The speed of greed: Characterizing myopic gossip through network voracity
Why this work is in the frame
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Bibliographic record
Abstract
This paper analyzes the rate of convergence of greedy gossip with eavesdropping (GGE). In previous work, we proposed GGE, a fast gossip algorithm based on exploiting the broadcast nature of wireless communications rather than location information. Assuming all transmissions are wireless broadcasts, nodes can keep track of their neighbors' values by eavesdropping on their communications. Then, when it comes time to gossip, a node greedily and myopically gossips with the neighbor whose value is most different from its own, rather than with a randomly chosen neighbor. Previously, we have proved that GGE converges to the average consensus on connected network topologies and demonstrated that GGE outperforms standard randomized gossip (RG). In this paper we study the rate of convergence of GGE in terms of network voracity which is a topology-dependent constant analogous to the second-largest eigenvalue characterization for RG. Simulations demonstrate that the convergence rate of GGE is superior to existing average consensus algorithms such as geographic gossip.
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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