Chris Ge

Hi, I’m Chris Ge, a junior majoring in CS (course 6-3) at MIT. My research interests include interpretability and using world models for first-person video understanding.

I’m currently doing research in Antonio Torralba Lab on mechanistically understanding (text+image)-to-image generation from reference images in Diffusion Transformer models like FLUX.2. Previously, I did a research fellowship with Fulcrum, where I developed a method to extract useful features about agentic coding tasks for determining the task’s difficulty to an agent. I will be an Algo Developer intern at Hudson River Trading this summer. Besides research, I’m a TA for Introduction to Machine Learning (6.3900) at MIT, where I teach a recitation section and help develop course content.

publications

Agent psychometrics: Task-level performance prediction in agentic coding benchmarks

Chris Ge, Daria Kryvosheieva, Daniel Fried, Uzay Girit, and Kaivalya Hariharan

arXiv preprint arXiv:2604.00594, 2026

arXiv Bib PDF Code

@article{ge2026agent,
  title = {Agent psychometrics: Task-level performance prediction in agentic coding benchmarks},
  author = {Ge, Chris and Kryvosheieva, Daria and Fried, Daniel and Girit, Uzay and Hariharan, Kaivalya},
  journal = {arXiv preprint arXiv:2604.00594},
  year = {2026},
}

Private Linear Regression via a Down-Sensitivity to Privacy Reduction

Ittai Rubinstein, Chris Ge, and Samuel B Hopkins

Bib PDF

other projects

Team Leduc Poker

We used multi-agent learning algorithms to find a correlated equilibrium of the Team Leduc Hold'em game, a variant of Leduc poker where players 1 & 3 and players 2 & 4 play as a team without being able to communicate privately during the game. We submitted our strategies as part of the poker competition for MIT 6.S890 Multiagent Learning and got 2nd place.

Code Report Presentation

Phoneme Bridges

We augment textual data with phonetic information in order to improve cross-lingual transfer from Hindi, a high-resource language, to Urdu, a low-resource language that shares many words but with a different written script. Done as a group project for 6.8611 Natural Language Processing.

Code Report Poster