An extensible perceptron framework for revision RTL debug automation
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
Automated debugging techniques can significantly reduce the manual effort required to localize RTL errors. These techniques return to the user a set of RTL locations where a change can correct erroneous behavior. However, each location must be manually investigated. This problem is exacerbated by the increasing amount of failures in the modern regression verification cycle. Recent work in clustering-based revision debugging mitigates this cost by ranking revisions based on their likelihood of having introduced an error. This work presents a perceptron based approach to revision debugging that can be extended to leverage the revision history of a design directly. Perceptrons are trained using labeled revisions from the design history. They are then used to predict the probability that a revision has introduced an error. The proposed methodology performs competitively with the state-of-the-art, but can be extended to handle more features. This allows for an automated regression debug flow integrated with Version Control and Issue Tracking Systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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