activity
20232026
most citedParallel Kac's Walk Generates PRU

1 citations · 2 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2026

Think Shallow, Solve Deep: Controlling Recurrent Dynamics for Reliable Test-Time Depth

Ivan Viakhirev, Kirill Borodin, Amirah Almutairi +3

Recurrent-depth reasoners aim to solve harder problems by iterating their update longer at test time, but additional iterations can improve, preserve, or degrade an answer. We show…

quant-ph2025★ 1 cited

Parallel Kac's Walk Generates PRU

Chuhan Lu, Minglong Qin, Fang Song +2

Ma and Huang recently proved that the PFC construction, introduced by Metger, Poremba, Sinha and Yuen [MPSY24], gives an adaptive-secure pseudorandom unitary family PRU. Their proo…

cs.CL2025

Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders

Kristian Kuznetsov, Laida Kushnareva, Polina Druzhinina +5

Artificial Text Detection (ATD) is becoming increasingly important with the rise of advanced Large Language Models (LLMs). Despite numerous efforts, no single algorithm performs co…

cs.CL2025

Quantifying Logical Consistency in Transformers via Query-Key Alignment

Eduard Tulchinskii, Anastasia Voznyuk, Laida Kushnareva +4

Large language models (LLMs) have demonstrated impressive performance in various natural language processing tasks, yet their ability to perform multi-step logical reasoning remain…

cs.CL2024★ 1 cited

Robust AI-Generated Text Detection by Restricted Embeddings

Kristian Kuznetsov, Eduard Tulchinskii, Laida Kushnareva +4

Growing amount and quality of AI-generated texts makes detecting such content more difficult. In most real-world scenarios, the domain (style and topic) of generated data and the g…

cs.CL2024

Listening to the Wise Few: Query-Key Alignment Unlocks Latent Correct Answers in Large Language Models

Eduard Tulchinskii, Laida Kushnareva, Kristian Kuznetsov +5

Large language models (LLMs) routinely fail to output the correct option in multiple-choice question answering (MCQA) while encoding the answer internally. We expose this latent kn…

cs.CV2024

Improving Interpretability and Robustness for the Detection of AI-Generated Images

Tatiana Gaintseva, Laida Kushnareva, German Magai +5

With growing abilities of generative models, artificial content detection becomes an increasingly important and difficult task. However, all popular approaches to this problem suff…

cs.CL2023

AI-generated text boundary detection with RoFT

Laida Kushnareva, Tatiana Gaintseva, German Magai +6

Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated. Detecting t…