SCIM: Self-Correcting Iterative Mechanism for Retrieval-Augmented Generation

Standard Retrieval-Augmented Generation (RAG) models are limited by their “one-shot” nature, failing to assess or improve answer quality dynamically. To address this, we introduce SCIM (Self-Correcting Iterative Mechanism), a framework featuring multi-dimensional evaluation and adaptive retrieval. A key distinction of SCIM is its efficiency: it operates on a lightweight Flan-T5-base model (250M parameters) and requires no fine-tuning, challenging the industry’s reliance on 7B+ parameter models. Experimental results across four major benchmarks show that SCIM yields a 17.2% improvement over standard RAG (p <0.001). Notably, SCIM achieves parity with state-of-the-art models like ITER-RETGEN while reducing retrieval overhead by 31%, with 35% of queries converging within just 1–2 iterations. With high human correlation (Spearman ρ=0.842), SCIM demonstrates that robust, self-correcting RAG performance is attainable without the computational costs of large-scale LLMs.