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Brando Miranda

Brando Miranda has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Reasoning; often with Sanmi Koyejo, Chuyue Sun, Clark Barrett.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 7 · 64 works · 1,187 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 177 times in our library · ⭐ Paper Stars ranking: #5280

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 States2022–2026 · 17 paperscurrent🎓Massachusetts Institute of TechnologyCambridge · United States2016–2019 · 9 papers🎓University of Illinois Urbana-ChampaignUrbana · United States2021–2022 · 2 papers🔬McGovern Institute for Brain ResearchCambridge · United States2017–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 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 worksArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 workMathematics · 1 workComputational Theory and Mathematics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Are Emergent Abilities of Large Language Models a Mirage? (2023 · 342 citations)
    The paper asks whether the "emergent abilities" reported for large language models are real. These are skills that seem to appear suddenly and unpredictably once models pass some size. The authors argue that this sharpness often comes from how performance is scored, not from abrupt changes inside the models. Using a…
  2. Pantograph: A Machine-to-Machine Interaction Interface for Advanced Theorem Proving, High Level Reasoning, and Data Extraction in Lean 4 (2024 · 14 citations)
    Machine learning agents that search for proofs in Lean 4 depend on an interface to the proof assistant. The standard Language Server Protocol was built for humans: it tracks cursor positions and offers no easy way to collect tactic data or to leave proof gaps for automation. Pantograph is an API and REPL written…

 Co-authors here

4 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 177 times (of the 149,439 papers whose bibliographies we read); 49 citing papers with a page here →.

Updated

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