GRAIL: A Deep-Granularity Hybrid Resonance Framework for Real-Time Agent Discovery via SLM-Enhanced Indexing

Problem
This paper addresses the critical bottleneck of efficient and accurate Agent Discovery in the rapidly expanding ecosystem of Large Language Model (LLM)-based agents. Existing methodologies either rely on heavyweight LLMs for intent parsing, resulting in excessive latencies (often exceeding 30 seconds), or utilize monolithic vector retrieval systems that compromise semantic precision for speed. The authors propose GRAIL (Granular Resonance-based Agent/AI Link) to bridge this gap, achieving sub-400ms discovery latency without sacrificing accuracy. This work is presented as a preprint and has not yet undergone peer review.

Method
GRAIL introduces three core innovations:

1. SLM-Enhanced Prediction: This component replaces the generalized LLM parser with a fine-tuned Small Language Model (SLM) specifically designed for rapid capability tag prediction, achieving millisecond-level response times.
2. Pseudo-Document Expansion: This technique enhances agent descriptions by augmenting them with synthetic queries, thereby increasing semantic density and improving the robustness of dense retrieval processes.
3. MaxSim Resonance: A fine-grained matching mechanism that computes the maximum similarity between user queries and discrete agent usage examples, effectively reducing semantic dilution. The framework is validated on a newly introduced dataset, AgentTaxo-9K, which comprises 9,240 agents.

Results
GRAIL demonstrates a remarkable reduction in end-to-end discovery latency, achieving over a 79× improvement compared to LLM-parsing baselines. Additionally, it significantly outperforms traditional vector search methods in Recall@10 metrics, although specific numerical values for Recall@10 are not disclosed in the abstract. The results indicate that GRAIL provides a scalable and efficient solution for real-time agent discovery, positioning it as a strong contender in the domain of multi-agent collaboration.

Limitations
The authors acknowledge that while GRAIL significantly reduces latency and improves retrieval accuracy, the reliance on a specialized SLM may limit its generalizability across diverse agent types and tasks. Furthermore, the performance metrics are evaluated on a single dataset (AgentTaxo-9K), which may not fully represent the variability encountered in real-world applications. The paper does not discuss potential challenges related to the scalability of the Pseudo-Document Expansion technique or the computational overhead associated with the MaxSim Resonance mechanism.

Why it matters
The implications of GRAIL are substantial for downstream applications in multi-agent systems, particularly in environments where real-time interaction is critical. By providing a framework that balances speed and accuracy, GRAIL enables more efficient agent discovery, which is essential for the development of collaborative AI systems. This work could pave the way for further research into hybrid models that leverage both lightweight and heavyweight language models, as well as inspire new methodologies for enhancing semantic retrieval in large-scale agent ecosystems.

Authors: Jinliang Xu
Source: arXiv:2605.02489
URL: https://arxiv.org/abs/2605.02489v1