arXiv Computation and Language
Aug 25

Most of the LLM routing gap is task type

The paper investigates why large‑language‑model (LLM) routers—systems that select the best model for each query—often fail to outperform a single best model. By evaluating 14 models on 294 questions across seven task types and three languages, the authors find that a simple static mapping of task type to model improves 21 of the 29 questions that routing could potentially solve, and that learned routers do not significantly exceed this performance. The study highlights that most routing gains stem from task‑type specialization rather than complex learned decision rules.

By Janghoon Lee
arXiv AI
Oct 5

Dynamic LLM Routers are Often Misguided

Dynamic LLM routers aim to reduce inference costs by directing each query to the cheapest capable model. In a study of six commercial routers across 14 settings and eight task categories, none surpassed a simple random router that selects between two well-chosen models at the same cost, with some underperforming by over 10 percentage points. The authors identify four common patterns—difficulty blindness, length reversal, semantic matching, and roster suboptimality—that explain this gap and propose a new evaluation method and a simple two-model router that mitigates these patterns, though its advantage over random routing remains modest.

By Sam Wang, Julia White, Sahibzada Allahyar, Dhruv Atreja, Urchade Zaratiana, Kelton Zhang
arXiv Computer Vision
Oct 27

Multi-Agent Relay Thinking

arXiv:2610.35219v1 Announce Type: new Abstract: Existing multi-agent collaboration (MAC) methods have primarily explored a group thinking collaboration topology, where agents independently generate s...

By Xu Liu, Jungang Qiao, Guikun Chen, Xiaojuan Qi, Wenguan Wang