Event-Triggered Consensus of Nonlinear Agents with Quantized Broadcasts: A Hybrid System Approach
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
Information exchange among agents operating over a network, in practice, is restrained by limited communication bandwidth; this concern is often addressed by employing quantized broadcasts. In this paper, we study the problem of consensus of nonlinear multi-agent systems (MASs) over a directed network where the agents employ: a) encoders, that quantize relevant information prior to broadcasting, and b) decoders, that process this information upon arrival. The decision on the broadcast instant itself is made with the help of a dynamic event-triggering mechanism (ETM) in that the agents evaluate their respective event-triggering conditions intermittently at pre-designed sampling instants (which may be both aperiodic and asynchronous). Subsequently, the agents utilize model-based propagates of the decoded neighbor states in their control protocols to achieve consensus. The overall MAS is modeled using hybrid systems framework and the results are demonstrated through an illustrative example.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 | 0.000 |
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