Evolution of Algorithms to find Prime Numbers
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

 
 
 
 Prime numbers, and the deterministic formulas used to find them, have garnered considerable attention from mathematicians, professionals and amateurs alike. A prime number is a positive integer, excluding 1, whose only divisors are 1 and itself. For example, 23 is a prime number as it can only be divided by 1 and 23. A number that is not prime is called a composite number.
 While prime numbers under 100 are fairly abundant, they become less frequent and difficult to find in a systematic manner as the digits in the number increase since they do not appear to follow a predictable distribution. So why do researchers keep studying them? For over 150 years, mathematicians have attempted to uncover a deterministic formula to identify prime numbers. If such a formula existed, all numbers could be factored relatively quickly using computers. Paradoxically, much of electronic data today is encrypted by taking advantage of the fact that it is difficult and time consuming for a computer program to factor a large composite number. A formula to find all prime numbers would be a significant breakthrough in mathematics, but severely detrimental to data security.
 
 
 
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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