Cluster‐based improvement rates for trust establishment models in single or distributed 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
Abstract Intelligent agents within open and dynamic multi‐agent systems are becoming increasingly capable in their decision‐making abilities and rely upon the notion of trustworthiness to determine which agents to interact with. To improve the overall performance of trust establishment models which trustees individually select and equip to improve their trustworthiness with trustors, while balancing the resources being spent, a cluster‐based trust establishment model update mechanism is proposed. This cluster‐based approach is applicable to robust trust establishment models which utilize dynamic improvement and disimprovement rate variables to adjust a trustee's behaviors toward trustors to improve or maintain trust with the trustor. By storing a single trust establishment model's dynamic improvement and disimprovement rate variables independently for each trustor and by clustering similar trustors together based on observed experiences, a model can more accurately update a trustee's behaviors toward trustors. Through simulated experiments comparing the performance of the existing integrated trust establishment (ITE) model with and without the cluster‐based approach, with varying trustor to trustee ratios to diversify the agent behaviors, the cluster‐based approach consistently improves a trustee's ability to fully meet a trustor's needs, for less resources than ITE, while minimizing the corresponding impact to the trustee's overall trust.
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 | 0.001 | 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 | 0.000 | 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