Automated distributed implementation of component-based models with priorities
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
In this paper, we introduce a novel model-based approach for constructing correct distributed implementation of component-based models constrained by priorities. We argue that model-based methods are especially of interest in the context of distributed embedded system due to their inherent complexity. Our three-phase method's input is a model specified in terms of a set of behavioural components that interact through a set of high-level synchronization primitives (e.g., rendezvous and broadcasts) and priority rules for scheduling purposes. Our technique, first, transforms the input model into a model that has no priorities. Then, it transforms the deprioritized model into another model that resolves distributed conflicts by incorporating a solution to the committee coordination problem. Finally, it generates distributed code using asynchronous point-to-point send/receive primitives. All transformations preserve the properties of their input model by ensuring observational equivalence. The transformations are implemented and our experiments validate their effectiveness.
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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) | 0.001 | 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 | 0.001 |
| 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