🤖 AI Summary
This study addresses the optimization of general-purpose lossless compression under realistic resource constraints—specifically, memory usage capped at 8 GB and decompressor size limited to 1 MB—by organizing an international challenge based on a public training set and a hidden test set comprising 16 heterogeneous files. Performance is evaluated multidimensionally using compression ratio, compression/decompression time, Weissman score, and Pareto front analysis. The generalization capability of submissions is further assessed on external large-scale datasets, while Normalized Compression Distance (NCD) is employed to analyze inter-solution relationships. Among 117 valid submissions, several outperformed mainstream tools on external data, underscoring the critical role of advanced probabilistic modeling and effective multi-objective trade-offs in enhancing compression performance.
📝 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 https://aitdcc.github.io.