RAG for SKILLS: Retrieval Augmented Execution (SkillRAE)

Standard AI engineering assumed that if you simply feed an LLM the correct tool repository, it has enough "reasoning" power to figure out the execution. SkillRAE proves this is mathematically naive. The LLM is essentially a raw CPU. Delivering uncompiled, isolated tools into its context window forces the LLM to do dependency resolution on the fly, which it fails at. The new insight is that Retrieval is not enough; execution requires Compilation. By explicitly rescuing and grafting boundary conditions (subunits) into a logically bound, low-token payload, we bypass the LLM's stateless amnesia and give it a fully resolved blueprint. all rights w/ authors: SkillRAE: Agent Skill-Based Context Compilation for Retrieval-Augmented Execution Xiangcheng Meng Shu Wang Yixiang Fang∗ from The Chinese University of Hong Kong, Shenzhen arXiv:2605.10114 #airesearch #scienceexplained #aiexplained