GRAIL Framework Achieves Sub-400ms Agent Discovery Using Small Language Models
Researchers propose GRAIL (Granular Resonance-based Agent/AI Link), a framework for real-time discovery of LLM-based agents in multi-agent systems. GRAIL achieves sub-400ms latency by replacing heavy LLM intent parsing with a fine-tuned Small Language Model (SLM) for capability tag prediction, and uses pseudo-document expansion to enhance semantic density. The approach addresses the trade-off between latency and accuracy in existing methods, which either exceed 30 seconds or sacrifice precision.
Key facts
- GRAIL achieves sub-400ms discovery latency
- Uses SLM-Enhanced Prediction for millisecond-level tag prediction
- Introduces Pseudo-Document Expansion for semantic densification
- Existing LLM-based approaches have latency over 30 seconds
- Monolithic vector retrieval sacrifices semantic precision
- Framework targets large-scale multi-agent collaboration
- Proposed in arXiv paper 2605.02489
- GRAIL stands for Granular Resonance-based Agent/AI Link
Entities
Institutions
- arXiv