AI RESEARCH
Self-Describing Structured Data with Dual-Layer Guidance: A Lightweight Alternative to RAG for Precision Retrieval in Large-Scale LLM Knowledge Navigation
arXiv CS.CL
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ArXi:2604.19777v1 Announce Type: new Large Language Models (LLMs) exhibit a well-documented positional bias when processing long input contexts: information in the middle of a context window receives substantially less attention than content at the boundaries, a phenomenon termed the Lost-in-the-Middle effect (Liu, 2024). This limits knowledge-retrieval applications that embed large structured knowledge bases directly in the LLM context.