Visualizing High Dimensional Objective Spaces for Multi-objective Optimization: A Virtual Reality Approach
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Bibliographic record
Abstract
This paper presents an approach for constructingvisual representations of high dimensional objective spacesusing virtual reality. These spaces arise from the solutionof multi-objective optimization problems with more than 3objective functions which lead to high dimensional Pareto frontswhich are difficult to use. This approach is preliminarily investigatedusing both theoretically derived high dimensional Paretofronts for a test problem (DTLZ2) and practically obtainedobjective spaces for the 4 dimensional knapsack problem viamulti-objective evolutionary algorithms like HLGA, NSGA, andVEGA. The expected characteristics of the high dimensionalfronts in terms of relative sizes, sequencing, embedding andasymmetry were systematically observed in the constructedvirtual reality spaces.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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