Universal lossless data compression with side information by using a conditional MPM grammar transform
Why this work is in the frame
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Bibliographic record
Abstract
The MPM (multilevel pattern matching) grammar transform underlies a lossless data compression algorithm developed by Kieffer, Yang, Nelson and Cosman (see IEEE Trans. on Inform. Theory, 2000. In this paper, we extend the MPM grammar transform to the case of side information known to both the encoder and decoder, yielding a conditional MPM grammar transform which is referred to as the CMPM (r,I) transform. Based on the CMPM (r,I) transform, we develop a universal lossless data compression algorithm with side information called the CMPM algorithm, which has linear time and storage complexity and asymptotically achieves the conditional entropy rate of any stationary, ergodic source pair. The advantage of using side information, if any, for data compression is obvious; one can considerably reduce the compression rate if the side information is highly correlated with a sequence to be compressed.
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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 | 0.007 |
| Open science | 0.001 | 0.000 |
| 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