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
A jigsaw puzzle solver reconstructs the original image from a given collection of non-overlapping image fragments using their color and shape information. In this paper we introduce new techniques for solving square jigsaw puzzles (with no prior knowledge of the initial image) that improves the accuracy of the state-of-the-art jigsaw puzzle solvers. While the current puzzle solving techniques are based on finding enhanced compatibility metrics across piece boundaries, we combine the existing techniques to achieve higher accuracy and robustness, i.e., our solver outperforms the known solvers even when the piece boundaries are imprecise. Unlike the most successful puzzle solvers that use greedy pairwise compatibility metrics among puzzle boundaries, we incorporate global information that enhances performance. As a step towards the future goal of developing an automated assembler for real-life corrupted image fragments or shredded documents, we examine puzzles that are corrupted by noise. Our proposed compatibility metrics shows robustness even in such scenarios.
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 | 0.001 |
| 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