LocalSketch: An Accurate and Efficient Sketch for Range Spread Estimation
Why this work is in the frame
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Bibliographic record
Abstract
Sketch demonstrates good properties in spread estimation over network measurements, providing fast processing and accurate estimation under limited memory usage. However, most current methods remain limited to single-flow spread estimation, resulting in suboptimal performance when applied to range spread estimation that requires measuring the spread of a range of flows. In this paper, we propose LocalSketch, a novel sketch that achieves both high estimation accuracy and memory efficiency for range spread estimation with provable theoretical guarantees. LocalSketch has two key innovations: (1) local key aggregation within predefined ranges that eliminates duplicate spread information through locality correlation, and (2) adaptive counter sizing that dynamically allocates memory resources for large-spread ranges while maintaining compact representations for low-spread ranges. LocalSketch also features an efficient abnormal bucket detection mechanism by comparing identification sign, avoiding exhaustive bucket traversal during super range detection. Moreover, the main idea of LocalSketch can be adapted to existing plug-in spread counters, which has been experimentally proved. We provide a theoretical analysis of estimation accuracy and conduct comprehensive evaluations using real-world network traffic datasets. Experimental results demonstrate that LocalSketch outperforms state-of-the-art methods by achieving 76× higher estimation accuracy for range spread estimation, while showing 15× better accuracy and 39× faster detection speed for super range identification across all datasets.
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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