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Our work on how LLMs store relations selected as NeurIPS Spotlight paper
Our paper The Structure of Relation Decoding Linear Operators in Large Language Models by Miranda Anna Christ, Adrián Csiszárik, Gergely Becsó, and Dániel Varga was accepted at the NeurIPS 2025 conference as a Spotlight paper (~3% of submissions).
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Healthcare

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Patient Pathways

Patient Pathway Mission
Leveraging Hungary’s healthcare data assets for prevention, prediction, and decision support.
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The table
All Blog posts Publications Events Grants Announcements

Development of a Real-Time, Autonomous Energy Information and Production Management System

Grant
October 1, 2021
grants
December 7, 2021 - Project Launch Press Release The energy industry has undergone significant changes over the past decade. The main drivers of these changes have been the strengthening of climate awareness, the importance of energy supply security, and the intertwining and connecting development of information technology across industries. The world of energy production is gradually shifting from traditional, centralized, large energy production systems towards decentralized production and distribution. Renewable-based power plants, largely weather-dependent (typically wind and solar power plants), are increasingly present in the world’s energy mix.
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October 1, 2021

The Role of Entropy in Guiding a Connection Prover

Publication
September 6, 2021
publications
In this work we study how to learn good algorithms for selecting reasoning steps in theorem proving. We explore this in the connection tableau calculus implemented by leanCoP where the partial tableau provides a clean and compact notion of a state to which a limited number of inferences can be applied. We start by incorporating a state-of-the-art learning algorithm — a graph neural network (GNN) – into the plCoP theorem prover.
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September 6, 2021

Towards Finding Longer Proofs

Publication
September 6, 2021
publications
In this work we study how to learn good algorithms for selecting reasoning steps in theorem proving. We explore this in the connection tableau calculus implemented by leanCoP where the partial tableau provides a clean and compact notion of a state to which a limited number of inferences can be applied. We start by incorporating a state-of-the-art learning algorithm — a graph neural network (GNN) – into the plCoP theorem prover.
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September 6, 2021

On measure-preserving Fp-omega-systems of order k

Publication
September 5, 2021
publications
Building on previous work in the nilspace-theoretic approach to the study of Host–Kra factors of measure-preserving systems, we prove that every ergodic $\mathbb{F}_p^\omega$-system of order $k$ is a factor of an Abramov $\mathbb{F}_p^\omega$-system of order $k$. This answers a question of Jamneshan, Shalom and Tao.
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September 5, 2021
The Team
The AI group at the institute brings together experts with backgrounds in both industry and academia. We place equal emphasis on theoretical foundations, thorough experimentation, and practical applications. Our close collaboration ensures a continuous exchange of knowledge between scientific research and applied projects.
All Theory Applications Former members
Balázs Szegedy
Mathematical Theory
Attila Börcs, PhD
NLP, Modeling, MLOps
Adrián Csiszárik
Representation Learning, Foundations
Győző Csóka
NLP, MLOps
Domonkos Czifra
NLP, Foundations
Botond Forrai
Modeling
Péter Kőrösi-Szabó
Modeling
Gábor Kovács
NLP, Modeling
Judit Laki, MD PhD
Healthcare
Márton Muntag
Time Series, NLP, Modeling
Dávid Terjék
Generalization, Mathematical Theory
Dániel Varga
Foundations, Computer aided proofs
Pál Zsámboki
Reinforcement Learning, Geometric Deep Learning
Zsolt Zombori
Formal Reasoning
Péter Ágoston
Combinatory, Geometry
Beatrix Mária Benkő
Representation Learning
Jakab Buda
NLP
Diego González Sánchez
Generalization, Mathematical Theory
Melinda F. Kiss
Representation Learning
Ákos Matszangosz
Topology, Foundations
Alex Olár
Foundations
Gergely Papp
Modeling
Open Positions
The Rényi AI group is actively recruiting both theorists and practitioners.
Machine Learning Engineer
Announcement: December 1, 2023
Deadline: rolling
applications
Rényi Institute is seeking Machine Learning Engineers to join our AI Research & Development team. Preferred Qualifications: • MLOps experience (especially in cloud environments) • Industry experience working on ML solutions
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Research Scientist
Announcement: December 1, 2023
Deadline: rolling
theory, applications
Rényi Institute is seeking Research Scientists to join our AI Research & Development team. You will have the privilege to work at a renowned academic institute and do what you love: do research and publish in the field of machine learning / deep learning.
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All Positions
Rényi AI - Building bridges between mathematics and artificial intelligence.