Efficient Generation of Monitor Circuits for GSTE Assertion Graphs
Why this work is in the frame
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Bibliographic record
Abstract
Generalized symbolic trajectory evaluation (GSTE) is a powerful, new method for formal verification that combines the industriallyproven scalability and capacity of classical symbolic trajectory evaluation with the expressive power of temporal-logic model checking. GSTE was originally developed at Intel and has been used successfully on Intel’s next-generation microprocessors. However, the supporting algorithms and tools for GSTE are still relatively immature. GSTE specifications are given as assertion graphs, an extension of ∀-automata. This paper presents a linear-time, linear-size translation from GSTE assertion graphs into monitor circuits, which can be used with dynamic verification both as a quick “sanity check ” of the specification before effort is invested in abstraction and formal verification, and also as means to reuse GSTE specifications with other validations methods. We present experimental results using real GSTE assertion graphs for real industrial circuits, showing that the circuit construction procedure is efficient in practice and that the monitor circuits impose minimal simulation overhead. 1.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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