Sparse Multi-Decoder Recursive Projection Aggregation for Reed-Muller\n Codes
Bibliographic record
Abstract
Reed-Muller (RM) codes are one of the oldest families of codes. Recently, a\nrecursive projection aggregation (RPA) decoder has been proposed, which\nachieves a performance that is close to the maximum likelihood decoder for\nshort-length RM codes. One of its main drawbacks, however, is the large amount\nof computations needed. In this paper, we devise a new algorithm to lower the\ncomputational budget while keeping a performance close to that of the RPA\ndecoder. The proposed approach consists of multiple sparse RPAs that are\ngenerated by performing only a selection of projections in each sparsified\ndecoder. In the end, a cyclic redundancy check (CRC) is used to decide between\noutput codewords. Simulation results show that our proposed approach reduces\nthe RPA decoder's computations up to $80\\%$ with negligible performance loss.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".