FlexRouter is a routing framework for large language models that explicitly models model complementarity to maximize answer coverage. It formulates routing as a coverage-oriented subset selection problem and uses Determinantal Point Processes to capture both competence and redundancy. During inference, a greedy strategy based on marginal log-determinant gains allows the router to adaptively determine subset sizes without a fixed budget, achieving higher coverage with lower redundancy on the RouterEval benchmark.
By Wang Wei, Harry Yang, Tiankai Yang, Samyadeep Basu, Hongjie Chen, Andy Zhao, Franck Dernoncourt, Ryan A. Rossi, Hoda Eldardiry
arXiv:2610.18566v1 Announce Type: new
Abstract: Large language model (LLM) routers reduce serving cost by choosing which model to call and how many output tokens to grant it for each query. Existing...
By Yichi Zhang, Zhenxiang Huang, Yuyang Zhang, Yuchen Yang
arXiv:2608. 08265v1 Announce Type: new Abstract: Oracle routing measures how much a pool of language models could gain from per-query selection, but the diagnostic has two flaws: testing against a best fixed model selected on the same examples invalidates paired inference, and a full-information oracle sees outcomes no deployable router observes.
By Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Md Najmus Swaqeeb
arXiv:2606. 27288v1 Announce Type: new Abstract: Multi-model LLM systems such as routing, voting, cascades, fusion, and mixture-of-agents are used to beat single-model accuracy.
By Josef Chen
arXiv:2609.38006v1 Announce Type: new
Abstract: Running a language model locally offers advantages in privacy, latency, and cost, but local hardware fits only small models, which are less capable tha...
By Kenan Alkiek, Moontae Lee, David Jurgens, V. G. Vinod Vydiswaran
arXiv:2512. 03057v2 Announce Type: replace-cross Abstract: We study distribution-free risk control for model routing, motivated by large language model reasoning.
By Hao Zeng, Bingyi Jing