Structural Entropy Guided Capability-Aware Expert Routing

Large language model services increasingly rely on heterogeneous pools of experts, but selecting the right expert requires query-level capability understanding while querying every model is prohibitively expensive. We introduce SEER (Structural Entropy Guided Capability-Aware Expert Routing), a pre-answer router that separates routing into structure for search and language for choice. Structure for search organizes train-only query–expert outcomes as a semantic graph, partitions the graph by minimizing two-dimensional structural entropy, and derives interpretable community-level expert priors. For each incoming query, soft community assignment and local memory correction export a compact Top-$3$ candidate set. Language for choice then uses a to analyze the query's required capabilities and generate capability-conditioned retrieval queries. Constrained retrieval provides historical questions with anonymous route outcomes, from which a chooses one candidate without inspecting current expert responses. The two policies are independently optimized with Group Relative Policy Optimization. SEER combines interpretable structural memory with evidence-grounded language decision making while invoking only the selected expert for the final answer. We evaluate SEER across 20 benchmarks with both open-source and closed-source expert pools.