Event-Triggered Bipartite Consensus for Multiagent Systems: A Zeno-Free Analysis
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Bibliographic record
Abstract
In this article, the bipartite consensus of first-order multiagent systems with a connected structurally balanced signed graph is studied. To reduce the communications among agents, a distributed event-triggered control law is proposed, where the event-triggering condition of each agent only uses its own state and the sampled states of its neighbours, and no knowledge of the global network topology is required. By relating to the nonexistence of some finite-time convergence, a novel analysis is given to show that there is no Zeno behavior in the proposed event-triggered multiagent system. Then, from the Lyapunov stability theory and the algebraic graph theory, it is proved that all agents can reach agreement with an identical magnitude but opposite signs. Finally, a numerical example is given to illustrate the efficiency and feasibility of the proposed results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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