Mathematical Programming Models for Optimizing Partial-Order Plan Flexibility
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Bibliographic record
Abstract
A partial-order plan (POP) compactly encodes a set of sequential plans that can be dynamically chosen by an agent at execution time. One natural measure of the quality of a POP is its flexibility, which is defined to be the total number of sequential plans it embodies (i.e., its linearizations). As this criteria is hard to optimize, existing work has instead optimized proxy functions that are correlated with the number of linearizations. In this paper, we develop and strengthen mixed-integer linear programming (MILP) models for three proxy functions: two from the POP literature and a third novel function based on the temporal flexibility criteria from the scheduling literature. We show theoretically and empirically that none of the three proxy measures dominate the others in terms of number of sequential plans. Compared to the state-of-the-art MaxSAT model for the problem, we empirically demonstrate that two of our MILP models result in equivalent or slightly better solution quality with savings of approximately one order of magnitude in computation time.
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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) | 0.000 | 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