On Simulation-based Metrics that Characterize the Behavior of RTL Errors
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
Recent advances in automated debugging offer significant reductions in the manual effort required to localize RTL errors. These tools return relatively compact sets of RTL locations that can be potential error sources. However, once these locations are returned, the engineer still has to perform detailed analysis to discard irrelevant locations and identify the culprit. This process happens without any further guidance from the debugger. In this work, we perform a statistical analysis that exposes a significant discrepancy between RTL errors and other unrelated locations returned by these tools. The analysis is conducted on industrial designs and is based on metrics extracted from simulation. Our methodology determines that specific continuous distributions can effectively characterize the behavior of RTL errors. Using these well-defined metrics one can automate the process of further pruning the RTL locations returned by debuggers, effectively accelerating the localization of error sources.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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