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New research from Google DeepMind on effective model routing. LLM routers get judged on accuracy and cost. Both can look great while the rou…
New research from Google DeepMind on effective model routing. LLM routers get judged on accuracy and cost. Both can look great while the router is meaningless. If every model in your society responds
New research from Google DeepMind on effective model routing. LLM routers get judged on accuracy and cost. Both can look great while the router is meaningless. If every model in your society responds the same way, routing is vacuous, you get the same answers no matter where queries land. And if paraphrases of one query get sent to different experts, the router is unstable, so its assignments carry no real signal. The argument is that two properties decide whether routing means anything. Namely, behavioural differentiation of the actors and stability under surface-form rewrites. And they both are orthogonal to task accuracy. What's the practical take here? If you use mixture-of-agents or model routing, your overall accuracy can hide a router that operates over a redundant society or assigns queries inconsistently. These two checks catch the routers that look good and do nothing. Paper: https://arxiv.org/abs/2607.09197 Learn to build effective AI agents in our academy: https://academy.dair.ai/
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Source: DAIR.AI (X) | 2026-07-14