DECONTAMINATING CHORDAL RINGS AND TORI USING MOBILE AGENTS
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
In this paper we consider a network where an intruder is moving "contaminating" the nodes it passes by, and we focus on the problem of decontaminating such a network by a team of mobile agents. The contamination/decontamination process has the following asynchronous dynamics: when the team is deployed all nodes are assumed to be contaminated, when an agent transits on a node, it will clean the node, when the node is left with no agent, the node will be recontaminated as soon as at least one of its neighbours is contaminated. We study the problem in asynchronous chordal ring networks and in tori. We consider two variations of the model: one where agents have only local knowledge, the other in which they have "visibility", i.e., they can "see" the state of their neighbouring nodes. We first derive lower bounds on the minimum number of agents necessary for the decontamination. In the case of chordal rings we show that the number of agents necessary to perform the cleaning does not depend on the size of the network; in fact it is linear in the length of the longest chord (provided that it is not too long). In the case of a torus, the minimum number of agents is equal to 2 · h, where h is the smallest dimension. We then propose optimal strategies for decontamination and we analyse the number of moves and the time complexity of the decontamination algorithms, showing that the visibility assumption allows us to decrease substantially both complexity measures. Another advantage of the "visibility model" is that agents move independently and autonomously without requiring any coordination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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