Low-Complexity Software Stack Decoding of Polar Codes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Polar codes are a recent class of linear error-correcting codes that asymptotically achieve the channel capacity at infinite code length. The Successive Cancellation List (SCL) algorithm yields very good error-correction performance, at the cost of high implementation complexity. The Stack (SCS) decoding algorithm provides similar error-correction performance at a lower complexity. In this work, we propose an efficient software implementation of the SCS decoding algorithm, along with techniques to further reduce its computational complexity. In particular, we reduce the SCS memory requirements through efficient path switching, replace the stack sorting with a linear search, and explore the use of a partial CRC along with an early termination criterion. Using the proposed methods, we are able to reduce the computational complexity of the SCS decoder, reducing the number of estimated bits up to 97% with respect to SCL, while maintaining similar error-correction performance as SCL.
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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 | 0.000 |
| 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 it