Managing traffic in peer-to-peer networks: the token-web protocol
Why this work is in the frame
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Bibliographic record
Abstract
We distinguish two types of P2P protocols: structured protocols, which use a directed search approach, and unstructured protocols, which use a flooding approach. We propose the Token-Web as a new type of P2P, semi-structured protocols, that combines directed search and controlled flooding. The protocol presumes that most participants are trustworthy and therefore it does not require authentification. However, mechanisms to prevent disruption are set in place. In this paper, we describe the Token-Web and present results of experiments conducted to assess its properties in a simulated environment and on PlanetLab. The results obtained show that the number of tokens tends to stabilize over time, the message drop rate depends on the level of activity in the network, and the query success rate is dependent on the popularity of the resource sought.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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