Optimizing the length of checking sequences
Why this work is in the frame
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Bibliographic record
Abstract
A checking sequence, generated from a finite state machine, is a test sequence that is guaranteed to lead to a failure if the system under test is faulty and has no more states than the specification. The problem of generating a checking sequence for a finite state machine M is simplified if M has a distinguishing sequence: an input sequence D~ with the property that the output sequence produced by M in response to D is different for the different states of M. Previous work has shown that, where a distinguishing sequence is known, an efficient checking sequence can be produced from the elements of a set A of sequences that verify the distinguishing sequence used and the elements of a set /spl gamma/ of subsequences that test the individual transitions by following each transition t by the distinguishing sequence that verifies the final state of t. In this previous work, A is a predefined set and /spl gamma/ is defined in terms of A. The checking sequence is produced by connecting the elements of /spl gamma/ and A to form a single sequence, using a predefined acyclic set E/sub c/ of transitions. An optimization algorithm is used in order to produce the shortest such checking sequence that can be generated on the basis of the given A and E/sub c/. However, this previous work did not state how the sets A and E/sub c/ should be chosen. This paper investigates the problem of finding appropriate A and E/sub c/ to be used in checking sequence generation. We show how a set A may be chosen so that it minimizes the sum of the lengths of the sequences to be combined. Further, we show that the optimization step, in the checking sequence generation algorithm, may be adapted so that it generates the optimal E/sub c/. Experiments are used to evaluate the proposed method.
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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 | 0.000 | 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 | 0.001 | 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