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John Jennings

dblp:228/2808 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Automated reasoning and model checking · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Automated reasoning and model checking › automated theorem proving
neural theorem proving
0.812024
PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition · NeurIPS 2024
Automated reasoning and model checking
theorem proving
0.812024
PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition · NeurIPS 2024

Methods — techniques the papers use, named apart from their topics

symbolic theorem prover · 0.8neural theorem prover · 0.8
YearPublicationVenuePosition
2024 PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition
abstract
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 hand-constructed formalizations of 640 theorems sourced from the William Lowell Putnam Mathematical Competition, the premier undergraduate-level mathematics competition in North America. All the problems have formalizations in Lean 4 and Isabelle; a substantial subset also has Coq formalizations. PutnamBench requires significant problem-solving ability and proficiency in a broad range of topics taught in undergraduate mathematics courses. We use PutnamBench to evaluate several established neural and symbolic theorem-provers. These approaches can only solve a handful of the PutnamBench problems, establishing the benchmark as a difficult open challenge for research on neural theorem-proving. PutnamBench is available at https://github.com/trishullab/PutnamBench.
George Tsoukalas, Jasper Lee, John Jennings, Jimmy Xin, Michelle Ding, Michael Jennings, Amitayush Thakur, Swarat Chaudhuri
NeurIPS3