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Daniel Selsam

Daniel Selsam has 2 papers in our library, in 2018–2025, most of them on Reasoning; often with Alexander Wei, David Dohan, Francis Song.

2 papers in our library · 2018–2025

OpenAlex · h-index 8 · 16 works · 832 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 11,840 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5548

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

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Stanford UniversityStanford · United States2016–2019 · 7 papers🏢MicrosoftRedmond · United States2018–2021 · 4 papers

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

 Fields, topics and concepts

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputational Theory and Mathematics · 1 workComputer Networks and Communications · 1 workArts and Humanities · 1 workComputer Science Applications · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1 work

 Works by year

2020s

20251 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Learning a SAT Solver from Single-Bit Supervision (2018 · 232 citations)
    Selsam et al. ask whether a neural network can learn to solve propositional satisfiability (SAT), an NP-complete problem, when it is told only whether each instance is satisfiable. They build NeuroSAT, a message passing neural network (MPNN). It represents a CNF formula as a graph of literal and clause nodes and…
  2. Competitive Programming with Large Reasoning Models (2025 · 68 citations)
    This OpenAI report asks whether LLM performance in competitive programming improves more from hand-built, task-specific inference pipelines or from scaling general reinforcement learning.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Learning a SAT Solver from Single-Bit SupervisionarXiv:1802.03685Daniel Selsam, Matthew Lamm, Benedikt Bünz, et al.2018 · 232 citations
  2. Competitive Programming with Large Reasoning ModelsarXiv:2502.06807OpenAI: Ahmed El-Kishky, Alexander Wei, Andre Saraiva, et al.2025 · 68 citations
  3. Developing Bug-Free Machine Learning Systems With Formal Mathematics ↗arXiv:1706.08605 · no summary yetDaniel Selsam, Percy Liang, David L. Dill2017 · 42 citations
  4. Guiding High-Performance SAT Solvers with Unsat-Core Predictions ↗arXiv:1903.04671 (International Conference on Theory and Applications of Satisfiability Testing. Springer, Cham, 2019) · no summary yetDaniel Selsam, Nikolaj Bjørner2019 · 16 citations

 Co-authors here

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

 Cited by

Papers of our library cite their works 11,840 times (of the 149,439 papers whose bibliographies we read); 2,589 citing papers with a page here →.

Updated

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