On the synthesis of MVL functions using input and output phase assignments
Bibliographic record
Abstract
In this paper, a number of decomposition based mapping techniques are proposed. In these techniques, the synthesis problem is formulated as a mapping from an input matrix to an output matrix. The minimization is obtained by constructing a matching-count matrix. The entries of the matching-count matrix MC/sub i,j/ represent the number of entry matches between the input variable number i in the input matrix (X) and the output function number j in the output matrix (Y). It then selects those input-output pairings that give the maximum matching count, thus maximizing the number of switching operations which can be eliminated in the realization of MVL functions. The proposed techniques are classified as: output-phase with complement, input-phase with and without complement. Numerical results are presented to show that the proposed techniques result in significant reduction in the number of switching operators required for the implementation of 5000 randomly generated r-valued functions (for r=3, 4, and 5).
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".