Arithmetic operators for on-the-fly evaluation of TRNGs
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
Many cryptosystems embed a high-quality true random number generator (TRNG). The randomness quality of a TRNG output stream depends on its implementation and may vary due to various changes in the environment such as power supply, temperature, electromagnetic interferences. Attacking TRNGs may be a good solution to decrease the security of a cryptosystem leading to lower security keys or bad padding values for instance. In order to protect TRNGs, on-the-fly evaluation of their randomness quality must be integrated on the chip. In this paper, we present some preliminary results of the FPGA implementation of functional units dedicated to statistical tests for on-the-fly randomness evaluation. In the entropy test the evaluation of the harmonic series at some ranks is required. Usually its approximation is costly. We propose a multiple interval polynomial approximation. The decomposition of the whole domain into small sub-intervals leads to a good trade-off between the degree of the polynomial (i.e. multipliers cost) and the memory resources required to store the coefficients for all sub-intervals.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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