Correcting erasure bursts with minimum decoding delay
Why this work is in the frame
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Bibliographic record
Abstract
Erasure correcting codes are widely used in upper layers of packet-switched networks, where the packet erasures often exhibit bursty patterns. The conventional wisdom to deal with bursty erasures is to apply block interleaving to break down the bursty patterns prior to error correcting coding, or use long-block Reed-Solomon codes. We show that they unnecessarily lead to sub-optimal decoding delay. In this work, with the problem model of multiple erasure bursts present in a coding block, we study the fundamental tradeoff among the rate, decoding delay and burst correction performance of erasure correcting codes. Focusing on a class of codes achieving the Singleton bound, we show that the lowest delay to recover any individual symbol not only depends on the erasure burst patterns correctable by a code, but also on whether the source symbols are encoded causally or non-causally. We also describe a few practical linear code constructions that achieve the performance limit discussed.
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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