Group Role Assignment with a Training Plan
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
Training is a cost-effective way to enhance individual ability, which is also of great significance for group development. According to Role-Based Collaboration (RBC), the performance of an agent on a specific role is the basis of role assignment. Training directly affects the agents’ performance on roles, which will also influence the assignment scheme. To explore the specific effect of agent training, this paper discusses the formulation of training plan and role assignment after training under the premise of maximizing the group performance. The training plan includes agents and corresponding training programs. By utilizing RBC and its general model, the proposed method formulates the optimal training plan, which makes sure the selected agents perform better than in-service ones on some certain roles. The role assignment is based on the updated ability matrix, and the benefit of the training plan is also calculated. The effectiveness of the proposed method is proved by simulation experiments, and the group performance is promoted after training.
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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 | 0.001 | 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