Connectivity preservation in a network of single integrator agents
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Bibliographic record
Abstract
This paper proposes a general class of distributed potential-based control laws with the connectivity preserving property for single integrator agents. Given a connected information flow graph, the potential functions are designed in such a way that when an edge in the graph is about to lose connectivity, the gradient of the potential function lies in the direction of the corresponding edge aiming to shrink it. Therefore, the corresponding control laws preserve the connectivity of the information flow graph. The potential functions are chosen to be bounded with bounded partial derivatives, resulting in bounded control inputs. The results are developed for the case of static information flow graph, but can be easily extended to the case of dynamic edge addition. Other constraints may be imposed on the potential functions to satisfy other design criteria such as consensus and formation convergence. The effectiveness of the proposed control strategy is illustrated via simulation.
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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 | 0.000 | 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 | 0.001 |
| 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