Checking Sequence Construction Using Adaptive and Preset Distinguishing Sequences
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
Methods for testing from finite state machine-based specifications often require the existence of a preset distinguishing sequence for constructing checking sequences. It has been shown that an adaptive distinguishing sequence is sufficient for these methods. This result is significant because adaptive distinguishing sequences are strictly more common and up to exponentially shorter than preset ones. However, there has been no study on the actual effect of using adaptive distinguishing sequences on the length of checking sequences. This paper describes experiments that show that checking sequences constructed using adaptive distinguishing sequences are almost consistently shorter than those based on preset distinguishing sequences. This is investigated for three different checking sequence generation methods and the results obtained from an extensive experimental study are given.
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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 | 0.001 |
| 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