解决模型库持续扩容下的智能路由难题，提升专家选择效率。
Continual Model Routing in Evolving Model Hubs
- 基于检查点锚定与结构化回放的对比嵌入方法
- 在2000+模型上实现模型、类别、领域三类准确率显著提升
- 适合动态更新的AI模型库场景，如多任务推理系统
AI模型库提供了大量预训练模型，支持即插即用的专家混合系统，但其快速扩展带来了两大挑战：在数千个专家中高效选择模型，以及随着新模型和新任务引入持续更新路由机制。本文将此场景形式化为持续模型路由（CMR），提出CMRBench——一个大规模基准，模拟真实模型库扩展，包含超过2000个候选模型。我们还提出CARvE，一种基于对比嵌入的高效持续模型路由方法，通过检查点锚定和结构化回放实现。大量实验证明，CARvE在模型、家族和领域级别准确率上显著优于零样本检索、微调及适配器融合基线。
原文摘要 · Abstract (English)
AI model hubs provide access to a rapidly growing collection of powerful pre-trained models, enabling off-the-shelf mixture-of-experts systems with different routing strategies. However, this rapid growth poses two fundamental challenges: scaling model selection across thousands of experts and continually updating routing mechanisms as new models and tasks are introduced. In this paper, we formalise this setting as Continual Model Routing (CMR) and propose CMRBench, a new large-scale benchmark simulating realistic hub expansion and including over 2,000 candidate models. Finally, we introduce CARvE, a contrastive embedding approach for efficient continual model routing via checkpoint-based anchoring and structured replay. Extensive empirical results and ablations show that CARvE significantly outperforms zero-shot retrieval, fine-tuning, and adapter-merging baselines in model, family, and domain-level accuracy.
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