TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing
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Computer Science > Machine Learning
arXiv:2605.18859 (cs)
[Submitted on 14 May 2026]
Title:TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing
Authors:Pei Yang, Wanyi Chen, Tongyun Yang, Pengbin Feng, Jiarong Xing, Wentao Guo, Yuhang Yao, Yuhang Han, Hanchen Li, Xu Wang, Zeyu Wang, Jie Xiao, Anjie Yang, Liang Tian, Lynn Ai, Eric Yang, Tianyu Shi
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Abstract:LLM routing matters most in long-horizon applications such as coding agents, deep research systems, and computer-use agents, where a single user request triggers many model calls. Routing each call to the cheapest sufficient model can cut costs without sacrificing quality, yet existing router benchmarks evaluate routers only on one-shot prompts. They never expose the router-visible prefix at an intermediate agent step, never test whether a cheaper replacement preserves downstream task success, and often rely on online LLM judges at evaluation time. We introduce TwinRouterBench, a step-level routing benchmark with two tracks. The static track provides 970 router-visible prefixes from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench, each paired with an execution-verified target tier estimated under a released downgrade-and-cascade protocol; scoring is deterministic arithmetic over tier labels, trajectory membership, and token costs, with no online evaluator-side LLM judge. The dynamic track supplies a harness that runs routers on the full 500-case SWE-bench Verified suite; in this paper we report a 100-case held-out evaluation disjoint from the static SWE supervision split. At each LLM call the router selects a concrete model from a locked pool, and success is measured by official task resolution and realized API spend. The two tracks support fast offline iteration followed by end-to-end validation under live agent execution. Code and data are available at this https URL.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.18859 [cs.LG] |
| (or arXiv:2605.18859v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18859
arXiv-issued DOI via DataCite
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View a PDF of the paper titled TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing, by Pei Yang and 16 other authors
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