3 citations · 5 across the 10 of their papers we have counts for
12 papers
Not All Proofs Are Equal: Evaluating LLM Proof Quality Beyond Correctness
Ivo Petrov, Jasper Dekoninck, Dimitar I. Dimitrov +1
Large language models (LLMs) have become capable mathematical problem-solvers, often producing correct proofs for challenging problems. However, correctness alone is not sufficient…
TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization
William Kalikman, Ivo Petrov, Dimitar I. Dimitrov +1
Federated learning allows multiple clients to jointly train a shared model by sending gradient updates to a central server while keeping raw inputs local. However, prior gradient i…
SABER-Math: Automated Benchmark for Information Retrieval Evaluation in Mathematics
Nikolay Georgiev, Maria Drencheva, Kseniia Ibragimova +3
As agentic AI systems tackle more complex mathematical tasks, they increasingly rely on information retrieval (IR) to search problem databases, theorem libraries, and educational r…
Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots
Mark Vero, Fabian Kaczmarczyck, Ivan Petrov +6
Honeypots are decoy systems mimicking real system components designed to defend against cyber attacks. Recently, LLMs increasingly serve as simulation backbones for honeypots. They…
Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs
Jasper Dekoninck, Nikola Jovanović, Tim Gehrunger +4
Large language models (LLMs) are becoming increasingly capable mathematical collaborators, but static benchmarks are no longer sufficient for evaluating progress: they are often na…
BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMs
Ivo Petrov, Jasper Dekoninck, Martin Vechev
Large language models (LLMs) have recently shown strong performance on mathematical benchmarks. At the same time, they are prone to hallucination and sycophancy, often providing co…
The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical Proofs
Jasper Dekoninck, Ivo Petrov, Kristian Minchev +13
In recent months, large language models (LLMs) have made significant progress in mathematical proof generation, but further advancement is hindered by the lack of a large-scale, hi…
MathArena: Evaluating LLMs on Uncontaminated Math Competitions
Mislav Balunović, Jasper Dekoninck, Ivo Petrov +2
The rapid advancement of reasoning capabilities in large language models (LLMs) has led to notable improvements on mathematical benchmarks. However, many of the most commonly used…
GRAIN: Exact Graph Reconstruction from Gradients
Maria Drencheva, Ivo Petrov, Maximilian Baader +2
Federated learning claims to enable collaborative model training among multiple clients with data privacy by transmitting gradient updates instead of the actual client data. Howeve…
Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad
Ivo Petrov, Jasper Dekoninck, Lyuben Baltadzhiev +5
Recent math benchmarks for large language models (LLMs) such as MathArena indicate that state-of-the-art reasoning models achieve impressive performance on mathematical competition…
MathConstruct: Challenging LLM Reasoning with Constructive Proofs
Mislav Balunović, Jasper Dekoninck, Nikola Jovanović +2
While Large Language Models (LLMs) demonstrate impressive performance in mathematics, existing math benchmarks come with significant limitations. Many focus on problems with fixed…
DAGER: Exact Gradient Inversion for Large Language Models
Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader +2
Federated learning works by aggregating locally computed gradients from multiple clients, thus enabling collaborative training without sharing private client data. However, prior w…