Average-consensus with switched Markovian network links
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
Decentralized network estimation of the average initial node value is considered here in a variety of stochastic network settings. Each setting assumes the elements of the network communication graph edge set are modeled as a collection of ergodic Markov chains with slowly switching regime and unknown stationary distribution. In this framework an asymptotic average-consensus is obtained by using a “damped” distributive averaging algorithm in conjunction with an adaptive weighting scheme. The weighting scheme is designed to off-set the unknown probabilities of node communication by associating with each transmission a weight that is inversely proportional to the current estimate of the nodes communication probability. It is shown that for suitably connected graphs with balanced edge sets, and in particular any connected undirected edge set, the weighting scheme and averaging algorithm together yield a network consensus that is bounded to within an arbitrary distance of the average initial node value. The asymptotic node value scaled error measured relative to the node steady-state is also characterized by a vanishing diffusion equation with parameters that approach zero only as the nodes approach consensus. Simulations employ the proposed algorithm to demonstrate its proficiency and illustrate our results.
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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) | 0.000 | 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