RPID: Rust Programmable Interface for Domain-Independent Dynamic Programming
Bibliographic record
Abstract
In domain-independent dynamic programming (DIDP), a problem is formulated as a dynamic programming (DP) model and then solved by a general-purpose solver. In the existing software for DIDP, a model is defined using expressions composed of a predefined set of operations. In this paper, we propose the Rust Programmable Interface for DIDP (RPID), new software for DIDP, where a model is defined by Rust functions. We discuss the design of RPID and compare it with existing DP-based frameworks, including decision diagram-based (DD-based) solvers. In our experiments, RPID is up to hundreds of times faster than the existing DIDP implementation with the same models. In addition, new DIDP models, enabled by the flexibility of RPID, outperform existing models in multiple problem classes. We also show that the relative performance of RPID and existing DD-based solvers depends on problem class with, so far, no clear dominant solver technology.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".