LLM Router Evaluation and Performance

arXiv:2605.11603v1 Announce Type: new Abstract: The growing deployment of large language models (LLMs) makes per-request routing essential for balancing response quality and

<!-- SC_OFF --><div class="md"><p>Enterprises running always-on AI agents keep hitting the same tradeoff. Send every task to a frontier model and the bill climbs fast. Build

arXiv:2608.28475v1 Announce Type: new Abstract: When a multi-agent system changes its team, it also changes the messages and final answer it produces, so an end-to-end accuracy

arXiv:2605.07112v1 Announce Type: new Abstract: Agentic AI systems that invoke external tools are powerful but costly, leading developers to default to large models and overspend

arXiv:2605.04454v1 Announce Type: new Abstract: Alignment evaluation in machine learning has largely become evaluation of models. Influential benchmarks score model outputs under

arXiv:2610.00025v1 Announce Type: new Abstract: Agent harnesses increasingly want to run small language models (SLMs) on the microtasks around a frontier large language model

arXiv:2605.08828v1 Announce Type: new Abstract: Large language model agents increasingly operate through environment-facing scaffolds that expose files, web pages, APIs, and logs.

arXiv:2608.28241v1 Announce Type: new Abstract: The rapid expansion of reusable skill repositories makes skill routing a critical capability for large language model (LLM) agents.