Adding local edge mobility to graph evolution
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
This study extends an earlier generative representation for the evolution of graphs to include a local reconfiguration operator, the hop operator, and a null operator. The hop operator is shown to be more effective in evolving graphs with a particular geometric character (eccentricity sequence). The null operator permits evolution to select the number of active commands used, avoiding a problem with needing to tune a “gene length” parameter. The representation is parametrized by the probability of each command appearing in initial populations and during mutation. A parameter study leads to a number of rules of thumb for using the new representation and it is found that the number of failures to find a solution, in 3000 attempts, varies from 17 in 3000 as the parameters are changed. The representation is tested on 100 instances of the eccentricity sequence matching problem. Use of the null operator has the beneficial side effect of reducing observed variation in problem difficulty.
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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 | 0.003 | 0.005 |
| 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) | 0.001 | 0.006 |
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