Ordered Reliability Direct Error Pattern Testing Decoding Algorithm
Bibliographic record
Abstract
We introduce a novel soft-decision decoding algorithm for binary block codes named ordered reliability direct error pattern testing (ORDEPT). The proposed technique tests a list of partial error patterns (PEP)s that are arranged according to their logistic weight and completed on-the-fly based on the instantaneous received sequence and the code’s parity-check matrix. Our results, obtained for a variety of popular short high-rate codes, demonstrate that ORDEPT outperforms state-of-the-art decoding algorithms such as ordered reliability bits guessing random additive noise decoding (ORBGRAND) in terms of decoding complexity, and is hardware-favorable in terms of latency and energy consumption. The improvements carry on to the iterative decoding of product codes and convolutional product-like codes, where ORDEPT demonstrates the ability to efficiently find multiple candidate codewords and outperform state-of-the art competitors.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".