arXiv:2606.09316v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative evidence, leaving agents to repeatedly infer task procedures from passages, manuals, examples, logs, or trajectories. This raises a fundamental question: can skills extracted from external knowledge bases be installed into an agent, enabling it to rapidly approximate domain expertise? In this paper, we propose Anything2Skill, a taxonomy-guided framework that compiles heterogeneous external knowledge
The rapid development of large language models and retrieval-augmented generation techniques has highlighted a critical bottleneck in how agents leverage external knowledge, prompting innovation in knowledge compilation.
This development represents a significant step towards enabling AI agents to acquire and utilize complex skills more autonomously and efficiently, moving beyond mere information retrieval.
Agents will shift from repeatedly inferring procedures from fragmented data to being 'installed' with reusable, compiled skills, leading to faster and more robust task execution.
- · AI Agent developers
- · Companies with proprietary knowledge bases
- · Autonomous system integrators
- · AI-powered automation platforms
- · Companies relying on basic RAG for complex tasks
- · Workflow automation susceptible to inferential errors
- · Platforms requiring extensive, repetitive human oversight for AI tasks
AI agents will exhibit enhanced domain expertise and procedural execution capabilities across various applications.
This improved capability could accelerate the adoption of autonomous agents in white-collar workflows, leading to significant productivity gains and job displacement in certain areas.
The ability to 'skill-install' could create new economic models around skill marketplaces for AI, and potentially influence the development of more human-like cognitive architectures.
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Read at arXiv cs.AI