A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence (2025 · 36 citations)
Today's LLMs are essentially frozen once trained. They cannot update their own parameters or behavior when they meet unfamiliar tasks, shifting knowledge, or changing interaction settings, and this becomes a serious limitation when agents work in open, interactive environments. This survey does not introduce new data…
Language Models as Science Tutors (2024 · 30 citations)
Most evaluations of scientific language models only check final answers to short problems. That says little about whether a model can help a student working through a long textbook chapter. To measure this, the authors built TutorEval: 834 questions written by 17 STEM researchers with teaching experience, tied to 202…
Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems (2025 · 4 citations)
The paper asks whether LLMs can act as dependable "AI scientists" by testing a basic research skill: inferring the hidden mechanism of a black-box system from its input–output behavior.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

Co-authors here

12 of 16 co-authors with a page, the most papers together first.

Papers of our library cite their works 70 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated 3 November 2026