53 citations · 54 across the 4 of their papers we have counts for
4 papers
QED-Nano: Teaching a Tiny Model to Prove Hard Theorems
LM-Provers, Yuxiao Qu, Amrith Setlur +6
Proprietary AI systems have recently demonstrated impressive capabilities on complex proof-based problems, with gold-level performance reported at the 2025 International Mathematic…
Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning
Haiming Wang, Mert Unsal, Xiaohan Lin +37
We introduce Kimina-Prover Preview, a large language model that pioneers a novel reasoning-driven exploration paradigm for formal theorem proving, as showcased in this preview rele…
Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning
Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur +4
Training models to effectively use test-time compute is crucial for improving the reasoning performance of LLMs. Current methods mostly do so via fine-tuning on search traces or ru…
giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration
Guillaume Tauzin, Umberto Lupo, Lewis Tunstall +6
We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C+…