Peer-reviewed research in NLP and graph representation learning.
ACL 2026 SRWConference2026
Factual State Discovery Benchmark: Evaluating Fact Elicitation in Polish Tax Law
M. Bystroński, K. Tagowski, D. Janiak, J. Farganus, Ł. Augustyniak, M. Kajdanowicz, T. Kajdanowicz
A benchmark for conversational fact elicitation in Polish tax law: 500 official tax-interpretation narratives decomposed into 32,874 validated atomic facts, evaluated with a discovery-through-dialogue protocol. Even the best model recovers under half the facts on hard cases after 50 turns.
@inproceedings{bystronski2026factual,
title = {Factual State Discovery Benchmark: Evaluating Fact Elicitation in Polish Tax Law},
author = {Bystro\'nski, Mateusz and Tagowski, Kamil and Janiak, Denis and Farganus, Julia and Augustyniak, {\L}ukasz and Kajdanowicz, Monika and Kajdanowicz, Tomasz},
booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop},
year = {2026}
}
AILaw 2026Workshop2026
Bridging AI and Law: A Scalable Multi-Agent Platform for Quantitative Legal Analytics Across Millions of Documents
Ł. Augustyniak, K. Tagowski, A. Szymczak, J. Binkowski, A. Sawczyn, M. Skibiński, D. Janiak, M. Bystroński, G. Piotrowski, M. Bernaczyk, K. Kamiński, T. Kajdanowicz
A production-scale, multi-agent platform bridging AI and legal practice, indexing 3M+ documents and 300M+ semantic vectors, with a Quantitative Legal Agent architecture for aggregation and interpretable analysis across jurisdictions.
@inproceedings{augustyniak2026bridging,
title = {Bridging AI and Law: A Scalable Multi-Agent Platform for Quantitative Legal Analytics Across Millions of Documents},
author = {Augustyniak, {\L}ukasz and Tagowski, Kamil and Szymczak, Adrian and Binkowski, Jakub and Sawczyn, Albert and Skibi\'nski, Micha{\l} and Janiak, Denis and Bystro\'nski, Mateusz and Piotrowski, Grzegorz and Bernaczyk, Micha{\l} and Kami\'nski, Krzysztof and Kajdanowicz, Tomasz},
booktitle = {Bridge between Artificial Intelligence and Law (AILaw)},
year = {2026},
url = {https://openreview.net/forum?id=hWjsyTSWrY}
}
ICCS 2023Conference2023
RAFEN: Regularized Alignment Framework for Embeddings of Nodes
K. Tagowski, P. Bielak, J. Binkowski, T. Kajdanowicz
A regularized framework for aligning node embeddings across snapshots of a dynamic graph, improving the stability of temporal representations.
@inproceedings{tagowski2023rafen,
title = {{RAFEN}: Regularized Alignment Framework for Embeddings of Nodes},
author = {Tagowski, Kamil and Bielak, Piotr and Binkowski, Jakub and Kajdanowicz, Tomasz},
booktitle = {International Conference on Computational Science (ICCS)},
year = {2023},
doi = {10.1007/978-3-031-35995-8_25}
}
NeurIPS 2022Datasets & Benchmarks2022
This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish
Ł. Augustyniak, K. Tagowski, A. Sawczyn, D. Janiak, R. Bartusiak, A. Szymczak, A. Janz, P. Szymański, M. Wątroba, M. Morzy, T. Kajdanowicz, M. Piasecki
A comprehensive, reproducible benchmark for Polish NLP spanning 14 diverse tasks, with a public leaderboard and pre-computed embeddings.
@inproceedings{augustyniak2022lepiszcze,
title = {This is the Way: Designing and Compiling {LEPISZCZE}, a Comprehensive {NLP} Benchmark for {P}olish},
author = {Augustyniak, {\L}ukasz and Tagowski, Kamil and Sawczyn, Albert and Janiak, Denis and Bartusiak, Roman and Szymczak, Adrian and Janz, Arkadiusz and Szyma\'nski, Piotr and W\k{a}troba, Marcin and Morzy, Miko{\l}aj and Kajdanowicz, Tomasz and Piasecki, Maciej},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track},
year = {2022}
}
Knowledge-Based SystemsJournal2022
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
P. Bielak, K. Tagowski, M. Falkiewicz, T. Kajdanowicz, N. V. Chawla
An incremental framework that updates dynamic-network embeddings as the graph evolves, avoiding costly full retraining.
@article{bielak2022fildne,
title = {{FILDNE}: A Framework for Incremental Learning of Dynamic Networks Embeddings},
author = {Bielak, Piotr and Tagowski, Kamil and Falkiewicz, Maciej and Kajdanowicz, Tomasz and Chawla, Nitesh V.},
journal = {Knowledge-Based Systems},
year = {2022},
doi = {10.1016/j.knosys.2021.107453}
}
ICCS 2021Conference2021
Embedding Alignment Methods in Dynamic Networks
K. Tagowski, P. Bielak, T. Kajdanowicz
A study of alignment methods that keep node embeddings comparable across consecutive snapshots of a dynamic network.
@inproceedings{tagowski2021embedding,
title = {Embedding Alignment Methods in Dynamic Networks},
author = {Tagowski, Kamil and Bielak, Piotr and Kajdanowicz, Tomasz},
booktitle = {International Conference on Computational Science (ICCS)},
year = {2021},
doi = {10.1007/978-3-030-77961-0_48}
}
ENIC 2017Conference2017
Incremental Learning in Dynamic Networks for Node Classification
T. Kajdanowicz, K. Tagowski, M. Falkiewicz, P. Kazienko
Early work on incremental node-classification methods for evolving network data.
@inproceedings{kajdanowicz2017incremental,
title = {Incremental Learning in Dynamic Networks for Node Classification},
author = {Kajdanowicz, Tomasz and Tagowski, Kamil and Falkiewicz, Maciej and Kazienko, Przemys{\l}aw},
booktitle = {Proceedings of the ENIC Conference},
year = {2017},
doi = {10.1007/978-3-319-90312-5_9}
}