Solving puzzles using knowledge-based automation: biomimicry of human solvers
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
Abstract The human brain’s remarkable efficiency in solving puzzles through pictorial information processing serves as a valuable inspiration for computational puzzle solving. In this study, we present a nucleation algorithm for automated puzzle solving, developed based on statistical analysis of an empirical database. This algorithm effectively solves puzzles by choosing pieces with infrequent and iridescent edges as nucleation centers, followed by the identification of neighboring pieces with high resemblances from the remaining puzzle pieces. For the 8 different pictures examined in this study, both empirical data and computer simulations consistently demonstrate a power-law relationship between solving time and the number of puzzle pieces, with an exponent less than 2. We explain this relationship through the nucleation model and explore how the exponent is influenced by the color pattern of the puzzle picture. Moreover, our investigation of puzzle-solving processes reveals distinct principal pathways, akin to protein folding behavior. Our study contributes to the development of a cognitive model for human puzzle solving and color pattern recognition.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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