Architectural Components of Information–Sharing Societies
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
Two similar multi–agent systems have been designed to address the issue of information sharing within a multi–agent system. This paper examines the architectural components that have been added to our information–sharing societies, ACORN and MP3. Through this exploration, we conclude that these components and their underlying concepts can be added to other information–retrieval societies. ACORN consists of a set of information–sharing locations referred to as cafés. Cafés are defined as meeting locations for like–minded agents. Like–minded agents are defined as agents that share a common set of interests. As an example, a café may contain agents that are interested in information relating to cars. A dynamic café clustering method is developed. The performance evaluation of the proposed structure for the café is presented. The concept of a fat / thin agent architecture is introduced. This agent architecture allows for minimizing network traffic as agents traverse the network in search of or distribution of knowledge. The directory server component is presented along with its relation to the fat/thin agent architecture. Finally, an anonymity service provider which allows anonymity for users is introduced. The MP3 society exists with the sole purpose of finding MP3s throughout a given network. Through this society, the core design issues of agent verification and agent validation are addressed and solutions are presented through respective interface components.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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