Selection should reward contribution
Accuracy matters, but a positive model-breeding lab should also select for contribution to the surrounding ecology. A candidate that makes outputs clearer, reduces cost, improves learning, or produces reusable artifacts may be worth promoting even when its raw task score is only modestly better.
Metric set
| Metric | Collection method |
|---|---|
| Capability transfer | Pre/post task without AI help. |
| Reuse rate | How often outputs become accepted artifacts. |
| Frugal gain | Memory, latency, and energy improvement. |
| Explanation score | Human review of clarity and correctness. |
| Portfolio contribution | New niche covered or fallback strengthened. |
FUNCTION positive_selection_score(candidate, incumbent)
score = benchmark_delta(candidate, incumbent)
score += learning_transfer_delta(candidate, incumbent)
score += artifact_reuse_delta(candidate, incumbent)
score += frugal_gain(candidate, incumbent)
score += niche_contribution(candidate)
RETURN score
END FUNCTIONLab rule
Do not let a leaderboard hide the practical reasons a model exists. Keep a separate benefit card for every candidate.
Source reports used for this guide
These reports are preserved verbatim in the site archive. The guide above is an editorial synthesis and may narrow, qualify, or reorganize claims from the source material.
Positive mutualism and governanceMutualist Persistence — Uploaded EditionGovernance synthesis · 15.7 KB
Model merging and adaptersModel Merging: Definitions, Taxonomy, Algorithms, Tooling, and Production Pipeline DesignEmerging practice · 35.7 KB
Evolutionary AIDesigning the Perfect Evolutionary AI System — Uploaded EditionEstablished and emerging methods · 46.5 KB