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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
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.
arXiv:2607. 22684v1 Announce Type: cross Abstract: Artificial intelligence systems increasingly mediate how science is found and credited.
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?
arXiv:2606. 01613v2 Announce Type: replace-cross Abstract: This paper presents an agentic multimodal retrieval-augmented generation (RAG) framework for domain-specific literature reasoning, instantiated on a curated corpus of several thousand papers in intelligent tires, vehicle dynamics, vehicle control, sensing, estimation, and machine learning.
arXiv:2606. 01613v1 Announce Type: cross Abstract: This paper presents an agentic retrieval-augmented generation (RAG) framework for domain-specific technical reasoning support, instantiated over a curated corpus of approximately 2,100 academic papers in intelligent tires, vehicle dynamics, and vehicle control.
arXiv:2607. 28229v1 Announce Type: cross Abstract: The web is increasingly accessed by AI agents rather than humans.