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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".