Fast Data Compression with Antidictionaries
Why this work is in the frame
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Bibliographic record
Abstract
We consider the data compression using antidictionaries and give algorithms for faster compression and decompression. While the original method of Crochemore et al. uses finite transducers with e-moves, we (de)compress using e-free transducers. This is provably faster, assuming data non-negligibly compressible, but we have to consider the overhead due to building the new ma-chines. In general, they can be quadratic in size compared to the ones allowing e-moves; we prove this bound optimal as it is reached for de Bruijn words. However, in practice, the size of the e-free machines turns out to be close to the size of the ones allowing e-moves and therefore we can achieve significantly faster (de)compression. We show our results for the files in Calgary corpus.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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