Low-Overhead Dynamic Sampling for Redundant Traffic Elimination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract—Protocol-independent redundant traffic elimination (RTE) is an "on the fly " method for detecting and removing redundant chunks of data from network-layer packets traversing a constrained link or path. Efficient algorithms are needed to sample data chunks and detect redundancy, so that RTE does not hinder network throughput. A recently proposed static algorithm samples chunks based on highly-redundant trigger bytes observed in data content. While this algorithm is fast, it requires pre-computed traffic information for the configuration of its static parameters, and it tends to either under-sample (reducing byte savings) or over-sample (increasing processing cost) on heterogeneous traffic. We propose a dynamic sampling algorithm for redundant content detection. Our algorithm is adaptive and self-configuring, and can precisely match the specified sampling rate. Furthermore, it offers byte savings comparable to the static algorithm, with very low additional processing overhead.
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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 | 0.000 |
| 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 | 0.002 |
| 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