Using synchronization bits to boost compression by substring enumeration
Why this work is in the frame
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Bibliographic record
Abstract
A new lossless data compression technique called compression via substring enumeration (CSE) has recently been introduced. It has been observed that CSE achieves lower performance on binary data. An hypothesis has been formulated that suggests that CSE loses track of the position of the bits relative to the byte boundaries more easily in binary data and that this confusion incurs a penalty for CSE. This paper questions the validity of the hypothesis and proposes a simple technique to reduce the penalty, in case the hypothesis is correct. The technique consists in adding a preprocessing step that inserts synchronization bits in the data in order to boost the performance of CSE. Experiments provide strong evidence that the formulated hypothesis is true and they demonstrate the effectiveness of the use of synchronization bits.
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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 | 0.001 |
| Open science | 0.000 | 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