Adaptive Termination in Agentic Multimodal RAG via Cross Modal Evidence Grounding

Multimodal retrieval-augmented generation misses two fundamental primitives: an evidence- grounding metric that evaluates generated claims based on evidence in text, imagery and structured data and a convergence criterion for determining the stopping point of an iterative system. We formally establish both. First, we propose CMEGS (Cross-Modal Evidence Grounding Score) as a claim-level metric with a specifically designed inter-modal agreement component that possesses boundedness, monotonicity and graceful degra- dation due to missing modalities. Second, we calculate a marginal improvement ratio (MIR) from consecutive values of CMEGS, proving its guaranteed termination under finite budget while meeting scale-relative sensitivity. We embed both primitives into an inference-only architecture with five agents and modality adaptation. On PathVQA, our architecture achieves 90.0% faithfulness with 1.1 mean iterations reducing hallucination by 80% relative to conventional RAG. On ScienceQA, it performs at par with the best iterative system in grounding but with 2.3×fewer iterations. The metric remains stable under nine different weight settings (variance below 1.3%) and exhibits strong correlation with grounding judgment scores (r= 0.79).