Robust Batch-Level Query Routing for Large Language Models under Cost and Capacity Constraints

• URL: http://arxiv.org/abs/2603.26796v1
• Date: Wed, 25 Mar 2026 22:24:11 GMT
• Title: Robust Batch-Level Query Routing for Large Language Models under Cost and Capacity Constraints
• Abstract summary: We study the problem of routing queries to large language models (LLMs)<n>We propose a batch-level, resource-aware routing framework that jointly optimize assignment for each batch.<n> Experiments on two multi-task benchmarks show that improves accuracy by 1-14% on non-robust counterparts.
• Score: 18.52522897906341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of routing queries to large language models (LLMs) under cost, GPU resources, and concurrency constraints. Prior per-query routing methods often fail to control batch-level cost, especially under non-uniform or adversarial batching. To address this, we propose a batch-level, resource-aware routing framework that jointly optimizes model assignment for each batch while respecting cost and model capacity limits. We further introduce a robust variant that accounts for uncertainty in predicted LLM performance, along with an offline instance allocation procedure that balances quality and throughput across multiple models. Experiments on two multi-task LLM benchmarks show that robustness improves accuracy by 1-14% over non-robust counterparts (depending on the performance estimator), batch-level routing outperforms per-query methods by up to 24% under adversarial batching, and optimized instance allocation yields additional gains of up to 3% compared to a non-optimized allocation, all while strictly controlling cost and GPU resource constraints.

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