Coevolving deep hierarchies of programs to solve complex tasks
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
Scaling genetic programming to organize large complex combinations of programs remains an under investigated topic in general. This work revisits the issue by first demonstrating the respective contributions of coevolution and diversity maintenance. Competitive coevolution is employed to organize a task in such a way that the most informative training cases are retained. Cooperative coevolution helps discover modularity in the solutions discovered and, in this work, is fundamental to constructing complex structures of programs that still execute efficiently (the policy tree). The role of coevolution and diversity maintenance is first independently established under the task of discovering reinforcement learning policies for solving Rubik's Cubes scrambled with 5-twists. With this established, a combined approach is then adopted for building large organizations of code for representing policies that solve 5 to 8-twist combinations of the Cube. The resulting 'deep' policy tree organizes hundreds of programs to provide optimal solutions to tens of millions of test cube configurations.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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