Bibliographic record
Abstract
Sparse Superposition Codes are a class of capacity achieving codes for which decoding can be interpreted as a compressive sensing problem. The approximate message passing algorithm, proven to be effective in compressive sensing, has been proposed in different incarnations as a valid decoding approach. However, most literature focuses on infinite code length and asymptotic performance, while the strong reliance on matrix-and vector-wise operations suggests that a hardware-oriented approach might be more efficient. This work analyzes the performance of two decoding algorithms with finite code lengths and fixed point precision: 5-bit codeword symbol quantization is shown to cause performance degradation ≤ 0.15 dB. In-algorithm quantization values are proposed, together with code construction and algorithm approximations that cause negligible performance degradation. After selecting a set of codes as a case study, a decoding complexity estimation is performed, demonstrating that a fully parallel architecture is unfeasible. Suggestions and improvements towards partially-parallel solutions are given.
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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 | 0.000 | 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".