MoE模型的路由机制一直是个黑盒，DBES让开发者能真正量化专家是否在干专长的事。做MoE训练或微调的团队，可以用这套指标直接优化后训练效率，省资源又提效果，值得一试。
DBES 是一个针对混合专家模型（MoE）中专家专业化程度的系统评估框架，包含多领域基准和五个理论驱动的指标：路由专业化、归一化有效秩、领域隔离度、路由刚度评分和N-gram专家度。研究发现不同模型呈现不同专业化范式：Qwen系列表现出模块化专业化和高领域隔离，而DeepSeek和GLM采用分布式协作。更重要的是，通过DBES识别高专业化专家路径进行领域特定后训练，仅用15%的训练资源即可实现66%到94.48%的专业领域性能提升。这项工作首次提供了独立于准确率指标的专家专业化评估方法，为下一代MoE系统的设计和后训练优化提供了关键见解。
DBES: A Systematic Benchmark and Metric Suite for Evaluating Expert Specialization in Large-Scale MoEs
Expert specialization in Mixture-of-Experts (MoE) models remains poorly understood, with traditional evaluations conflating architectural load-balancing with functional specialization. We introduce DBES, a comprehensive diagnostic framework combining a multi-domain benchmark with five theoretically grounded metrics: Routing Specialization, Normalized Effective Rank, Domain Isolation, Routing Stiffness Score, and N-gram Expertise measures. Critical findings demonstrate distinct specialization paradigms across models: Qwen-series exhibit modular specialization with high domain isolation, while DeepSeek and GLM employ distributed collaboration. However, we emphasize that specialization is a diagnostic dimension, necessary but not sufficient for downstream performance. Most crucially, interventional evidence validates the actionability of these metrics: by using DBES to identify high-specialization expert paths during domain-specific post-training, we achieved 66% to 94.48% improvement in specialized domains with only 15% of original training resources, demonstrating that these diagnostic tools can be converted into concrete optimization operators. This work provides the first systematic methodology for evaluating expert specialization independently of accuracy metrics, offering crucial insights for the design and post-training optimization of next-generation MoE systems.