18 citations · 18 across the 4 of their papers we have counts for
12 papers
AutoOR: Scalably Post-training LLMs to Autoformulate Operations Research Problems
Sumeet Ramesh Motwani, Chuan Du, Aleksander Petrov +4
Optimization problems are central to decision-making in manufacturing, logistics, scheduling, and other industrial settings. Translating complicated descriptions of these problems…
LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning
Sumeet Ramesh Motwani, Daniel Nichols, Charles London +17
As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An essential component of this…
HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification
Erik Y. Wang, Sumeet Motwani, James V. Roggeveen +7
Can AI make progress on important, unsolved mathematical problems? Large language models are now capable of sophisticated mathematical and scientific reasoning, but whether they ca…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1144
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
h1: Bootstrapping LLMs to Reason over Longer Horizons via Reinforcement Learning
Sumeet Ramesh Motwani, Alesia Ivanova, Ziyang Cai +5
Large language models excel at short-horizon reasoning tasks, but performance drops as reasoning horizon lengths increase. Existing approaches to combat this rely on inference-time…
MALT: Improving Reasoning with Multi-Agent LLM Training
Sumeet Ramesh Motwani, Chandler Smith, Rocktim Jyoti Das +6
Large Language Models (LLMs) often produce answers with a single chain-of-thought, which restricts their ability to explore reasoning paths or self-correct flawed outputs in comple…