Nonbinary Constraint Satisfaction: From the Dual to the Primal
Bibliographic record
Abstract
Non binary constraints have recently been studied quite ex-tensively since they represent real life problems very natu-rally. Specifically, extensions to binary arc consistency into generalised arc consistency (GAC), and forward checking that incorporates a limited amount of GAC have been pro-posed, to handle non-binary constraints directly. Enforc-ing arc consistency on the dual encoding has been shown to strictly dominate nforcing GAC on the primal encoding. More recently, modifications to dual arc consistency have ex-tended these results to dual encodings that are based on the construction of compact onstraint coverings, that retain the completeness of the encodings, while using a fraction of the space. In this paper we present results that combine the en-forcement of arc consistency in these covering based dual en-codings, with performing forward checking based search in the primal encoding. We demonstrate how this new scheme can be shown to strictly dominate standard non-binary for-ward checking, while being able to efficiently enforce ex-tremely high levels of consistency.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".