CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework
This work addresses the hallucination problem in vision-language models by providing a framework that ensures step-level citation grounding and iterative correction, which is a significant improvement over existing methods.
CaVe-VLM-CoT introduces a modular reflection-based agentic-RAG framework that enforces evidence-grounded reasoning in VLMs via a five-stage closed-loop pipeline, achieving 87.1% accuracy on ScienceQA and 55.2% on MMMU.
Vision-Language Models (VLMs) remain prone to hallucinations, producing fluent but visually unfaithful outputs. Existing chain-of-thought and retrieval-augmented methods only partially address this, as they neither enforce step-level citation grounding nor route verification failures back to retrieval for correction. We present CaVe-VLM-CoT, a modular reflection-based agentic-RAG framework that enforces evidence-grounded reasoning through a five-stage closed-loop pipeline: Extractor, Retriever, Solver, Citation Injector, and Verifier, in which detected ungrounded claims trigger structured feedback to the Extractor for targeted re-retrieval. Since no existing framework jointly measures retrieval quality, step-wise citation faithfulness, and cross-modal grounding, we propose a suite of 23 component-wise metrics across all stages, anchored by CaVeScore, a composite metric weighting accuracy, citation precision and recall, attribution, and evidence grounding. Without any architectural or prompt modifications, CaVe-VLM-CoT achieves 87.1\% accuracy and 56.6\% CaVeScore on ScienceQA , and 55.2\% accuracy and 35.7\% CaVeScore on MMMU (30 subjects).