Decomposing Evolutionary Mixture-of-LoRA Architectures: The Routing Lever, the Lifecycle Penalty, and a Substrate-Conditional Boundary
Ramchand Kumaresan

TL;DR
This paper decomposes an evolutionary mixture-of-LoRA system into three factors, analyzing their individual contributions and effects on model performance, revealing the importance of routing and substrate conditions.
Contribution
It introduces a detailed decomposition of the evolutionary mixture-of-LoRA architecture and evaluates the impact of each component on model performance.
Findings
The routing rewrite accounts for the entire performance improvement over the static baseline.
The full evolutionary system shows a modest but not statistically significant improvement over the baseline.
The substrate-conditional regime boundary indicates that evolutionary search is effective only when adapters are pre-aligned to the task.
Abstract
We decompose an evolutionary mixture-of-LoRA system on a from-scratch ~150M-parameter widened-D substrate (D=1536, V=32000; D/V approx 0.048; the "widened-1536" substrate) into three factors -- a router rewrite (parallel sigmoid gate with learnable per-adapter floor and bounded temperature anneal, fed post-stack hidden states rather than token-embedding means), a per-domain leave-one-out evaluation scope, and a lifecycle of death plus alpha-blend inheritance plus SVD mutation plus slot reallocation -- and report a 5-of-8 partial 2^3 factorial run at n=3 seeds and 25000 adaptation steps per cell. The attribution chain is sharp on this substrate: the router rewrite carries the entire +0.0426 nat balanced log-PPL improvement (Delta = log PPL_ref - log PPL_test, positive = improvement; t=12.86, p=0.006) attributed to "the full evolutionary system vs the static B3 baseline"; the headline…
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