Representation Engineering · AI Monitoring
Maciej Chrabąszcz
How to say my name
Maciek MA‑check
- MA “ma” as in mama.
- checkexactly like the English word check; the “ch” is soft, as in cheese.
Maciej (MA‑chey) is the formal form you will see on my papers. Maciek is the everyday one, and the one I prefer.
Chrabąszcz hra‑BONSH‑ch
- hra a breathy “h”, as in loch, straight into “ra” as in the sun god Ra.
- BONSH “bon” as in James Bond, then “sh” as in shoe.
- ch as in church.
The “szcz” ending is just “sh” + “ch”, like the middle of fresh cheese.
Fun fact: Chrabąszcz means “May beetle” in Polish. 🪲
I study the hidden representations of generative models: how to read them, steer them and monitor them, and how that can help make these systems safer.
PhD candidate, Warsaw University of Technology
AI Safety Researcher, NASK National Research Institute
About
I am a PhD candidate at Warsaw University of Technology and an AI Safety researcher at NASK National Research Institute. My thesis, supervised by Prof. Tomasz Trzciński and Dr. Sebastian Cygert, is on efficient machine-learning methods for the safety of AI models.
At NASK I co-led the safety and evaluation work for PLLuM, the Polish government's family of Polish large language models. I hold an MSc in Mathematical Statistics and Data Analysis from WUT.
Research interests
I'm interested in AI safety in general. My work currently centres on white-box methods for AI monitoring and steering: using a model's internal activations to build cheap guardrails, track its reasoning, or change its behaviour. I also work on safety evaluation of LLMs.
Publications
Google Scholar2026
Conditioned Activation Transport for T2I Safety Steering
Efficient LLM Moderation with Multi-Layer Latent Prototypes
An earlier version appeared at the ICLR 2025 Workshop on Building Trust in LLMs.
Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics
An earlier version appeared at the ICML 2026 Mechanistic Interpretability Workshop.
Safety of Large Language Models Beyond English: A Systematic Literature Review of Risks, Biases, and Safeguards
2025
Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
PLLuM-Align: Polish Preference Dataset for Large Language Model Alignment
The PLLuM Instruction Corpus
PLLuM: A Family of Polish Large Language Models
Rainbow-Teaming for the Polish Language: A Reproducibility Study
Aggregated Attributions for Explanatory Analysis of 3D Segmentation Models
2024
2023
Get in touch
Happy to talk about AI safety, representation engineering or possible collaborations. Email is the best way to reach me.