3 papers
cs.CL2026
GENEB: Why Genomic Models Are Hard to Compare
Daria Ledneva, Mikhail Nuridinov, Denis Kuznetsov
Progress in genomic foundation models is difficult to assess due to fragmented benchmarks, incompatible evaluation protocols, and task-specific reporting. As a result, claims of su…
cs.CL2026
LDARNet: DNA Adaptive Representation Network with Learnable Tokenization for Genomic Modeling
Daria Ledneva, Denis Kuznetsov
Genomic foundation models increasingly adopt large language model architectures, yet almost universally rely on fixed tokenization schemes such as -mers, BPE, or single nucleoti…
cs.CL2025
AutoIntent: AutoML for Text Classification
Ilya Alekseev, Roman Solomatin, Darina Rustamova +1
AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, c…