DeepMind casts LLM routing as a Pandora's Box problem, cutting inspection cost 96%
rohanpaul_ai · x · 2026-08-22
A new Google DeepMind paper, "Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation," tackles a practical paradox: routing is supposed to save compute, but deciding where to route costs compute itself. The idea: spend more on model selection only when the better decision is worth the extra cost.
The decision is framed as a Pandora's Box problem: every specialist gets a cheap, noisy score first, and the router pays for a stronger estimate only when the expected value of that extra information exceeds its cost.
Results: across MATH, RAG, and EmbedLLM, Pandora's Router had the lowest or tied-lowest combined routing regret plus inspection cost on average across tested cost settings. On EmbedLLM with 100+ routing targets, average inspection cost was 0.075 versus 1.986 for always running the expensive estimator, while routing regret also fell from 0.370 to 0.311. Paper: arxiv.org/abs/2608.20316.
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