Google Research turns model routing into measurable diversity and stability metrics

burkov · x · 2026-07-21

A new Google Research paper studies how routers choose among multiple language models or agents.
The authors argue that accuracy and cost alone miss two issues: some models behave too similarly for routing to matter, and tiny wording changes can send the same request to different models.
They define two measurable quantities: one for behavioral diversity across models, and one for routing stability under meaning-preserving edits such as typos, paraphrases, or irrelevant additions.
Experiments suggest that a carefully selected set of fewer than ten models can preserve most useful diversity from much larger pools.
Nearest-neighbor routers can be accurate yet brittle, while description-based routers may be less accurate in some cases but more stable.

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