65 papers
EvoMem: Memory-Augmented Evolution for Code Optimization
Viktor Volkov, Valentin Khrulkov, Andrey V. Galichin +8
Successful mutation strategies in evolutionary code search may contain reusable knowledge that is useful beyond a single run, and in some cases may transfer across related tasks an…
Spectral-Informed Neural Networks Outperform Spectral Methods in High-dimensional PDEs
Tianchi Yu, Ivan Oseledets
The paper introduces Modified Spectral-Informed Neural Networks (SINNs) that combine spectral methods with physics-informed neural networks, using coefficient decay scaling and bas…
Search, Fail, Recover: A Training Framework for Correction-Aware Reasoning
Dmitry Beresnev, Vladimir Makharev, Roman Khalikov +2
Many reasoning tasks are not well described by a single left-to-right chain: a solver may need to pursue a plausible branch, observe delayed failure, and return to the latest prefi…
ImprovEvolve: Basin-Hopping Meets LLM-Guided Evolutionary Search
Alexey Kravatskiy, Valentin Khrulkov, Ivan Oseledets
LLM-guided evolutionary computation, most notably AlphaEvolve, has been remarkably successful in discovering novel mathematical constructions by solving challenging optimization pr…
Ky Fan Norms and Beyond: Dual Norms and Combinations for Matrix Optimization
Alexey Kravatskiy, Ivan Kozyrev, Nikolai Kozlov +3
In this article, we explore the use of various matrix norms for optimizing functions of weight matrices, a crucial problem in deep learning. Moving beyond the spectral norm that un…
TheoremBench: Evaluating LLMs on Theorem Proving in Formal Mathematics
QuocViet Pham, Elvir Karimov, Andrey Galichin +1
LLMs have recently achieved strong results on formal proving benchmarks. However, existing evaluations remain heavily concentrated on competition-style problems and often fail to c…