Simulation of Graphical Models for Multiagent Probabilistic Inference
Why this work is in the frame
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Bibliographic record
Abstract
Multiply-sectioned Bayesian networks (MSBNs) extend Bayesian networks to graphical models for multiagent probabilistic reasoning. The empirical study of algorithms for manipulations of MSBNs (e.g., verification, compilation, and inference) requires experimental MSBNs. As engineering MSBNs in large problem domains requires significant knowledge and engineering effort, the authors explore automatic simulation of MSBNs. Due to the large domain over which an MSBN is defined and a set of constraints to be satisfied, a generate-and-test approach toward simulation has a high rate of failure. The authors present an alternative approach that treats the simulation process as a sequence of decisions. They constrain the space of each decision so that backtracking is minimized and the outcome is always a legal MSBN. A suite of algorithms that implements this approach is presented, and experimental results are shown.
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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 | 0.001 |
| 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