Study: bandit-based LLM selectors beat embedding routers but lose to fixed models against adaptive opponents
Arastun Mammadli · hf · 2026-10-15
A paper from the University of Exeter and IBM Research, 'Choosing LLMs for Strategic Agent Interactions,' formalizes model selection under game-theoretic benchmarks. Current LLM routers optimize quality-cost trade-offs with no notion of an unknown, adapting opponent. The authors show that multi-armed bandit methods outperform embedding-based selectors, but still fall below a fixed-model baseline against adaptive opponents. Posted to arXiv (cs.AI, Oct 13, 2026).
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