Speeding Up Multi-Scalar Multiplication over Fixed Points Towards Efficient zkSNARKs
Why this work is in the frame
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Bibliographic record
Abstract
The arithmetic of computing multiple scalar multiplications in an elliptic curve group then adding them together is called multi-scalar multiplication (MSM). MSM over fixed points dominates the time consumption in the pairing-based trusted setup zero-knowledge succinct non-interactive argument of knowledge (zkSNARK), thus for practical applications we would appreciate fast algorithms to compute it. This paper proposes a bucket set construction that can be utilized in the context of Pippenger’s bucket method to speed up MSM over fixed points with the help of precomputation. If instantiating the proposed construction over BLS12-381 curve, when computing n-scalar multiplications for n = 2e (10 ≤ e ≤ 21), theoretical analysis ndicates that the proposed construction saves more than 21% computational cost compared to Pippenger’s bucket method, and that it saves 2.6% to 9.6% computational cost compared to the most popular variant of Pippenger’s bucket method. Finally, our experimental result demonstrates the feasibility of accelerating the computation of MSM over fixed points using large precomputation tables as well as the effectiveness of our new construction.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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