RED-f routing protocol for complex networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we address routing in complex networks. Routing traffic across a network requires finding best possible paths between sources and destinations. When data traffic changes dynamically, a path that was optimal in the past may not be the best for the next packet. Adapting to traffic changes and finding optimal paths dynamically are challenging tasks. They become more demanding in large and complex networks. In optical burst switching (OBS) networks, two optical bursts contending for the same link need resolution mechanisms other than queueing. Deflection routing protocols are used to override routing tables and “deflect” one of the bursts to a free link. Instead of deflecting bursts at an immediate point of contention, the proposed Random Early Deflection (RED-f) routing protocol triggers deflection ahead of time and, thus, offers additional routing paths and lowers the burst loss rate due to contention. Simulations demonstrate that RED-f enabled nodes in a scale-free complex network reduce burst loss rate by exchanging control information with only few other network nodes.
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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 | 0.000 | 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