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
Let G = (V,E) be a graph with vertex set V and edge set E. The k-coloring problem is to assign a color (a number chosen in {1,..., k}) to each vertex of G so that no edge has both endpoints with the same color. We present a new local search algorithm, called Variable Space Search (VSS), which we apply to the k-coloring problem. VSS extends the Formulation Space Search (FSS) methodology by considering sev-eral non equivalent formulations of a same problem, each one being associated with a set of neighborhoods and an objective function. The search moves from one formulation to another when it is blocked at a local optimum with a given formulation. The k-coloring problem is thus solved by combining different formulations of the problem which are not equivalent, in the sense that some constraints are possibly re-laxed in one search space and always satisfied in another. We show that the proposed algorithm improves on every local search used inde-pendently (i.e., with a unique search space), and is competitive with the currently best coloring methods, which are complex hybrid evolu-tionary algorithms. 1
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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