Reinforcement-Learning-Based Successive Approximation Algorithm
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Bibliographic record
Abstract
This paper presents a new approach to analog-to-digital converter (ADC) for low to medium-activity signals. We integrate the concept of reinforcement learning into the successive approximation register (SAR) ADC search methodology to reduce the comparator activity and switching energy of the digital-to-analog converter (DAC). This method is based on selecting the best solution among 8 available different solutions to digitize the first-order difference between an unknown sample and its previous digitized sample, in addition to the conventional method to digitize the sample amplitude itself. In this way, the number of comparisons needed can be smartly reduced. Our 10-bit SAR ADC simulation results show that the proposed method reduces comparator activity for low to medium-activity signals by up to 79.74%, while it operates the same as the conventional method for high-activity signals.
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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 | 0.000 | 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 | 0.000 | 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