Flexibility of planar graphs—Sharpening the tools to get lists of size four
Bibliographic record
Abstract
A graph where each vertex v has a list L(v) of available colors is L-colorable if there is a proper coloring such that the color of v is in L(v) for each v. A graph is k-choosable if every assignment L of at least k colors to each vertex guarantees an L-coloring. Given a list assignment L, an L-request for a vertex v is a color c∈L(v). In this paper, we look at a variant of the widely studied class of precoloring extension problems from Dvořák, Norin, and Postle (J. Graph Theory, 2019), wherein one must satisfy “enough”, as opposed to all, of the requested set of precolors. A graph G is ɛ-flexible for list size k if for any k-list assignment L, and any set S of L-requests, there is an L-coloring of G satisfying ɛ-fraction of the requests in S. It is conjectured that planar graphs are ɛ-flexible for list size 5, yet it is proved only for list size 6 and for certain subclasses of planar graphs. We give a stronger version of the main tool used in the proofs of the aforementioned results. By doing so, we improve upon a result by Masařík and show that planar graphs without K4− are ɛ-flexible for list size 5. We also prove that planar graphs without 4-cycles and 3-cycle distance at least 2 are ɛ-flexible for list size 4. Finally, we introduce a new (slightly weaker) form of ɛ-flexibility where each vertex has exactly one request. In that setting, we provide a stronger tool and we demonstrate its usefulness to further extend the class of graphs that are ɛ-flexible for list size 5.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".