Towards Rapid Redesign: Pattern-based Redesign Planning for Large-Scale and Complex Redesign Problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We have developed a decomposition-based rapid redesign methodology for large and complex computational redesign problems. While the overall methodology consists of two general steps: diagnosis and repair, in this paper we focus on the repair step in which decomposition patterns are utilized for redesign planning. Resulting from design diagnosis, a typical decomposition pattern solution to a given redesign problem indicates the portions of the design model necessary for recomputation as well as the interaction part within the model accountable for design change propagation. Following this, in this paper we suggest repair actions with an approach derived from an input pattern solution, to generate a redesign road map allowing for taking a shortcut in the redesign solution process. To do so, a two-stage redesign planning approach from recomputation strategy selection to redesign road map generation is proposed. An example problem concerning the redesign of a relief valve is used for illustration and validation.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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