Maintenance-based trust for multi-agent systems
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
In last years, trust and reputation has been gaining increasing interest in multi-agent systems (MAS). To address this issue, we propose in this paper a maintenance-based trust mechanism for agents operating in multi-agent systems. In the proposed model, a comprehensive trust assessment process is provided to assess the trustworthiness of the participating agents. The main characteristic of this model is the retrospect trust adjustments, which integrate the applicable constraints and modify the involved features with respect to the actual performance of the evaluated agent. Specifically, the retrospect process updates the belief set of the agents in order to adapt them to the social network changes. This paper has two contributions: after describing the architecture of the proposed framework, we provide a theoretical analysis of its assessment and discuss the system implementation, along with simulations comparing it with the broadly known frameworks.
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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 | 0.000 |
| 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