Adaptive variable-to-variable length codes
Why this work is in the frame
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Bibliographic record
Abstract
In the last several years, adaptive codes for fixed-to-variable length and variable-to-fixed length codes have been described. This paper examines two methods for implementing adaptive variable-to-variable length codes, which have not been considered before due to the difficulty of designing optimum variable-to-variable length codes. The two adaptive methods are based on dual-tree codes, where a source tree parses the input sequence into source words and a code tree assigns each source word a code word. One adaptive method uses a single dual-tree code, and uses an algorithm which requires a complex logic circuit to adjust the shape of the source and code trees. The second method, called state-tree codes, uses a fixed pool of dual-tree codes and a state machine to select which dual-tree code is used. State-tree codes require more memory than the first method, but only a trivial logic circuit is needed to implement the codes, which will result in a very fast circuit.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.001 | 0.001 |
| 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