Why this work is in the frame
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Bibliographic record
Abstract
Fast arc-reversal (FAR) was recently proposed as a new exact inference algorithm in discrete Bayesian networks (BNs), merging favourable features of Arc-reversal (AR) and Variable elimination (VE). AR constantly maintains a sub-BN structure when rendering a variable barren via arc reversals, often requiring more computational effort than VE, which sacrifices a sub-BN structure by directly eliminating a variable. It was formally established that FAR can recover a unique and sound sub-BN structure after consecutive variable eliminations. Experimental results on real-world benchmark networks empirically show an improvement in the average run-time and variance of FAR compared to AR. A novel method, called d-contraction , was suggested for graphically understanding FAR since FAR is not always the same as a sequence of arc reversals. Here, we extend this work by formally establishing that AR's sub-DAG is necessarily contained within FAR's sub-DAG. Unfortunately, neither FAR nor AR can guarantee the construction of minimal I-maps, although both methods may subsequently recover minimal I-mapness. Finally, it is shown how FAR improves Sum-Product network interpretability by relaxing a restriction on the elimination ordering used.
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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 | 0.001 | 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