Marginal Calibration in Multi-agent Probabilistic Systems
Why this work is in the frame
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Bibliographic record
Abstract
The multiply sectioned Bayesian network (MSBN) model successfully extends the traditional Bayesian network (BN) model for the support of probabilistic inference in distributed multi-agent systems. However, existing MSBN inference methods do not allow agents to reason about their own problem sub-domains right after the initialization process. Extensive amount of inter-agent message passings are needed to calibrate each agent's local subnet into a correct prior marginal distribution. In this paper, we introduce the concept of prior marginal factors to facilitate this process. Based on the analysis of the prior marginal factors, minimum message passing is required during calibration. Furthermore, we have removed the requirement of maintaining a consistent junction tree (JT) during message calculation. Therefore, our marginal calibration algorithm guarantees that a prior marginal in each MSBN subnet is formed with greatly reduced communication and computational cost. Our preliminary experiments have confirmed the improved time efficiency of the proposed algorithm.
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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