9 papers
When Finite Free Curves Split
Baran Hashemi, Jihoon Hyun
We characterize equality in the finite free Stam and entropy-power inequalities, proving that Hermite polynomials are the unique extremizers among simple real-rooted inputs, up to…
Benchmarks in Leipzig
Andrei Balakin, Miklós Bóna, Marie-Charlotte Brandenburg +45
Between April 1 and May 15, 2026, a group of 49 mathematicians compiled a dataset of research-level mathematics questions with known answers. Most of the work was done during the t…
Spectral Structure in Finite Free Information Inequalities and -Stam Phase Transitions
Baran Hashemi
Using FlowBoost, a closed-loop deep generative optimization framework for extremal structure discovery, we investigate -generalizations of the finite free Stam inequality f…
Phylogenetic Tree Inference with Tropical Axial Attention
Chris Teska, Kurt Pasque, Ruriko Yoshida +1
In this work, we introduce a Tropical Axial Attention neural reasoning architecture that replaces vanilla softmax dot-product attention with max-plus operators, inducing a piecewis…
Flow-based Extremal Mathematical Structure Discovery
Gergely Bérczi, Baran Hashemi, Jonas Klüver
The discovery of extremal structures in mathematics requires navigating vast and nonconvex landscapes where analytical methods offer little guidance and brute-force search becomes…
IMProofBench: Benchmarking AI on Research-Level Mathematical Proof Generation
Johannes Schmitt, Gergely Bérczi, Jasper Dekoninck +57
As the mathematical capabilities of large language models (LLMs) improve, it becomes increasingly important to evaluate their performance on research-level tasks at the frontier of…
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms
Baran Hashemi, Kurt Pasque, Chris Teska +1
Can algebraic geometry enhance the sharpness, robustness, and interpretability of modern neural reasoning models by equipping them with a mathematically grounded inductive bias? To…
Can Transformers Do Enumerative Geometry?
Baran Hashemi, Roderic G. Corominas, Alessandro Giacchetto
How can Transformers model and learn enumerative geometry? What is a robust procedure for using Transformers in abductive knowledge discovery within a mathematician-machine collabo…
Interpretable correlator Transformer for image-like quantum matter data
Abhinav Suresh, Henning Schlömer, Baran Hashemi +1
Due to their inherent capabilities of capturing non-local dependencies, Transformer neural networks have quickly been established as the paradigmatic architecture for large languag…