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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Patient Pathway Mission

Leveraging Hungary’s healthcare data assets for prevention, prediction, and decision support.

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All Blog posts Publications Events Grants Announcements

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.

September 5, 2021

Ordering Subgoals in a Backward Chaining Prover

Publication

September 5, 2021

publications

Many automated theorem provers are based on backward chaining: reasoning starts from a goal statement which we aim to prove and each inference step reduces one of the goals to a (possibly empty) set of new subgoals. We thus maintain a set of open goals that need to be proven and the proof is complete once the open goal set becomes empty. For each goal, there can be several valid inferences, resulting in different successor goal sets and selecting the right inference constitutes the core problem of such theorem provers which has been thoroughly studied in the past half century.

September 5, 2021

Ordering Subgoals in a Backward Chaining Prover

Publication

September 5, 2021

publications

World models represent the basic mechanisms of a system and can provide predictions about how transformations (actions) affect the state of the system. Such models have recently gained attention in Reinforcement Learning (RL) and in several domains model based learning systems performed similarly or better than highly tuned model free variants [1, 8, 12]. World models can increase sample efficiency since trajectories can be generated without interacting with the environment, and they can aid exploration by yielding a semantically meaningful latent structure that allows for identifying promising directions.

September 5, 2021

A Refinement of Cauchy-Schwarz Complexity, with Applications

Publication

August 24, 2021

publications

We introduce a notion of complexity for systems of linear forms called \emph{sequential Cauchy-Schwarz complexity}, which is parametrized by two positive integers $k,\ell$ and refines the notion of Cauchy-Schwarz complexity introduced by Green and Tao. We prove that if a system of linear forms has sequential Cauchy-Schwarz complexity at most $(k,\ell)$ then any average of 1-bounded functions over this system is controlled by the $2^{1-\ell}$-th power of the Gowers $U^{k+1}$-norms of the functions.

August 24, 2021

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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: July 1, 2025

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

Research Scientist

Announcement: July 1, 2025

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.

All Positions

Rényi AI - Building bridges between mathematics and artificial intelligence.