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Marcelo Menegali

dblp:312/5804 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—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.

Artificial intelligence
1 paper
Language models and text generation · 33% Knowledge representation and reasoning · 33% Generative modeling · 33%
Theoretical computer science
1 paper
Automated reasoning and model checking · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation
language-model-guided search
0.912025
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2 · J. Mach. Learn. Res. 2025
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic-based reasoning
symbolic reasoning
0.912025
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2 · J. Mach. Learn. Res. 2025
Machine learning › Generative modeling
synthetic data generation
0.912025
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2 · J. Mach. Learn. Res. 2025
Automated reasoning and model checking › automated reasoning › mathematical reasoning
geometry problem solving
0.912025
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2 · J. Mach. Learn. Res. 2025

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

symbolic engine · 1.7search tree · 1.7language model · 1.7knowledge sharing · 1.7
YearPublicationVenuePosition
2025 Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2
abstract
We present AlphaGeometry2, a significantly improved version of AlphaGeometry introduced in Nature, 625 (7995):476, 2024, which has now surpassed an average gold medalist in solving Olympiad geometry problems. To achieve this, we first extend the original AlphaGeometry language to tackle problems involving movements of objects, and problems containing linear equations of angles, ratios, and distances. This, together with support for non-constructive problems, has markedly improved the coverage rate of the AlphaGeometry language on International Math Olympiads 2000-2024 geometry problems from 66% to 88%. The search process of AlphaGeometry2 has also been greatly improved through the use of Gemini architecture for better language modeling, and a novel knowledge-sharing mechanism that enables effective communication between search trees. Together with further enhancements to the symbolic engine and synthetic data generation, we have significantly boosted the overall solving rate of AlphaGeometry to 84% on all geometry problems over the last 25 years, compared to 54% previously. AlphaGeometry2 was also part of the system that achieved the silver-medal standard at IMO 2024 https://dpmd.ai/imo-silver. Finally, we report progress towards using AlphaGeometry2 as a part of a fully automated system that reliably solves geometry problems from natural language input. Code: https://github.com/google-deepmind/alphageometry2.
Yuri Chervonyi, Trieu H. Trinh, Miroslav Olsák, Marcelo Menegali, Junehyuk Jung, Vikas Verma, Quoc V. Le, Thang Luong
J. Mach. Learn. Res.6