Michael Brenner — accepted AI-conference papers

2026

CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers

Haining Pan, James V Roggeveen, Erez Berg, Juan Felipe Carrasquilla Alvarez, Debanjan Chowdhury, Surya Ganguli +13

ICLR 2026poster

Large language models (LLMs) have demonstrated remarkable progress in coding and mathematical problem-solving; however, evaluation on advanced research-level problems in the hard sciences remains scarce. To fill this gap, we present \cmt, a dataset of 50 original problems covering condensed matter…

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2025

CURIE: Evaluating LLMs on Multitask Scientific Long-Context Understanding and Reasoning

Hao Cui, Zahra Shamsi, Gowoon Cheon, Xuejian Ma, Shutong Li, Maria Tikhanovskaya +24

ICLR 2025poster

Scientific problem-solving involves synthesizing information while applying expert knowledge. We introduce CURIE, a scientific long-Context Understanding, Reasoning, and Information Extraction benchmark to measure the potential of Large Language Models (LLMs) in scientific problem-solving a…

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2025

HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Class

James V Roggeveen, Erik Y. Wang, David Ettel, Will Flintoft, Peter Donets, Raglan Ward +39

NeurIPS 2025poster

Large language models (LLMs) have shown remarkable progress in mathematical problem-solving, but evaluation has largely focused on problems that have exact analytical solutions or involve formal proofs, often overlooking approximation-based problems ubiquitous in applied science and engineering. To…

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2025

HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics

Jingxuan Fan, Sarah Martinson, Erik Y. Wang, Kaylie Hausknecht, Jonah Brenner, Danxian Liu +3

ICLR 2025poster

Advanced applied mathematics problems are underrepresented in existing Large Language Model (LLM) benchmark datasets. To address this, we introduce $\textbf{HARDMath}$, a dataset inspired by a graduate course on asymptotic methods, featuring challenging applied mathematics problems that require anal…

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2022

Context-Aware Abbreviation Expansion Using Large Language Models

Shanqing Cai, Subhashini Venugopalan, Katrin Tomanek, Ajit Narayanan, Meredith Morris, Michael Brenner

NAACL 2022long

Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phrases are abbreviated aggressively as primarily word-initial letters. Our approach is to expand the abbreviations into full…

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2021

Variational Data Assimilation with a Learned Inverse Observation Operator

Thomas Frerix, Dmitrii Kochkov, Jamie Smith, Daniel Cremers, Michael Brenner, Stephan Hoyer

ICML 2021spotlight

Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data. The physical model can subsequently be evolved into the future to make predictions. This principle is a cornerstone of large scale forecasting applications such as nume…

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