Finding discrete logarithms with a set orbit distinguisher
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Bibliographic record
Abstract
Abstract. We consider finding discrete logarithms in a group of prime order p when the help of an algorithm D that distinguishes certain subsets of from each other is available. If the complexity of D is a polynomial, say , then we can find discrete logarithms faster than square-root algorithms. We consider two variations on this idea and give algorithms solving the discrete logarithm problem in with complexity and when has factors of suitable size. When multiple distinguishers are available and is sufficiently smooth, logarithms can be found in polynomial time. We discuss natural classes of algorithms D that distinguish the required subsets, and prove that for some of these classes no algorithm for distinguishing can be efficient. The subsets distinguished are also relevant in the study of error correcting codes, and we give an application of our work to bounds for error-correcting codes.
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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 | 0.000 |
| 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