A multimodal cross-modal explainable retrieval-augmented generation framework for hallucination-grounded visual question answering

Recent multimodal large language models (MLLMs) have achieved remarkable performance on visual question answering (VQA) and multimodal reasoning tasks. But one overlooked failure case stubbornly persists: when answers require simultaneous synthesis of evidence from natural images, free-form text, and structured tables, present models suffer from trimodal hallucination—generating content unattributed to any of the input modalities. Prior approaches to hallucination mitigation focus on image-text pairs, and existing retrieval-augmented generation (RAG) systems for multimodal settings do not readily include supporting evidence and often lack modality-attributed explainability. In this paper, we present MXRAG (Multimodal Cross-Modal Explainable Retrieval-Augmented Generation), a new approach to this trimodal evidence problem with three key innovations that work in concert: (1) a Trimodal Evidence Retriever (TER) that retrieves image patches, text passages, and table rows jointly using a shared semantic manifold; (2) a Cross-Modal Attribution Network (CMAN) that computes fine-grained, token-level attribution scores inline during generation, mapping each generated token to supporting evidence from all three modalities; and (3) a Hallucination-Aware Constrained Decoding (HACD) strategy that penalises generation steps with attribution entropy above a calibrated threshold, suppressing unsupported factual tokens at inference time. CMAN is trained with novel cross-modal attribution and modality-coherence losses using token-level gold annotations; HACD requires no additional training and is calibrated per dataset on the validation split. We cast joint retrieval-generation as a constrained variational problem over a trimodal evidence space and introduce MMTabQA, a new trimodal VQA benchmark derived from WikiTableQuestions and MSCOCO with 12,847 instances and token-level attribution labels. Evaluation on four benchmarks (MMTabQA, WebSRC, ChartQA, MIMIC-CXR-VQA) shows that MXRAG achieves state-of-the-art. +21.4% points (p.p.) exact match accuracy, − 38.6% relative reduction in hallucination rate (− 12.1 p.p. absolute) versus the best multimodal RAG baseline, and 89.4% modality coherence score. Ablation experiments confirm the contribution of each component, with CMAN providing the largest accuracy gain (+ 14.2%) and HACD the largest hallucination reduction (− 23.1%). A supplementary human evaluation on 200 instances confirms that the entropy-based hallucination metric tracks human judgement (human-judged HR: 21.3% vs. metric HR: 19.1%). MXRAG advances the state of the art for reliable, interpretable, evidence-based multimodal AI.