Index of everything on this site: https://ai.hiina.space/index.md
Taken together, the evidence says biology is most useful for LLMs as a rule about where search works, not as a metaphor. Mutation and crossover pay off in low-dimensional recipe spaces anchored to one ancestor. Gradients pay off when horizons are short. Evolution and archives take over once the objective spans a lifetime or must be run as code. Lineage is a measurable constraint, not a figure of speech: shared initialization gates merging, task arithmetic and hidden trait transfer, while text crosses every boundary. That makes the question "who distilled from whom" a different question from "who descends from whom", and it is why a credible family tree needs typed edges.
The other shift is in where the open problems sit. The algorithmic pieces are built: merge evolution, LoRA populations, ES at billion scale, learned ES operators, self-modifying agent archives with clade-level selection. What is missing is mostly measurement of evolutionary processes that already run in public, namely the speedrun log, the Hugging Face graph and harness forks, plus the step of evolving more than one layer of the stack at once. Both are within reach of one person with a single consumer GPU, and the first two experiments above need no GPU at all.
Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space