Identifying redundant gate replacements in verification by error modeling
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers verification of combinational circuits by test vectors under assumption of gate and wire replacement faults. Identifying redundant faults is critical to the quality and speed of such verification schemes. We propose the first known exact redundancy identification of gate replacement faults, together with its efficient approximations. While both solutions use the SAT formulation of redundancy identification, we propose the means to effectively use any single stuck-at-value redundancy identification in the approximate schemes, with varying detection accuracy. Critical to the latter are the novel uses of don't care approximations that detect many redundant faults and quickly identify those that can be detected by methods for stuck-at value faults. A test generation scheme that uses the error-correcting properties of Arithmetic Transforms is incorporated into the overall verification procedure, and is shown to provide high fault coverage for these fault models.
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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 | 0.001 |
| 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