Eduardo Lopes | Searcharxiv

arXiv2026

The 2026 Algorithmic Information Theory Data Compression Challenge

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.

cs.IT↗

arXiv2007

On simulating nondeterministic stochastic activity networks

In this work we deal with a mechanism for process simulation called a NonDeterministic Stochastic Activity Network (NDSAN). An NDSAN consists basically of a set of activities along with precedence relations involving these activities, which determine their order of execution. Activity durations are stochastic, given by continuous, nonnegative random variables. The nondeterministic behavior of an NDSAN is based on two additional possibilities: (i) by associating choice probabilities with groups of activities, some branches of execution may not be taken; (ii) by allowing iterated executions of groups of activities according to predetermined probabilities, the number of times an activity must be executed is not determined a priori. These properties lead to a rich variety of activity networks, capable of modeling many real situations in process engineering, project design, and troubleshooting. We describe a recursive simulation algorithm for NDSANs, whose repeated execution produces a close approximation to the probability distribution of the completion time of the entire network. We also report on real-world case studies.

cs.DM↗