Exploring A Better Way to Constraint Propagation Using Naked Pair
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
Sudoku is an NP-complete problem therefore developing various efficient algorithms is crucial. This paper presents an enhanced approach to solving sudoku puzzles by improving on recursive backtracking with constraint propagation and bitmask, focusing on the implementation of the naked pair technique. This research aims to add constraints to reduce the backtracking steps in solving sudoku using naked pairs, which is a technique that eliminates candidates of a cell from other cells in the same row, column or sub-grid when two cells share the same candidate sets. This research uses a dataset of 1 million sudoku puzzlers from Kaggle to evaluate the proposed solver’s performance. The solver first processes the puzzle into bitmask vectors then uses the singles and naked pairs technique to minimize the candidates, and then uses depth-first search to backtrack and solve the puzzle. As a result, the naked pair strategy slightly increases the total steps, and it significantly reduces the computation of depth-first search steps, making the solver potentially more efficient in solving difficult puzzles.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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