Backbone-based connectivity control for mobile networks
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
While a network of autonomous mobile agents is capable of performing spatially distributed tasks, communication between agents imposes a class of constraints over the corresponding task. This paper proposes a distributed paradigm to deal with a critical communication constraint, connectedness constraint, where a group of mobile agents are required to remain connected while performing a task (e.g., formation control, consensus, etc.). The proposed method adaptively extracts communication backbone of the group, which is formed by a subset of agents, and thus partitions the group into backbone agents and non-backbone agents. The connectedness of the system is maintained at two levels: motion of backbone agents is controlled to maintain existing connections in the backbone; motion of non-backbone agents is determined via a leader-follower formation control method with backbone agents as the leaders. Key advantages of the proposed approach are that it can deal with arbitrary system topologies, it is a distributed method, it uses only two-hop neighbor information, and has low communication cost.
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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 | 0.000 | 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