RIDGE: Role-Adaptive Routing for LLM-based Multi-Agent Systems

LLM-based multi-agent systems (MASs) can support complex iterative workflows. However, they commonly face a challenging trade-off between performance and cost. Existing systems typically assign a fixed model to each agent role, ignoring variations in subtask difficulty. Furthermore, current LLM routers are mainly designed for single-turn conversations and rely on paired model outputs that are difficult to obtain in multi-agent workflows. To address these issues, we propose RIDGE, a lightweight router that dynamically identifies model capability boundaries during task execution. It combines online Critic evaluations with forensic attribution of failed tasks to distill a strong model’s assessment of execution quality, failure risk, and failure-critical steps into a role-conditioned hypernetwork. The hypernetwork generates role-specific prediction heads that separately estimate the potential execution states of fast and strong models from the current task semantics. An online MemoryBank further supports routing decisions. On SWE-bench Lite and WebArena-Verified, RIDGE matches the success rate of a static strong-model baseline while reducing strong-model token usage by up to 41.6%. It also routes 76.19% of failure-critical steps to the strong model, compared with 21.99% for OmniRouter and 15.06% for RouteLLM.