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Oleg Rogov

4 accepted papers

2026

I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

AAAI 2026technical

Recent LLMs like DeepSeek-R1 have demonstrated state-of-the-art performance by integrating deep thinking and complex reasoning during generation. However, the internal mechanisms behind these reasoning processes remain unexplored. We observe reasoning LLMs consistently use vocabulary associated with

Cited by 0SourcePDFScholar
2026

Speech-to-LaTeX: New Models and Datasets for Converting Spoken Equations and Sentences

ICLR 2026poster

Conversion of spoken mathematical expressions is a challenging task that involves transcribing speech into a strictly structured symbolic representation while addressing the ambiguity inherent in the pronunciation of equations. Although significant progress has been achieved in automatic speech reco…

Cited by 0SourcecodeScholar
2025

CLEAR: Character Unlearning in Textual and Visual Modalities

ACL 2025finding

Machine Unlearning (MU) is critical for removing private or hazardous information from deep learning models. While MU has advanced significantly in unimodal (text or vision) settings, multimodal unlearning (MMU) remains underexplored due to the lack of open benchmarks for evaluating cross-modal data…

Cited by 0SourcePDFScholar
2024

Probabilistically Robust Watermarking of Neural Networks

IJCAI 2024poster

As deep learning (DL) models are widely and effectively used in Machine Learning as a Service (MLaaS) platforms, there is a rapidly growing interest in DL watermarking techniques that can be used to confirm the ownership of a particular model. Unfortunately, these methods usually produce watermarks…

Cited by 4SourcePDFScholar