Intellectual leadership should define authorship rather than textual production in LLM assisted scientific writingAI Tools Could Change Who Gets Credit in Science

The growing use of large language models (LLMs) in scientific writing has intensified debates about authorship, originality, and research integrity. Critics often argue that LLM-generated text lacks originality because it is derived from existing literature. This Perspective challenges that assumption by arguing that scientific knowledge, whether produced by humans or machines, is inherently cumulative. We argue that both researchers and LLM systems operate within inherited bodies of scientific knowledge, making intellectual leadership rather than textual production the defining criterion of authorship. Researchers remain the legitimate authors when they formulate the research question, develop the central ideas, and critically direct LLMs throughout the writing process. Beyond authorship, we argue that scientific publishing increasingly rewards procedural compliance over epistemic quality, allowing formal citation practices to substitute for conceptual coherence. Rather than undermining research integrity, LLMs offer an opportunity to restore greater emphasis on reasoning and critical evaluation. Finally, we propose integrating standardized LLM-based verification directly into journal submission systems. Automated verification of references, factual consistency, and scientific and technical integrity should precede peer review, allowing reviewers to focus on originality, scientific significance, and conceptual evaluation. Properly governed, LLMs can become not only a writing assistant but also an institutional component of scientific quality assurance.