9 papers
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…
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…
LARP: Learner-Agnostic Robust Data Prefiltering
Kristian Minchev, Dimitar I. Dimitrov, Nikola Konstantinov
Public datasets, crucial for modern machine learning and statistical inference, often contain low-quality or contaminated samples that can harm model performance. This creates a ne…
SPEAR++: Scaling Gradient Inversion via Sparsely-Used Dictionary Learning
Alexander Bakarsky, Dimitar I. Dimitrov, Maximilian Baader +1
Federated Learning has seen an increased deployment in real-world scenarios recently, as it enables the distributed training of machine learning models without explicit data sharin…
MixAT: Combining Continuous and Discrete Adversarial Training for LLMs
Csaba Dékány, Stefan Balauca, Robin Staab +2
Despite recent efforts in Large Language Model (LLM) safety and alignment, current adversarial attacks on frontier LLMs can still consistently force harmful generations. Although a…