7 papers
SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings and Speaks in Tokens
Nikita Dragunov, Temurbek Rahmatullaev, Elizaveta Goncharova +5
The recently proposed Large Concept Model (LCM) generates text by predicting a sequence of sentence-level embeddings and training with either mean-squared error or diffusion object…
Logit-KL Flow Matching: Non-Autoregressive Text Generation via Sampling-Hybrid Inference
Egor Sevriugov, Nikita Dragunov, Anton Razzhigaev +2
Non-autoregressive (NAR) language models offer notable efficiency in text generation by circumventing the sequential bottleneck of autoregressive decoding. However, accurately mode…
MindShift: Analyzing Language Models' Reactions to Psychological Prompts
Anton Vasiliuk, Irina Abdullaeva, Polina Druzhinina +2
Large language models (LLMs) hold the potential to absorb and reflect personality traits and attitudes specified by users. In our study, we investigated this potential using robust…
Real-World Transferable Adversarial Attack on Face-Recognition Systems
Andrey Kaznacheev, Matvey Mikhalchuk, Andrey Kuznetsov +2
Adversarial attacks on face recognition (FR) systems pose a significant security threat, yet most are confined to the digital domain or require white-box access. We introduce GaP (…
Inverting Black-Box Face Recognition Systems via Zero-Order Optimization in Eigenface Space
Anton Razzhigaev, Matvey Mikhalchuk, Klim Kireev +3
Reconstructing facial images from black-box recognition models poses a significant privacy threat. While many methods require access to embeddings, we address the more challenging…
Universal Adversarial Attack on Aligned Multimodal LLMs
Temurbek Rahmatullaev, Polina Druzhinina, Nikita Kurdiukov +3
We propose a universal adversarial attack on multimodal Large Language Models (LLMs) that leverages a single optimized image to override alignment safeguards across diverse queries…