Transformations of self-stabilizing algorithms
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we are interested in transformations of self-stabilizing algorithms from one model to another that preserve stabilization. We propose an easy technique for proving correctness of a natural class of transformations of self-stabilizing algorithms from any model to any other. We present a new transformation of self-stabilizing algorithms from a message passing model to a shared memory model with a finite number of registers of bounded size and processors of bounded memory and prove it correct using our technique. This transformation is not wait-free, but we prove that no such transformation can be wait-free. For our transformation, we use a new self-stabilizing token-passing algorithm for the shared memory model. This algorithm stabilizes in O(nlog 2 n) rounds, which improves existing algorithms.
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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