Benchmark Results and Use Cases

The benchmark workbook makes the tradeoff explicit. On the five selected LogHub datasets, native codecs remain the right answer for self-contained compression: the best native archive ratios range from about 0.025 on Zookeeper to about 0.047 on Linux and Proxifier, while the current full-log AIXC LZP profile lands between 0.050 and 0.109. It completes the lossless runs, but it is slower than native zstd/xz and usually larger when no shared model is allowed.

AIXC earns its best ratios when the predictor is shared out of band. With corpus-specific max-context byte models, the order-64 archive-only ratios beat the best native archive ratio on all five selected logs: Linux reaches 0.012, Zookeeper 0.018, Apache 0.020, Mac 0.022, and Proxifier 0.022. Higher orders can drive some archive-only ratios below 0.001, but the model bytes dominate if each archive has to carry its own model.

The format fits a specific class of systems: repeated, related text streams where the predictor can be installed once and amortized across many archives. Log families, telemetry exports, generated reports, protocol traces, or corpora distributed with a known sidecar model fit the design better than one-off files. For arbitrary standalone compression, a mature native codec is still the practical choice.

• Self-contained logs: native zstd/xz remains smaller and much faster in the current benchmark set.
• Shared-model logs: AIXC order-64 trained-byte archives reach roughly 1.2%-2.2% of original size on the selected LogHub files.
• Shakespeare max-context experiments show the upper bound of the idea: an order-64 shared model produced a 3,760-byte archive, about 0.0007 of the original text, with an 11.3 MB sidecar model.
• Best fit: many related archives decoded in an environment that can already provide the predictor manifest and sidecar model.