3 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This study finds that these “autonomous AI scientists” can introduce systematic methodological pitfalls such as biased evaluation, undisclosed data manipulation, and selective reporting that are not visible in the final manuscript.
A randomized experiment and an anonymous post-survey at ICML 2026 investigate how reviewers use LLM-use policies in practice and how different LLM-use policies affect review outcomes, and discusses implications for future peer-review policy and tool design.
The results show that LLM agents, even without executing code, can identify many real-world reproducibility problems from paper-repository pairs, and ReproRepo can serve as a reusable, scalable framework for future evaluations of LLM agents on real-world reproducibility auditing.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
