Pangram Labs released Pangram 4, an AI text classifier that detects whether text was written by humans or AI systems. The model achieves 99.16% accuracy and can now identify mixed human-AI writing and fine-grained edits better than its predecessor.

• AUROC of 0.9916 with 0.0041% false positive rate and 0.3396% false negative rate
• Can distinguish fine-grained edits and detect interleaved AI assistance in co-authored text
• Shows improved robustness to adversarial attacks and better generalization to out-of-distribution data

This article explores an unconventional method for classifying text by leveraging compression algorithms. The author demonstrates how to concatenate labeled documents, compress them, and use the compressed sizes to predict labels for new texts. While the method shows promise, it is computationally expensive and generally underperforms compared to traditional classifiers.

• Compression-based classification (concatenate labeled texts, measure compressed-size increase) hits a 0.749 macro F1 with gzip on 4 categories of 20 Newsgroups, versus 0.88 for multinomial Naive Bayes.
• lzma pushes accuracy up to 0.897, beating Naive Bayes, but takes 32 minutes versus over 5 minutes for gzip on just 1,353 test cases—far too slow to be practical.
• The technique reframes text classification as an information-theory problem, showing compression algorithms implicitly model word probability distributions.

This article explores how Python 3.14's zstd module enables efficient text classification through incremental compression. It outlines a method where text is classified based on the size of compressed output from different class-specific compressors, demonstrating improved speed and accuracy over traditional methods.

• Python 3.14's zstd module supports incremental compression, letting classifiers update on new data in tens of microseconds instead of recompressing everything from scratch.
• Classification works by feeding a new document to per-class compressors and picking the class whose compressed output size increases least.
• Tunable parameters (window size, compression level, rebuild frequency) let you trade off speed against accuracy for a given use case.
• Benchmarked on 20 Newsgroups, the compression-based classifier shows competitive learning ability while avoiding traditional ML pipeline complexity.