Localized Broadcasting with Guaranteed Delivery and Bounded Transmission Redundancy
Why this work is in the frame
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Bibliographic record
Abstract
The common belief is that localized broadcast algorithms are not able to guarantee both full delivery and a good bound on the number of transmissions. In this paper, we propose the first localized broadcast algorithm that guarantees full delivery and a constant approximation ratio to the minimum number of required transmissions in the worst case. The proposed broadcast algorithm is a self-pruning algorithm based on one round of information exchange. Using the proposed algorithm, each node determines its forwarding status in O(D logD), where D is the maximum node degree of the network. By extending the proposed algorithm, we show that localized broadcast algorithms can achieve both full delivery and a constant approximation ratio to the optimum solution with message complexity O(N), where N is the total number of nodes in the network and each message contains a constant number of bits. We also show how to save bandwidth by reducing the size of piggybacked information. Finally, we relax several system-model assumptions, or replace them with practical ones, in order to improve the practicality of the proposed broadcast algorithm.
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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 | 0.001 | 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 it