Skill Retrieval Augmentation Enhances Agentic AI Scalability
A new research paper introduces Skill Retrieval Augmentation (SRA), a paradigm for large language models (LLMs) acting as agents. The current method of listing all skills in the context window fails to scale as skill corpora grow. SRA enables agents to dynamically retrieve and apply relevant skills from large external corpora on demand. The authors constructed a large-scale skill corpus and created SRA-Bench, the first benchmark for evaluating the full SRA pipeline, including retrieval, incorporation, and end-task performance. The paper is available on arXiv under ID 2604.24594.
Key facts
- Skill Retrieval Augmentation (SRA) is a new paradigm for LLM agents.
- Existing agent systems enumerate skills in the context window, which does not scale.
- SRA allows dynamic retrieval of skills from external corpora.
- SRA-Bench is the first benchmark for the full SRA pipeline.
- The benchmark covers skill retrieval, incorporation, and end-task evaluation.
- A large-scale skill corpus was constructed for the benchmark.
- The paper is on arXiv with ID 2604.24594.
- The research addresses scalability issues in agentic AI.
Entities
Institutions
- arXiv