Group Maximum Differentiation Competition: Model Comparison with Few Samples
Why this work is in the frame
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Bibliographic record
Abstract
In many science and engineering fields that require computational models to predict certain physical quantities, we are often faced with the selection of the best model under the constraint that only a small sample set can be physically measured. One such example is the prediction of human perception of visual quality, where sample images live in a high dimensional space with enormous content variations. We propose a new methodology for model comparison named group maximum differentiation (gMAD) competition. Given multiple computational models, gMAD maximizes the chances of falsifying a "defender" model using the rest models as "attackers". It exploits the sample space to find sample pairs that maximally differentiate the attackers while holding the defender fixed. Based on the results of the attacking-defending game, we introduce two measures, aggressiveness and resistance, to summarize the performance of each model at attacking other models and defending attacks from other models, respectively. We demonstrate the gMAD competition using three examples-image quality, image aesthetics, and streaming video quality-of-experience. Although these examples focus on visually discriminable quantities, the gMAD methodology can be extended to many other fields, and is especially useful when the sample space is large, the physical measurement is expensive and the cost of computational prediction is low.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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