activity
20242026
most citedOmniFusion Technical Report

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI2026

NoReGeo: Non-Reasoning Geometry Benchmark

Irina Abdullaeva, Anton Vasiliuk, Elizaveta Goncharova +4

We present NoReGeo, a novel benchmark designed to evaluate the intrinsic geometric understanding of large language models (LLMs) without relying on reasoning or algebraic computati…

cs.CL2025

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…

cs.CV2025

MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume for Enhanced Video Understanding

Pengyi Li, Irina Abdullaeva, Alexander Gambashidze +2

Modern Video Large Language Models (VLLMs) often rely on uniform frame sampling for video understanding, but this approach frequently fails to capture critical information due to f…

cs.CL2024

ESQA: Event Sequences Question Answering

Irina Abdullaeva, Andrei Filatov, Mikhail Orlov +6

Event sequences (ESs) arise in many practical domains including finance, retail, social networks, and healthcare. In the context of machine learning, event sequences can be seen as…

cs.CV2024★ 2 cited

OmniFusion Technical Report

Elizaveta Goncharova, Anton Razzhigaev, Matvey Mikhalchuk +6

Last year, multimodal architectures served up a revolution in AI-based approaches and solutions, extending the capabilities of large language models (LLM). We propose an \textit{Om…