Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform-part two: with context models
Why this work is in the frame
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Bibliographic record
Abstract
For pt. I see ibid., vol.46, p.755-88 (2000). The concept of context-free grammar (CFG)-based coding is extended to the case of countable-context models, yielding context-dependent grammar (CDG)-based coding. Given a countable-context model, a greedy CDG transform is proposed. Based on this greedy CDG transform, two universal lossless data compression algorithms, an improved sequential context-dependent algorithm and a hierarchical context-dependent algorithm, are then developed. It is shown that these algorithms are all universal in the sense that they can achieve asymptotically the entropy rate of any stationary, ergodic source with a finite alphabet. Moreover, it is proved that these algorithms' worst case redundancies among all individual sequences of length n from a finite alphabet are upper-bounded by d log log n/log n, as long as the number of distinct contexts grows with the sequence length n in the order of O(n/sup a/), where 0 < /spl alpha/ < 1 and d are positive constants. It is further shown that for some nonstationary sources, the proposed context-dependent algorithms can achieve better expected redundancies than any existing CFG-based codes, including the Lempel-Ziv (1978) algorithm, the multilevel pattern matching algorithm, and the context-free algorithms in Part I of this series of papers.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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