Existing multimodal systems typically associate text and available images based on embedding similarity or simple co-location, but such approaches often fail to ensure that the linked image accurately depicts the specific product or component mentioned in a troubleshooting instruction. We introduce MIRAGE, a metadata-first paradigm that treats structured metadata, (not raw pixels), as a first-class modality for multimodal grounding. In MIRAGE, both text and images are projected through a shared semantic schema capturing product attributes, context, and visual aspects, enabling reasoning over interpretable attributes for troubleshooting rather than unstructured embeddings. MIRAGE comprises of three complementary modules: M-Link for schema-guided image–text linking, M-Gen for metadata-conditioned multimodal generation, and M-Eval for consistency evaluation in the same structured space. Experiments on large-scale enterprise e-commerce troubleshooting data across 10 product types on 100K text chunks and 35K images show that metadata-centric grounding achieves over 40% higher linking coverage of high-quality visual content and over 45% in linking and response quality than embedding-based baselines. MIRAGE demonstrates the potential of structured metadata in enabling scalable, fine-grained grounding in multimodal troubleshooting systems.

Ambiguous user queries pose a significant challenge in task-oriented dialogue systems relying on information retrieval. While Large Language Models (LLMs) have shown promise in generating clarification questions to tackle query ambiguity, they rely solely on the top-k retrieved documents for clarification which fails when ambiguity is too high to retrieve relevant documents in the first place. Traditional approaches lack principled mechanisms to determine when to use broad domain knowledge vs specific retrieved document context for clarification. We propose AsK, a novel hybrid approach that dynamically chooses between document-based or aspect-based clarification based on query ambiguity. Our approach requires no labeled clarification data and introduces: (1) Weakly-supervised Longformer-based ambiguity analysis, (2) Automated domain-specific aspect generation using clustering and LLMs and (3) LLM-powered clarification generation. AsK demonstrates significant improvements over baselines in both single-turn and multi-turn settings (recall@5 gain of ~20%) when evaluated on product troubleshooting and product search datasets.