Narrowing the Complexity Gap for Colouring (<i>C<sub>s</sub>, P<sub>t</sub></i>)-Free Graphs
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
For a positive integer |$k$| and graph |$G=(V,E)$|, a |$k$|-colouring of |$G$| is a mapping |$c: V\rightarrow \{1,2,\ldots ,k\}$| such that |$c(u)\neq c(v)$| whenever |$uv\in E$|. The |$k$|-Colouring problem is to decide, for a given |$G$|, whether a |$k$|-colouring of |$G$| exists. The |$k$|-Precolouring Extension problem is to decide, for a given |$G=(V,E)$|, whether a colouring of a subset of |$V$| can be extended to a |$k$|-colouring of |$G$|. A |$k$|-list assignment of a graph is an allocation of a list—a subset of |$\{1,\ldots ,k\}$|—to each vertex, and the List |$k$|-Colouring problem is to decide, for a given |$G$|, whether |$G$| has a |$k$|-colouring in which each vertex is coloured with a colour from its list. We consider the computational complexity of these three decision problems when restricted to graphs that do not contain a cycle on |$s$| vertices or a path on |$t$| vertices as induced subgraphs (for fixed positive integers |$s$| and |$t$|). We report on past work and prove a number of new NP-completeness results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.002 |
| 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 it