SCIM: Self-Correcting Iterative Mechanism for Retrieval-Augmented Generation
Abstract
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
Fields of Study & Subjects
Information Retrieval and Search Behavior
Topic Modeling
Advanced Graph Neural Networks
Publication details
- Journal / source
- Electronics
- Publisher
- MDPI AG
- Volume
- 15
- Issue
- 5
- Pages
- 996–996
- Publication date
- 27 February 2026
- Language
- EN