The evolution of swarm grammars- growing trees, crafting art, and bottom-up design
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
We presented swarm grammars as an extension of Lindenmayer systems. Instead of applying a single ('turtle') agent to convert linear strings into 3D structures, we use a swarm of agents "which navigate in 3D space and-as a side effect-place structural building blocks into their environment. The swarm grammars are used to specify how the setup of agent types changes over time. Additional agent parameters determine the agents' behaviors and their interaction dynamics. Both the grammar rules and the agent parameters are evolvable and can change over time-either automatically at replication and collision events among the agents, or triggered by external 'tinkering' from a supervising breeder. When swarm grammars are applied to concrete problems, constraints on the developmental processes as "well as on the emerging structures may provide the basis for an automatic evolutionary algorithm.
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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 | 0.000 | 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