LLM evolution and meta-optimization: 7. What nobody has done yet

Section 7 of 10 of the report "LLM evolution and meta-optimization" by Hiina (v1, updated 2026-10-27)
Report overview and contents: https://ai.hiina.space/reports/llm-evolution-and-meta-optimization.md
Previous: https://ai.hiina.space/reports/llm-evolution-and-meta-optimization/06-family-trees-the-instruments-exist-but-nobody.md
Next: https://ai.hiina.space/reports/llm-evolution-and-meta-optimization/08-five-experiments-cheapest-first.md

7. What nobody has done yet

These are gaps this review's searches found. "Not found" reflects the limits of a few dozen targeted searches, not proof of absence.

Full-tuple genomes. No work evolves architecture, data mix, hyperparameters, adapters and harness together, with trained weights inherited across generations. Every system evolves one layer: merge recipes, LoRA populations, PBT hyperparameters, or agent code. Data mix has no evolutionary treatment in anything found here.
Speedrun log as evolutionary search. The modded-nanogpt README is a dated, attributed, categorized record of 92 selections, with cross-track transfer and labelled AI-System entrants. No published analysis treats it as a fitness-landscape walk: gain by mutation category, feature survival, contributor networks, human versus agent contribution.
Price-equation and multilevel analysis of the Hugging Face ecosystem. Laufer et al. measure inheritance and drift of metadata traits but exclude merges and adapters. No one has partitioned trait change into selection and transmission terms, or into within-family and between-family components, using downloads or descendants as fitness.
Harness genealogy. Nothing systematic exists on the lineage of coding-agent harnesses or system prompts.
A multi-signal typed genealogy. Declared, structural, weight and behavioural evidence have never been combined with per-edge confidence, and there is no ground-truth benchmark for distillation edges.
Lineage distance versus trait transfer. Subliminal learning is reported as same-base versus different-base. Nobody has tested whether output-based or weight-based phylogenetic distance predicts graded transfer.
Lab and market ecology. No data-driven model treats labs, funding, talent mobility or architecture adoption as an evolutionary system; the only rigorous treatment is qualitative philosophy.
Permutation-aligned merging of independently pretrained LLMs. Demonstrated for vision networks only; nothing found for LLMs.
Independent replications. Learned optimizers at scale, ES beating GRPO beyond Countdown, and evolved merges beating hand-tuned ones all rest mainly on the proposing authors.
Swarms as colonies, and the selected-versus-inherited unit gap. No work maps multi-agent systems on shared weights to eusocial or clonal biology, or measures the persona-versus-weights mismatch.

Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space