Trajectory Analysis and Optimization of Agentic Multi-modal RAG Under Referential Definiteness Modulation

Agentic multimodal Retrieval-Augmented Generation (RAG) systems rely on Vision-Language Models (VLMs) to formulate retrieval queries. However, these queries often contain under-specified references to visual entities, causing the retriever to return semantically related but functionally non-resolving evidence, thereby impairing downstream multi-hop reasoning. While existing work on multimodal disambiguation largely assumes static pipelines or manually clarified references, to the best of our knowledge we provide the first systematic study of how such referential under-specification propagates through iterative retrieval trajectories and impacts final answers. To enable rigorous diagnosis, we construct a multi-dataset benchmark and introduce Retrieval-Involved Trajectory Efficiency (RTE), a metric that quantifies the novelty and utility of retrieved evidence. Controlled interventions using oracle entity grounding and entity obfuscation across various VLM backbones reveal that obfuscation increases factual novelty at the expense of utility, while oracle grounding yields diminishing returns as backbone capability grows. Notably, final answers exhibit surprising robustness to ambiguous entity references compared to their intermediate reasoning trajectories. We further identify trajectory stagnation as a recurring failure mode where naive query rewriting offers limited remedy. Motivated by these findings, we propose IIGCR, a training-free optimization mechanism that detects stagnation online and recovers degraded trajectories through context reconstruction and referent-aware rewriting. Extensive experiments demonstrate that IIGCR effectively leverages trajectory-level failure signals for online repair, consistently enhancing both final answer quality and trajectory efficiency. Our code will be publicly available upon acceptance.