Harnessing Design Space: A Similarity-Based Exploration Method for Generative Design
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
Working with multiple alternatives is a central activity in design; therefore, we expect computational systems to support such work. There is a need to find out the tool features supporting this central activity so that we can build new systems. To explore such features, we propose a method that aims to enable interaction with a large number of design alternatives by similarity-based exploration. Using existing data analysis and visualization techniques adopting similarity-based search, we formalized the method and its elements by focusing on systematic filtering, clustering, and choosing alternatives. We present a scenario on developing conceptual designs for a residential apartment to illustrate how the method can be applied, as well as to reveal the limitation of current tools and the potential interactive clustering and filtering features for the new systems coupled with parametric design.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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