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Germany.

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Preprint Sep 2026

Subspace Controllability in Variational Quantum Circuits: Maximising Expressivity and Increasing Search Efficiency with Dynamical Lie Algebras

Variational hybrid quantum models are a common paradigm for realising machine learning on quantum hardware. Nonetheless, they are challenging to train due to several problems intrinsic to the loss landscapes formed by variational quantum circuits. To mitigate these issues, researchers have extended the idea of subspace...

A. R. Barlow, Hans-Martin Rieser, Markus Lange Institute for AI Safety et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Sharing standardized image-derived data in computational pathology using DICOM

Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into collecting, centralizing, and sharing pathology imaging data. In contrast, sharing of image-derived data such as region-of-interest delineatio...

Daniela P. Schacherer, Christopher P. Bridge, D. Clunie et al. · 0 citations

#machine learning Preprint Sep 2026

Quantifying the Value of Privileged Information Using a PAC-Bayesian Approach

An algorithm-agnostic, information-theoretic approach based on the PAC-Bayes framework is introduced that quantifies the potential of privileged features before committing to a model, and takes a necessary step toward an information-theoretic understanding of LUPI.

Vasily Bokov, Sebastian Schmitt, V. Dunjko et al. · 0 citations

#human-computer interacti... Preprint Sep 2026

Grounding GUI Design in Computational Psychology

Creating visually appealing user interfaces often requires extensive manual iteration. We propose an approach that applies answer set programming (ASP) to automatically generate and optimize UI layouts while satisfying design objectives such as grid alignment, grouping, color harmony, and whitespace, along with designe...

Xian-Ni Wang, Javier Romero Davila, Saku Sourulahti et al. · 0 citations

#natural language process... Preprint Aug 2026

Configurable Semantic Chunking for Biomedical Information Extraction in Retrieval-Augmented Generation

Cross-dataset analysis shows that semantic chunking improves extraction datasets with explicit relation cues, such as GM-CIHT and DDI, while fixed chunking remains competitive or stronger for dense biochemical extraction and binary classification settings such as ChemProt and ADE.

Riya Ahuja, Tim Kacprowski, Roya Shiasi Sardoabi Institute of Data Science in Biomedicine et al. · 0 citations

Preprint Aug 2026

PragMatch: Separating Pragmatic Incongruity from Cross-Modal Mismatch in Large Vision-Language Models

The results show that LVLM predictions are sensitive to lexical, OCR-derived and stylistic cues, with injected surface signals causing substantial changes in model predictions despite unchanged underlying image-text relationships.

Zhanna Mukhametsharip, Vera Demberg, Varsha Suresh Saarland University et al. · 0 citations