[2606.17712] The 2026 Algorithmic Information Theory Data Compression Challenge

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Computer Science > Information Theory

arXiv:2606.17712 (cs)

[Submitted on 16 Jun 2026]

Title:The 2026 Algorithmic Information Theory Data Compression Challenge

Authors:André Ribeiro, Rúben Garrido, Violeta Ramos, António Alberto, Diogo Fernandes, João Varela, Eduardo Lopes, Rodrigo Abreu, Hugo Ribeiro, Tomás Brás, David Pelicano, Afonso Ferreira, Sebastião Teixeira, Maria Linhares, Martim Santos, Rui Machado, Duarte Santos, Gabriel Silva, Guilherme Rosa, João Roldão, Henrique Teixeira, Cláudia Seabra, Ricardo Fonseca, Richard Miranda, Hugo Castro, Ângela Ribeiro, Fouad Bellili, Luís Diogo, André Cardoso, Armando J. Pinho, Diogo Pratas<br>View a PDF of the paper titled The 2026 Algorithmic Information Theory Data Compression Challenge, by Andr\'e Ribeiro and 29 other authors

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Abstract:Lossless data compression remains central to computer science, with direct impact on storage, communication bandwidth, computational cost, and energy consumption. It is also closely related to Algorithmic Information Theory, where compressibility provides an operational measure of structure and non-randomness. This paper presents the 2026 Algorithmic Information Theory Data Compression Challenge, a benchmark for evaluating general-purpose lossless compressors under realistic constraints. Submissions were encouraged to use arithmetic or range coding, limited to at most 8 GB of memory, and required to include a decompressor no larger than 1 MB. The benchmark comprised sixteen heterogeneous files, split into public training and hidden testing datasets. In total, 117 valid submitted compressors were evaluated alongside established reference compressors using compression ratio, compression and decompression time, Weissman score, and Pareto-frontier analysis. The results show that performance depends strongly on the optimization criterion: fast compressors achieved the best speed-oriented scores, whereas modelling-intensive compressors produced smaller outputs at higher computational cost. A Normalized Compression Distance analysis further revealed clusters of related submissions and distinguished incremental variants from more independent implementations. Selected submissions were described for their methodological novelty or competitive performance and further tested on four large external datasets, where several achieved competitive or superior results relative to established compressors. Overall, the challenge confirms the importance of probabilistic modelling, hidden testing, and external datasets for assessing compression performance and generalization. Benchmark resources, leaderboard data, binaries, and selected source code are publicly available at this https URL.

Subjects:

Information Theory (cs.IT); Data Structures and Algorithms (cs.DS)

Cite as:<br>arXiv:2606.17712 [cs.IT]

(or<br>arXiv:2606.17712v1 [cs.IT] for this version)

https://doi.org/10.48550/arXiv.2606.17712

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Diogo Pratas [view email]<br>[v1]<br>Tue, 16 Jun 2026 09:22:39 UTC (1,284 KB)

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