Constrained Optimization with Partial CP-Nets
Why this work is in the frame
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Bibliographic record
Abstract
The Conditional Preference Network (CP-net) is a graphical tool for representing and reasoning about user's conditional ceteris paribus preference statements. In case the user provides partial preferences, we get a partial CP-net. In this paper, we propose a novel algorithm, that we call Search-Partial-CP, to find the Pareto optimal outcomes with respect to an acyclic partial CP-net and a set of hard constraints. Search-Partial-CP is a backtrack search algorithm that utilizes the topological order of the related Directed Acyclic Graph (DAG) to order the variables, as well as the topological order of the partial preferences to order the values, during the instantiation process. Search-Partial-CP can significantly reduce the search space by pruning every infeasible or dominated outcome. We present and discuss the formal properties of Search-Partial-CP.
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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 | 0.000 | 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 it