A Generalized Loop Correction Method for Approximate Inference in\n Graphical Models
Why this work is in the frame
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Bibliographic record
Abstract
Belief Propagation (BP) is one of the most popular methods for inference in\nprobabilistic graphical models. BP is guaranteed to return the correct answer\nfor tree structures, but can be incorrect or non-convergent for loopy graphical\nmodels. Recently, several new approximate inference algorithms based on cavity\ndistribution have been proposed. These methods can account for the effect of\nloops by incorporating the dependency between BP messages. Alternatively,\nregion-based approximations (that lead to methods such as Generalized Belief\nPropagation) improve upon BP by considering interactions within small clusters\nof variables, thus taking small loops within these clusters into account. This\npaper introduces an approach, Generalized Loop Correction (GLC), that benefits\nfrom both of these types of loop correction. We show how GLC relates to these\ntwo families of inference methods, then provide empirical evidence that GLC\nworks effectively in general, and can be significantly more accurate than both\ncorrection schemes.\n
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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