On Weak Flexibility in Planar Graphs
Bibliographic record
Abstract
Recently, Dvořák, Norin, and Postle introduced flexibility as an extension of list coloring on graphs [JGT 19']. In this new setting, each vertex $v$ in some subset of $V(G)$ has a request for a certain color $r(v)$ in its list of colors $L(v)$. The goal is to find an $L$ coloring satisfying many, but not necessarily all, of the requests. The main studied question is whether there exists a universal constant $ε>0$ such that any graph $G$ in some graph class $\mathcal{C}$ satisfies at least $ε$ proportion of the requests. More formally, for $k > 0$ the goal is to prove that for any graph $G \in \mathcal{C}$ on vertex set $V$, with any list assignment $L$ of size $k$ for each vertex, and for every $R \subseteq V$ and a request vector $(r(v): v\in R, ~r(v) \in L(v))$, there exists an $L$-coloring of $G$ satisfying at least $ε|R|$ requests. If this is true, then $\mathcal{C}$ is called $ε$-flexible for lists of size $k$. Choi et al. [arXiv 20'] introduced the notion of weak flexibility, where $R = V$. We further develop this direction by introducing a tool to handle weak flexibility. We demonstrate this new tool by showing that for every positive integer $b$ there exists $ε(b)>0$ so that the class of planar graphs without $K_4, C_5 , C_6 , C_7, B_b$ is weakly $ε(b)$-flexible for lists of size $4$ (here $K_n$, $C_n$ and $B_n$ are the complete graph, a cycle, and a book on $n$ vertices, respectively). We also show that the class of planar graphs without $K_4, C_5 , C_6 , C_7, B_5$ is $ε$-flexible for lists of size $4$. The results are tight as these graph classes are not even 3-colorable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".