Grammatical Ziv-Lempel Compression: Achieving PPM-Class Text Compression Ratios with LZ-Class Decompression Speed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary form only given: GLZA is a free, open-source, enhanced grammar-based compressor that constructs a low entropy grammar amenable to entropy coding, using a greedy hill-climbing search guided by estimates of encoded string lengths; the estimates are efficiently computed incrementally during (parallelized) suffix tree construction in a batched iterative repeat replacement cycle. The grammar-coded symbol stream is further compressed by order-1 Markov modeling of trailing/leading subsymbols and selective recency modeling, MTF-coding only symbols that tend to recur soon. This combination results in excellent compression ratios-similar to PPMC's for small files, averaging within about five percent of PPMd's for large text files (1 MB - 10 MB)-with fast decompression on one core or two. Compression time and memory use are not dramatically higher than for similarly high-performance asymmetrical compressors of other kinds. GLZA is on the Pareto frontier for text compression ratio and decompression speed on a variety of benchmarks (LTCB, Calgary, Canterbury, Large Canterbury, Silesia, Maximum Compression, World Compression Challenge), compressing better and/or decompressing faster than its competitors (PPM, LZ77-Markov, BWT, etc.), with better compression ratios than previous grammar-based compressors such as RePair, Sequitur, Offline 3 (Greedy), Sequential/grzip, and IRR-S.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it