The article compares seven open decision models with a generative large language model (LLM) in a hands‑on repository called Jev. It evaluates each model based on routing accuracy, latency, and output reliability. The comparison provides practical insights into how these models perform in real‑world scenarios.
By Lambert Leong
Using mathematical optimization to solve a pickup-and-delivery problem with time windows. The post “Los Movimientos”: The Routing Problem That Nearly Broke My Spirit appeared first on Towards Data Science .
By Luis Fernando Pérez Armas
COMPASS is an agentic router that uses a capability-oriented model profiling approach to select large language models (LLMs) efficiently. It follows a routing recipe, gathers performance and cost evidence through tools, and can inspect candidate answers before making a selection. In evaluations on LLMRouterBench and DeepSWE, COMPASS improves accuracy while reducing model‑call cost compared to specialized and trained routing baselines, and it can adapt to new models without retraining.
By Amin Rezaei, Pouya Parsa
OpenRouter advertises automatic fallback and cost‑effective routing to the best backend provider for a single API endpoint. However, Mohamed Moustafa highlights that different providers use varied serving software, optimizations, and capabilities, leading to inconsistent behavior across the same endpoint. Users can mitigate this by specifying a provider with the provider.only option and checking available providers via the /endpoints method.
arXiv:2606. 18774v1 Announce Type: new Abstract: We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://routejudge.
By Guannan Lai, Haoran Hu, Han-Jia Ye