Oliver Weissl Senior Data Scientist & PhD Candidate

// research

Testing deep learning systems through the latent spaces of generative models.

11 papers 7 peer-reviewed 1 open-source project

Original Original
Fine layers Fine layers
Medium layers Medium layers
Coarse layers Coarse layers
Target Target
Style mixing across StyleGAN layers, car → truck. From Mimicry.
GitHub project

🌱 Context Garden: Trimming the Weeds from Your Agent's Context

Oliver Weißl

I started a new open-source project: Context Garden, a collection of self-contained Agent Skills that cut an agent’s context usage. The skills are deterministic, run fully offline, and make no LLM calls inside the tooling itself. They ar...

arXiv preprint

Generative Testing of Automated Speech Recognition Systems

Yanis Xabier Wilbrand Peña, Oliver Weißl, Andrea Stocco

In this work, we bring generative test input generation to the audio domain. Instead of perturbing waveforms directly, GATAS operates in the phoneme-level latent space of a text-to-speech model, producing failure-inducing inputs for auto...

DeepTest @ ICSE 2026 workshop paper

Latent Regularization in Generative Test Input Generation

Giorgi Merabishvili, Oliver Weißl, Andrea Stocco

[Update 12.04.2026] Our paper is now published in the Proceedings of the 7th IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest ‘26). The preprint remains available on arXiv.

arXiv preprint

Feature-Aware Test Generation for Deep Learning Models

Xingcheng Chen, Oliver Weißl, Andrea Stocco

In this work, we examine how models behave under isolated semantic changes by directly manipulating individual features in StyleGAN’s S-space, instead of entangled Z or W representations. This enables precise, feature-level testing and h...

arXiv preprint

HyperNet-Adaptation for Diffusion-Based Test Case Generation

Oliver Weißl, Vincenzo Riccio, Severin Kacianka, Andrea Stocco

This paper introduces a novel Diffusion based testing approach for deep learning systems, combining SUT feedback with weight adaptations in HyperNets. Unlike prior methods, HyNeA does not rely on curated datasets, making it more flexible...

ACM TOSEM journal paper

Targeted Deep Learning System Boundary Testing

Oliver Weißl, Amr Abdellatif, Xingcheng Chen, Giorgi Merabishvili, Vincenzo Riccio, Severin Kacianka, Andrea Stocco

[Update 28.09.2025] I am happy to announce, that our paper was accepted for publication in TOSEM. [Original 11.05.2025] This paper introduces a novel boundary testing approach for deep learning systems, combining SUT feedback with contr...

GECCO 2025 conference paper

Fertility During Learning In Evolutionary Robot Systems

Jacopo Michele Di Matteo, Oliver Weißl, A.E. Eiben

I’m excited to share that the paper “Fertility During Learning In Evolutionary Robot Systems” lead by Jacopo Michele Di Matteo and co-authored by Prof. Dr. Guszti Eiben and myself has been accepted at GECCO 2025, one of the top ranking c...

IEEE QCNC 2025 conference paperthesis

An Equivariant Machine Learning Decoder for 3D Toric Codes

Oliver Weißl, Evgenii Egorov

[Update 20.01.2025] I am happy to announce that this work has been accepted to QCNC 2025 as a short paper. The abstract in this post is now adjusted to the Conference Paper. For the previous version look at the arXiv paper.

ALife 2024 Workshop workshop paper

Interactive embodied evolution for socially adept Artificial General Creatures

Kevin Godin-Dubois, Oliver Weissl, Karine Miras, Anna V. Kononova

I worked with Dr. Kevin Godin-Dubois on realising a concept towards ‘Artificial General Creatures’. This work was presented as part of the Evolution of Things workshop at ALife2024. My main contributions were to implement hardware access...

IEEE SSCI 2023 conference paperthesis

Morphological-Novelty in Modular Robot Evolution

Oliver Weissl, A.E. Eiben

I`m happy to announce that my first paper Morphological-Novelty in Modular Robot Evolution, has been accepted for presentation at the IEEE SSCI Conference 2023 in Mexico-City. This paper was based on my BSc Thesis, supervised by Prof. Dr...