CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMs

ArXi:2603.21524v1 Announce Type: cross Large Language Models (LLMs) are deployed in high-stakes settings but can show graphic, gender, and geographic biases that undermine fairness and trust. Prior debiasing methods, including embedding-space projections, prompt-based steering, and causal interventions, often act at a single stage of the pipeline, resulting in incomplete mitigation and brittle utility trade-offs under distribution shifts. We propose CatRAG Debiasing, a dual-pronged framework that integrates functor with Retrieval-Augmented Generation (RAG) guided structural debiasing.