Efficiently reaching consensus on the largest entries of a vector
Why this work is in the frame
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Bibliographic record
Abstract
We consider a problem where agents gossip on a d-dimensional state vector. The goal is to achieve a consensus on the average. However, instead of computing the average of the entire d-dimensional state, the goal is to have all agents reach a consensus on the largest k entries of the average initial state vector. For example, the value in each entry could correspond to the agents' opinions about a different item, in which case the goal is to determine which are the k most popular items, on average. A primary challenge is that the indices of the k largest entries are not known a priori, and so the agents must adaptively identify which entries are the largest while also computing their values. We consider an asynchronous gossip-style algorithm where pairs of agents interact, communicate, and update only those state entries which either agent currently believes to be one of the largest k. We show that, as long as the underlying communication graph is connected, the algorithm converges to a state where all agents reach a consensus on the indices and values of the largest k entries of the initial average. We study, via numerical simulation, the convergence rate of the algorithm in terms of the total number of scalar values transmitted to reach a desired level of accuracy.
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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 | 0.000 |
| 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