A self-stabilising algorithm for 3-edge-connectivity
Why this work is in the frame
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Bibliographic record
Abstract
Self-stabilisation is a theoretical framework for fault-tolerance without external assistance. Adoption of self-stabilisation in distributed systems has received considerable research interest over the last decade. In this paper, we propose a self-stabilising algorithm for 3-edge-connectivity of an asynchronous distributed model of computation. A self-stabilising depth-first search algorithm is run concurrently to build a depth-first search spanning tree of the system. Once such a tree is constructed, all the 3-edge-connected components of the system can be detected in O ( h ) rounds, where h is the height of the depth-first search tree. The result of computation is kept in a distributed fashion in the sense that, upon stabilisation of the algorithm, each processor knows all other processors that are 3-edge-connected to it. The space complexity of our algorithm is O ( n 2 log Δ) bits per processor, where Δ is an upper bound on the degree of a processor.
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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 | 0.001 |
| 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