concepts—interpretive appearance, evaluation contract, and standing substitution—to analyze how interpretations gain recognition within sociotechnical processes. The authors propose delayed closure as a practice to keep recognized interpretations revisable and outline five public requirements for transparency, evidence, failure handling, contract revision, and responsibility.
arXiv:2607. 22684v1 Announce Type: cross Abstract: Artificial intelligence systems increasingly mediate how science is found and credited.
arXiv:2608.28596v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly embedded in scientific workflows for literature analysis, drafting, and review. Existing systems adv...
arXiv:2606. 29437v1 Announce Type: cross Abstract: The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them?