Minimal‐time complex consensus for multi‐agent systems with time delay
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
Abstract This paper studies the minimum time consensus problem for discrete‐time multi‐agent systems with complex Laplacian delay networks such that each agent can find its complex consensus value in a minimum number of steps using its local observations. The stability analysis is first provided and the convergence condition is derived for complex weighted networks with time delays. Specifically, the delayed multi‐agent system is modeled by employing the augmented graph representation. Via adding virtual agents in the augmented systems, the complex consensus is obtained in the networks with bounded time delay if the communication topology digraph of the system has a spanning tree. A decentralized algorithm is proposed for the minimal‐time computation of complex consensus based on the information from the robot itself without relying on the external environment. The algorithm hinges on the minimal polynomial of the matrix concerning the augmented graph. Furthermore, the rearrangement of the virtual agents in the augmented system provides an upper bound for the number of agents required to compute the consensus value. Simulation examples demonstrate the effectiveness of our results. The advantage of this approach is that it can be easily deployed on a group of agents to rapidly achieve a complex consensus setting within any delayed networks.
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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 | 0.001 | 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