Bibliographic record
Abstract
When decoding low-rate and short-length Reed-Muller (RM) codes, the recently proposed projection-aggregation (PA) decoder yields near maximum-likelihood decoding performance. However, the practicability of the PA decoder's implementation is nevertheless negatively impacted by its high computational cost. It has been demonstrated that this decoder is closely connected to the belief propagation (BP) decoder based on the parity-check matrix. Techniques inspired by the layered decoding and the broadcast modification for the BP decoder used by the low-density parity-check codes are proposed in this work. The proposed broadcast modification reduces the computational overhead induced by the proposed layered decoding for the collapsed PA (CPA) decoder. The proposed layered and broadcast-based CPA decoder has negligible degradation in decoding performance, and it produces a 47% reduction in the average complexity at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E_{b}/N_{0}=2.5\text{dP}$</tex>, when decoding RM(8, 3) codes.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".