COVERING OF TRANSIENT SIMULATION OF FEEDBACK-FREE CIRCUITS BY BINARY ANALYSIS
Bibliographic record
Abstract
Transient simulation of a gate circuit is an efficient method of counting signal changes occurring during a transition of the circuit. It is known that this simulation covers the results of classical binary analysis, in the sense that all signal changes appearing in binary analysis are also predicted by the simulation. For feedback-free circuits of 1- and 2-input gates, it had been shown that the converse also holds, if wire delays are taken into account. In this paper we generalize this result. First, we prove that, for any feedback-free circuit N of arbitrary gates, there exists an expanded circuit[Formula: see text], constructed by adding a number of delays to each wire of N, such that binary analysis of [Formula: see text] covers transient simulation of N. For this result, the number of delays added to a wire is obtained from the transient simulation. Our second result involves adding only one delay per wire, which leads to the singular circuit[Formula: see text] of N. This result is restricted to circuits consisting only of gates realizing functions from the set [Formula: see text], functions obtained by complementing any number of inputs and/or the output of a function from [Formula: see text], and FORKS. The numbers of inputs of the AND, OR and XOR gates are arbitrary, and all functions of two variables are included. We show that binary analysis of such a circuit [Formula: see text] covers transient simulation of N. We also show that this result cannot be extended to arbitrary gates, if we allow only a constant number of delays per wire.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".