2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
SorryDB: Can AI Provers Complete Real-World Lean Theorems?
Austin Letson, Leopoldo Sarra, Auguste Poiroux +9
We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub. Unlike existing static benchmarks, often composed…
Advancing Mathematics Research with AI-Driven Formal Proof Search
George Tsoukalas, Anton Kovsharov, Sergey Shirobokov +18
Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research. A mitigation is using LLMs to gener…
ProofWala: A Framework for Multilingual Proof Data Synthesis and Theorem-Proving
Amitayush Thakur, George Tsoukalas, Greg Durrett +1
Neural approaches to theorem proving require robust infrastructure for interfacing with interactive theorem provers (ITPs), extracting structured proof data, and executing proof se…
Learning Interestingness in Automated Mathematical Theory Formation
George Tsoukalas, Rahul Saha, Amitayush Thakur +2
We take two key steps in automating the open-ended discovery of new mathematical theories, a grand challenge in artificial intelligence. First, we introduce , a rein…
PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition
George Tsoukalas, Jasper Lee, John Jennings +5
We present PutnamBench, a new multi-language benchmark for evaluating the ability of neural theorem-provers to solve competition mathematics problems. PutnamBench consists of 1692…