A complete approach to unreachable state diagnosability via property directed reachability
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
In modern hardware design, substantial manual effort is required to fix a design when verification discovers a state unreachable. This paper addresses this growing pain where given an unreachable target state, a methodology is presented to return all design locations where a change can be implemented to make the target state reachable. In contrast to previous state reachability rectification techniques that use bounded model checking, our approach addresses the issue using unbounded model checking. It first enhances the circuit transition relation by inserting a novel error model construction at each suspect location. An unbounded model checking algorithm is then applied to the enhanced transition relation to find which of the suspect locations can be changed to make the target state reachable. The use of unbounded model checking allows it to identify the complete problem solution set. As an added benefit, it also returns a proof that no further solution(s) exist in the form of an inductive invariant. Empirical results on industrial designs confirm the theoretical and practical gains of this approach.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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