arXiv — Machine Learning · · 3 min read

RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing

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Computer Science > Machine Learning

arXiv:2606.18774 (cs)
[Submitted on 17 Jun 2026]

Title:RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing

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Abstract:We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at this https URL. Different from model-level response evaluation, RouteJudge focuses on router-level decision quality. For each user query, multiple routing strategies independently recommend candidate models under the same model pool and budget constraints. The selected model responses are then presented to users through anonymous pairwise comparisons, and the resulting user preferences are attributed back to the routing strategies behind the compared responses. Each evaluation record stores the query, routing decisions, model responses, preference labels, cost, latency, and task metadata, enabling preference-aware, cost-aware, and task-conditioned analysis of LLM routers. To support the continuous expansion of routing methods in RouteJudge, we further release ORBIT (Optimal Routing and Budgeted Inference Toolbox), a modular and extensible toolbox that standardizes the end-to-end workflow of LLM routing. ORBIT provides unified interfaces for benchmark loading, query representation, router implementation, budget-aware evaluation, and method comparison, allowing researchers to develop and evaluate routing algorithms under consistent protocols. It also serves as the submission and integration layer for RouteJudge: researchers can implement routing methods within ORBIT, validate them on existing routing benchmarks, and submit compatible routers for online preference-based evaluation. The code of ORBIT is available at this https URL.
Comments: Accepted by Pluralistic Alignment Workshop at ICML 2026
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2606.18774 [cs.LG]
  (or arXiv:2606.18774v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2606.18774
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Guannan Lai [view email]
[v1] Wed, 17 Jun 2026 07:35:10 UTC (1,613 KB)
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