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
Consider a communication network where each process needs to securely exchange messages with its neighboring processes. In this network, each sent message is encrypted using one or more symmetric keys that are shared only between two processes: the process that sends the message and the neighboring process that receives the message. A straightforward scheme for assigning symmetric keys to the different processes in such a network is to assign each process O ( d ) keys, where d is the maximum number of neighbors of any process in the network. In this article, we present a more efficient scheme for assigning symmetric keys to the different processes in a communication network. This scheme, which is referred to as logarithmic keying, assigns O (log d ) symmetric keys to each process in the network. We show that logarithmic keying can be used in rich classes of communication networks that include star networks, acyclic networks, limited-cycle networks, planar networks, and dense bipartite networks. In addition, we present a construction that utilizes efficient keying schemes for general bipartite networks to construct efficient keying schemes for general networks.
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 | 0.001 | 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