Retrieving coherent evidence subgraphs is critical for Knowledge Base Question Answering (KBQA). Existing paradigms often treat facts independently, rely on biased heuristics, or employ myopic search, failing to optimize collective subgraph utility. In this paper, we propose **COSMOS** (**C**onnectivity-**O**riented **S**ubmodular **M**aximization for **O**ptimal **S**ubgraph Retrieval), a unified framework that formalizes evidence retrieval as a constrained submodular maximization problem. This formulation mathematically captures the trade-off between information relevance and structural complexity. To tractably solve this combinatorial challenge, COSMOS employs a decompose-and-conquer strategy, which first performs a seed-guided greedy expansion to maximize local semantic utility, followed by a topology-aware component aggregation to bridge disjoint evidence clusters via Maximum Spanning Tree aggregation. Guided by theoretical bounds, we introduce Structure-Aware Contrastive Tuning to align semantic space with KG topology. Experimental results on WebQSP, CWQ, and M³GQA benchmarks demonstrate that COSMOS achieves state-of-the-art performance.

Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often incur significant costs and face challenges in dynamic LLM selection. Current LLM routing methods effectively reduce overhead in single-agent scenarios by customizing LLM selection for each query, but they overlook the critical decisions regarding collaboration modes and agent roles in MAS. In response to this challenge, we first introduce the problem of Multi-Agent System Routing (MASR), which integrates all components of MAS into a unified routing framework. Toward this goal, we propose MasRouter, the first high-performing, cost-effective, and inductive MASR solution. MasRouter employs collaboration mode determination, role allocation, and LLM routing through a cascaded controller network, progressively constructing a MAS that balances effectiveness and efficiency. Extensive experiments demonstrate that MasRouter is (1) high-performing, achieving a 1.8 improvement over the state-of-the-art method on MBPP; (2) economical, reducing overhead by up to 52.07 compared to SOTA methods on HumanEval; and (3) plug-and-play, seamlessly integrating with mainstream MAS frameworks, reducing overhead by 17.21 via customized routing.