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Miroslav Olšák

Miroslav Olšák has 4 papers in our library, in 2019–2025, most of them on Reasoning; often with Josef Urban, Bartosz Piotrowski, Cezary Kaliszyk.

4 papers in our library · 2019–2025

OpenAlex · ORCID · h-index 8 · 37 works · 217 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 54 times in our library · ⭐ Paper Stars ranking: #7926

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

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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 4 worksComputational Theory and Mathematics · 4 worksMathematics · 1 workGeometry and Topology · 1 work

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 2 works

 Works by year

2020s

2010s

 Papers

The most cited first (OpenAlex and our library).

  1. ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (system description) (2020 · 42 citations)
    Learned guidance for saturation-style provers usually relies on symbol names. Such guidance cannot carry over to problems or libraries that use different names. This system description adds two name-agnostic clause classifiers to the ENIGMA framework of the E prover. Under either classifier, consistently renaming all…
  2. Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2 (2025 · 31 citations)
    AlphaGeometry2 (AG2) is Google DeepMind's successor to AlphaGeometry (AG1), a neuro-symbolic system for Euclidean geometry problems at International Mathematical Olympiad level.
  3. Property Invariant Embedding for Automated Reasoning (2019 · 30 citations)
    Machine-learned guidance for theorem provers depends on turning formulas into vectors. Most existing encoders, including the graph-based FormulaNet, learn a separate representation for each symbol name. They therefore cope poorly with the many fresh symbols that Skolemization and definitional clausification produce…
  4. 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.

 Co-authors here

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

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 54 times (of the 149,439 papers whose bibliographies we read); 24 citing papers with a page here →.

Updated

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