Jason Rute

Jason Rute has 2 papers in our library, in 2021–2024, most of them on Language models & pretraining, Reasoning; often with Edward W. Ayers, Jesse Michael Han, Miroslav Olšák.

2 papers in our library · 2021–2024

The profile is the library's match of the name; two people with one name are not always told apart.

Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

Of its works in our library, with the number of works on each.

The most cited first (OpenAlex and our library).

Proof Artifact Co-training for Theorem Proving with Language Models (2021 · 82 citations)
Neural theorem provers for big formal libraries don't have much training data. Human-written proofs take a lot of work to produce, so large Transformer models overfit fast and stop improving as they get bigger. The authors propose PACT (Proof Artifact Co-Training) to get more data. Lean's kernel stores every proof as…
Graph2Tac: Online Representation Learning of Formal Math Concepts (2024 · 19 citations)
The paper tackles a practical gap in machine-learning provers for Coq: models trained offline cannot easily handle the new definitions, lemmas and proofs that appear when working in an unfamiliar formalization project.

5 co-authors with a page, the most papers together first.

Papers of our library cite their works 121 times (of the 149,439 papers whose bibliographies we read); 73 citing papers with a page here →.

Updated 3 November 2026