A class of representations for evolving graphs
Why this work is in the frame
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Bibliographic record
Abstract
This study introduces a parametrized family of representations for evolving graphs together with a benchmark function that is diagnostic of an important quality of a representation for graph evolution, its natural distribution of edge densities. The new benchmark function, the edge maximization function, is equivalent to the trivial OneMax function for some representations and represents a difficult problem for others. The utility of the edge maximization function lies in the fact that the edge density distribution in a graph is a critical parameter for evolving graphs and so performance of a representation on EdgeMax is diagnostic of an important aspect of its behavior. Three cases of the EdgeMax problem are examined using six different parameterizations of the new representation. The representations presented here are generative and so need not have any particular length. For each problem case and parametrization of the representation two lengths of chromosome are examined, one that is just long enough to solve the benchmark problem and one that is 10% longer. The EdgeMax is found to be diagnostic of representation properties.
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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 | 0.001 |
| 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