A criterion for decoding on the binary symmetric channel
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Bibliographic record
Abstract
We present an approach to showing that a linear code is resilient to random errors. We use this approach to obtain decoding results for both transitive and doubly transitive codes. We give three kinds of results about linear codes in general, and transitive linear codes in particular.1. We give a tight bound on the weight distribution of every transitive linear code $ C \subseteq \mathbb{F}_2^N $:$ \mathop {\Pr }\limits_{c \in C}[ \text{wt}(c) = \alpha N] \leq 2^{-(1-h(\alpha)) \mathsf{dim}(C)}. $2. We give a criterion that certifies that a linear code $ C $ can be decoded on the binary symmetric channel. Let $ K_s(x) $ denote the Krawtchouk polynomial of degree $ s $, and let $ C^\perp $ denote the dual code of $ C $. We show that bounds on $ \mathbb{E}_{c \in C^{\perp}}[ K_{\epsilon N}( \text{wt}(c))^2] $ imply that $ C $ recovers from errors on the binary symmetric channel with parameter $ \epsilon $. Weaker bounds can be used to obtain list-decoding results using similar methods. One consequence of our criterion is that whenever the weight distribution of $ C^\perp $ is sufficiently close to the binomial distribution in some interval around $ \frac{N}{2} $, $ C $ is resilient to $ \epsilon $-errors.3. We combine known estimates for the Krawtchouk polynomials with our weight bound for transitive codes, and with known weight bounds for doubly transitive codes, to obtain list-decoding results for both these families of codes. In some regimes, our bounds for doubly transitive codes achieve the information-theoretic optimal trade-off between rate and list size.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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