Solving design problems in a logic-based visual design environment
Bibliographic record
Abstract
Designs of complex objects often include parameters which, when given values, provide a realisation of a specific example of the design. Such parametrised designs, therefore, actually represent families of objects. In order to provide the programming capabilities necessary for building such parametrised designs, some computer-aided design systems include programming languages or provide interfaces to them. This creates a sharp division in the design process between designer and programmer. To address this discontinuity, a Language for Structured Design (LSD) has been proposed as an extension to a visual logic programming language. In LSD, design components and operations on them are homogeneously represented in one language. Here we report on another advantage of the LSD approach; namely, that visual logic programming, used as the engine to drive the parametrised assembly of objects, also provides powerful symbolic problem-solving capability. This allows the designer/programmer to work at a higher level, giving descriptive rather than prescriptive specifications of a design. Hence LSD integrates problem solving, synthesis, and modeling in a single homogeneous programming/design environment. We demonstrate the problem-solving capabilities of LSD using the masterkeying problem.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".