As the ecosystem of Large Language Model (LLM)-based agents expands rapidly, efficient and accurate Agent Discovery becomes a critical bottleneck for large-scale multi-agent collaboration. Existing approaches typically face a dichotomy: either relying on heavy-weight LLMs for intent parsing, leading to prohibitive latency (often exceeding 30 seconds), or using monolithic vector retrieval that sacrifices semantic precision for speed. To bridge this gap, we propose \textbf{GRAIL} (Granular Resonance-based Agent/AI Link), a novel framework achieving sub-400ms discovery latency without compromising accuracy. GRAIL introduces three key innovations: (1) \textbf{SLM-Enhanced Prediction}, replacing the generalized LLM parser with a specialized, fine-tuned Small Language Model (SLM) for millisecond-level capability tag prediction; (2) \textbf{Pseudo-Document Expansion}, augmenting agent descriptions with synthetic queries to enhance semantic density for robust dense retrieval; and (3) \textbf{MaxSim Resonance}, a fine-grained matching mechanism computing maximum similarity between user queries and discrete agent usage examples, effectively mitigating semantic dilution. Validated on \textbf{AgentTaxo-9K}, our new large-scale dataset of 9,240 agents, GRAIL reduces end-to-end discovery latency by over \textbf{79$\times$} compared to LLM-parsing baselines, while significantly outperforming traditional vector search in Recall@10. This framework offers a scalable, industrial-grade solution for the real-time ``Internet of Agents."

翻译：随着基于大语言模型（LLM）的智能体生态系统快速扩张，高效且准确的智能体发现成为大规模多智能体协作的关键瓶颈。现有方法通常面临两难困境：要么依赖重量级LLM进行意图解析，导致延迟过高（通常超过30秒）；要么采用单一向量检索以速度换取语义精度。为弥合这一差距，我们提出**GRAIL**（基于粒度共振的智能体/人工智能链接）——一种在不牺牲准确性的前提下实现低于400毫秒发现延迟的新型框架。GRAIL引入三项核心创新：（1）**SLM增强预测**，用专门微调的小语言模型（SLM）替代通用LLM解析器，实现毫秒级能力标签预测；（2）**伪文档扩展**，通过合成查询增强智能体描述，提升语义密度以支持鲁棒的稠密检索；（3）**MaxSim共振**，一种细粒度匹配机制，计算用户查询与离散智能体使用示例间的最大相似度，有效缓解语义稀释问题。在包含9240个智能体的新大规模数据集**AgentTaxo-9K**上验证表明，与LLM解析基线相比，GRAIL将端到端发现延迟降低**79倍以上**，同时在Recall@10指标上显著优于传统向量搜索。该框架为实时“智能体互联网”提供了一种可扩展的工业级解决方案。