Setting Speed Records with the (Fractional) Multibase Non-Adjacent Form Method for Efficient Elliptic Curve Scalar Multiplication.
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Bibliographic record
Abstract
Abstract. In this paper, we introduce the Fractional Window-w Multibase Non-Adjacent Form (Frac-wmbNAF) method to perform the scalar multiplication. This method generalizes the recently developed Window-w mbNAF (wmbNAF) method by allowing an unrestricted number of precomputed points. We then make a comprehensive analysis of the most recent and relevant methods existent in the literature for the ECC scalar multiplication, including the presented generalization and its original non-window version known as Multibase Non-Adjacent Form (mbNAF). Moreover, we present new improvements in the point operation formulae. Specifically, we reduce further the cost of composite operations such as doublingaddition, tripling, quintupling and septupling of a point, which are relevant for the speed up of methods using multiple bases. Following, we also analyze the precomputation stage in scalar multiplications and present efficient schemes for the different studied scenarios. Our analysis includes the standard elliptic curves using Jacobian coordinates, and also Edwards curves, which are gaining growing attention due to their high performance. We demonstrate with extensive tests that mbNAF is currently the most efficient method without precomputations not only for the standard curves but also for the faster Edwards form. Similarly, Frac-wmbNAF is shown to attain the highest performance among window-based methods for all the studied curve forms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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