Michał Tyrolski

Michał Tyrolski

Senior AI Consultant at EY / Independent Researcher.

Publications

You can also find my articles on my Google Scholar profile.

Publications

Resolution of Recursive Data Corruption to Transform T-cell Epitope Discovery

Published in bioRxiv 2026, 2026

bioRxiv preprint on data contamination in T-cell epitope discovery; reframes MHC-I prediction as protein-centric learning to rank.

Recommended citation: Preibisch, G., Tyrolski, M., Kucharski, P., Giziński, S., Grzegorczyk, P., Moon, S., Kim, S., Zaro, B., & Gambin, A. (2026). Resolution of recursive data corruption to transform T-cell epitope discovery. bioRxiv, 2026.03.30.710191. https://doi.org/10.64898/2026.03.30.710191
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OpenGVL: Benchmarking Visual Temporal Progress for Data Curation

Published in CoRL 2025 workshop, 2025

Benchmark, toolkit, and live leaderboard for evaluating VLM temporal progress estimation in robotics videos; supports automated dataset curation.

Recommended citation: Budzianowski, P., Wiśnios, E., Tyrolski, M., Góral, G., Kulakov, I., Petrenko, V., & Walas, K. (2025). OpenGVL--Benchmarking Visual Temporal Progress for Data Curation. arXiv preprint arXiv:2509.17321.
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What Matters in Hierarchical Search for Combinatorial Reasoning Problems?

Published in ICLR 2024 (Generative Models for Decision Making), 2024

Empirical study of when hierarchical search helps in combinatorial reasoning, with evaluation guidelines for comparing methods.

Recommended citation: Zawalski, M., Góral, G., Tyrolski, M., Wiśnios, E., Budrowski, F., Cygan, M., Kuciński, Ł., & Miłoś, P. (2024). What matters in hierarchical search for combinatorial reasoning problems? arXiv preprint arXiv:2406.03361.
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Adjusting Planning Horizon with Adaptive Subgoal Search

Published in ICLR 2023 (Top-5%, Oral), 2023

AdaSubS adjusts planning horizon using generated subgoals and reachability filtering; evaluated on Sokoban, Rubik’s Cube, and INT.

Recommended citation: Zawalski, M., Tyrolski, M., Czechowski, K., Odrzygóźdź, T., Stachura, D., Piękos, P., Wu, Y., Kuciński, Ł., & Miłoś, P. (2022). Fast and precise: Adjusting planning horizon with adaptive subgoal search. arXiv preprint arXiv:2206.00702.
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Hierarchical Transformers Are More Efficient Language Models

Published in NAACL 2022, 2022

Hourglass adds downsampling and upsampling to Transformers for more efficient long-sequence modeling; evaluated on ImageNet32 and enwik8.

Recommended citation: Nawrot, P., Tworkowski, S., Tyrolski, M., Kaiser, Ł., Wu, Y., Szegedy, C., & Michalewski, H. (2022). Hierarchical transformers are more efficient language models. Findings of the Association for Computational Linguistics: NAACL 2022, 1559-1571.
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