ROUTEAUDIT: Interaction-Aware Identification for Budgeted Multi-Verifier Routing

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

arXiv:2605.01710v1 Announce Type: new Abstract: AI products often route requests through version aliases, service tiers, tool choices, regional endpoints, fallback rules, or

arXiv:2603.18043v1 Announce Type: cross Abstract: Multi-agent LLM systems delegate tasks across trust boundaries, but current protocols do not govern delegation under unverifiable

arXiv:2605.05657v1 Announce Type: new Abstract: Multi-agent LLM systems for code generation face a fundamental routing problem: the optimal orchestration topology depends on the

arXiv:2608.28011v1 Announce Type: new Abstract: Trajectory-level credit assignment can localize which module of a tool-using LLM agent causes failures using only verifiable

arXiv:2604.00421v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely

arXiv:2606.15822v1 Announce Type: new Abstract: AI agents increasingly access external models, tools, and services through Agentic Routing Infrastructure (ARI) to manage the