Weak Alphabet Merging of Partial Behavior Models
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
Constructing comprehensive operational models of intended system behavior is a complex and costly task, which can be mitigated by the construction of partial behavior models, providing early feedback and subsequently elaborating them iteratively. However, how should partial behavior models with different viewpoints covering different aspects of behavior be composed? How should partial models of component instances of the same type be put together? In this article, we propose model merging of modal transition systems (MTSs) as a solution to these questions. MTS models are a natural extension of labelled transition systems that support explicit modeling of what is currently unknown about system behavior. We formally define model merging based on weak alphabet refinement, which guarantees property preservation, and show that merging consistent models is a process that should result in a minimal common weak alphabet refinement (MCR). In this article, we provide theoretical results and algorithms that support such a process. Finally, because in practice MTS merging is likely to be combined with other operations over MTSs such as parallel composition, we also study the algebraic properties of merging and apply these, together with the algorithms that support MTS merging, in a case study.
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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