Decentralized Diagnosis of Discrete Event Systems Using an Arborescent Architecture
Why this work is in the frame
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Bibliographic record
Abstract
Decentralized diagnosis of discrete event systems consists in detecting faults in discrete event systems by using decentralized architectures. In particular, inference-based diagnosis is a decentralized architecture of interest, since it is more general than several other decentralized architectures. In this paper, we first propose a method that realizes a diagnosis objective D by an arborescent architecture (or tree). Each leaf of the tree is a decentralized diagnosis, and each node n is a disjunction or con- junction of the diagnosis decisions of the two children of n. Then, we show that if inference-based diagnosis is applicable to D, then all the leafs of the obtained tree are basic decentralized diagnosers. This implies that every inference-based diagnosis is realizable by a combination of basic decentralized diagnosers.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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