Towards Rapid Redesign: Decomposition Patterns 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
In an effort to develop a decomposition-based rapid redesign methodology, this paper introduces the basis of such a methodology on decomposition patterns for a general redesign problem that is computation-intensive and simulation-complex. In particular, through pattern representation and quantification, this paper elaborates the role and utility of the decomposition patterns in decomposition-based rapid redesign. In pattern representation, it shows how a decomposition pattern can be used to capture and portray the intrinsic properties of a redesign problem. Thus, through pattern synthesis, the collection of proper decomposition patterns allows one to effectively represent in a concise form the complete body of redesign knowledge covering all redesign problem types. In pattern quantification, it shows how a decomposition pattern can be used to extract and convey the quantum information of a redesign problem using the pattern characteristics. Thus, through pattern analysis, the formulation of an index incorporating two redesign metrics allows one to efficiently predict in a simple manner the amount of potential redesign effort for a given redesign problem. This work represents a breakthrough in extending the decomposition-based solution approach to computational redesign problems.
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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 | 0.006 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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