AI RESEARCH
Unsolvability Ceiling in Multi-LLM Routing: An Empirical Study of Evaluation Artifacts
arXiv CS.AI
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ArXi:2605.07395v1 Announce Type: cross Efficient routing across multiple LLMs enables cost-quality tradeoffs by directing queries to the cheapest capable model. Prior work attributes routing headroom to an "unsolvability ceiling", queries no model in the pool can solve. We present a large-scale study of multi-tier LLM routing with 206,000 query-model pairs across six benchmarks (MMLU, MedQA, HumanEval, MBPP, Alpaca, ShareGPT) using the Gemma 4 and Llama 3.1 families.