Automatic verification of reduction techniques in Higher Order Logic
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
Abstract In this paper we propose an automatic methodology to verify the soundness of model checking reduction techniques. The idea is to use the consistency of the specifications to verify if the reduced model is faithful to the original one. The user provides the reduction technique, the specification and the system under verification. Then, using Higher Order Logic he verifies automatically if the reduction technique is soundly applied. The method is completely defined in an MDG–HOL special integration platform that combines an automatic high level model checking tool Multiway Decision Graphs (MDGs) within the HOL theorem prover. We provide two case studies, the first one is the reduction using SAT–MDG of an Island Tunnel Controller and the second one is the MDG–HOL assume-guarantee reduction of the Look-Aside Interface. The obtained results of our approach offer a considerable gain in terms of the correctness of heuristics and reduction techniques as applied to commercial model checking, however a small penalty is paid in terms of CPU time and memory usage.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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