Optimal and scalable distribution of content updates over a mobile social network
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
We study the dissemination of dynamic content, such as news or traffic information, over a mobile social network. In this application, mobile users subscribe to a dynamic-content distribution service, offered by their service provider. To improve coverage and increase capacity, we assume that users share any content updates they receive with other users they meet. We make two contributions. First, we determine how the service provider can allocate its bandwidth optimally to make the content at users as "fresh" as possible. More precisely, we define a global fairness objective (namely, maximizing the aggregate utility over all users) and prove that the corresponding optimization problem can be solved by gradient descent. Second, we specify a condition under which the system is highly scalable: even if the total bandwidth dedicated by the service provider remains fixed, the expected content age at each user grows slowly (as log(n)) with the number of users n. To the best of our knowledge, our work is the first to address these two aspects (optimality and scalability) of the distribution of dynamic content over a mobile social network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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