collaborators

5 papers

cs.AI2026

PRAIB: Peer Review AI Benchmark of Behaviour of LLM-Assisted Reviewing

Krzysztof Żurawicki, Julia Farganus, Arkadiusz Gaweł +2

The growing number of submitted papers has motivated the exploration of Large Language Models (LLMs) as a means to support and augment the peer review process, particularly in term…

cs.CV2026

Learning Representations from 3D Gaussian Splats

Julia Farganus, Krzysztof Żurawicki, Arkadiusz Gaweł +2

3D Gaussian Splatting (3DGS) is a recent approach for scene rendering. Although primarily designed for view synthesis, its potential for scene understanding tasks remains underexpl…

cs.LG2026

V4FinBench: Benchmarking Tabular Foundation Models, LLMs, and Standard Methods on Corporate Bankruptcy Prediction

Marcin Kostrzewa, Sebastian Tomczak, Roman Furman +5

Corporate bankruptcy prediction is a high-stakes financial task characterized by severe class imbalance and multi-horizon forecasting demands. Public datasets supporting it remain…

cs.GR2026

FaceParts: Segmentation and Editing of Gaussian Splatting

Tymoteusz Zapała, Julia Farganus, Dominik Galus +3

Facial editing is an important task with applications in entertainment, virtual reality, and digital avatars. Most existing approaches rely on generative models in the 2D image dom…

cs.CV2026

XSPLAIN: XAI-enabling Splat-based Prototype Learning for Attribute-aware INterpretability

Dominik Galus, Julia Farganus, Tymoteusz Zapala +4

3D Gaussian Splatting (3DGS) has rapidly become a standard for high-fidelity 3D reconstruction, yet its adoption in multiple critical domains is hindered by the lack of interpretab…