TOWARDS A FRAMEWORK FOR INTUITIVE PROGRAMMING OF CELLULAR AUTOMATA
Why this work is in the frame
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Bibliographic record
Abstract
The ability to obtain complex global behaviour from simple local rules makes cellular automata an interesting platform for massively parallel computation. However, manually designing a cellular automaton to perform a given computation can be extremely difficult, and automated design techniques such as genetic programming have their limitations because of the absence of human intuition. In this paper, we propose elements of a framework whose goal is to make the manual synthesis of cellular automata rules exhibiting desired global characteristics more programmer-friendly, while maintaining the simplicity of local processing elements. Although many of the framework elements that we describe here are not new, we group them into a consistent framework and show that they can all be implemented on a traditional cellular automaton, which means that they are merely more human-friendly ways of describing simple cellular automata rules, and not foreign structures that require changing the traditional cellular automaton model.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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