Decontamination of Arbitrary Networks using a Team of Mobile Agents with Limited Visibility
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we consider the problem of decontaminating synchronous networks with mobile agents using breadth-first- search (BFS) technique. We consider various networks with different number of home bases to study the relationship between the number of home bases and the mobile agents/steps required to decontaminate. Through experiments, we demonstrate that as the number of home bases increases, the number of mobile agents required decreases in all network topologies considered. We observed that as the number of home bases increases the number of steps taken to decontaminate the network also decreases. The overuse of mobile agents due to the BFS strategy increases with the decrease in the number of contaminated nodes. For synchronous networks, increasing the number of home bases has an impact on the number of mobile agents needed. In particular, we note that this translates to a reduced number of mobile agents required for certain number of home bases.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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