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Farhad Ameri

Publications and source records attributed to Farhad Ameri.

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ManuRAG: Multi-modal Retrieval Augmented Generation for Manufacturing Question Answering (Early Version)

The evolution of digital manufacturing requires intelligent Question Answering (QA) systems that can seamlessly integrate and analyze complex multi-modal data, such as text, images, formulas, and tables. Conventional Retrieval Augmented Generation (RAG) methods often fall short in handling this complexity, resulting in subpar performance. We introduce ManuRAG, an innovative multi-modal RAG framework designed for manufacturing QA, incorporating specialized techniques to improve answer accuracy, reliability, and interpretability. To benchmark performance, we evaluate ManuRAG on three datasets comprising a total of 1,515 QA pairs, corresponding to mathematical, multiple-choice, and review-based questions in manufacturing principles and practices. Experimental results show that ManuRAG consistently outperforms existing methods across all evaluated datasets. Furthermore, ManuRAG's adaptable design makes it applicable to other domains, including law, healthcare, and finance, positioning it as a versatile tool for domain-specific QA.

cs.CE

C-MAG: Cascade Multimodal Attributed Graphs for Supply Chain Link Prediction

Workshop version accepted at KDD 2025 (AI4SupplyChain). Connecting an ever-expanding catalogue of products with suitable manufacturers and suppliers is critical for resilient, efficient global supply chains, yet traditional methods struggle to capture complex capabilities, certifications, geographic constraints, and rich multimodal data of real-world manufacturer profiles. To address these gaps, we introduce PMGraph, a public benchmark of bipartite and heterogeneous multimodal supply-chain graphs linking 8,888 manufacturers, over 70k products, more than 110k manufacturer-product edges, and over 29k product images. Building on this benchmark, we propose the Cascade Multimodal Attributed Graph C-MAG, a two-stage architecture that first aligns and aggregates textual and visual attributes into intermediate group embeddings, then propagates them through a manufacturer-product hetero-graph via multiscale message passing to enhance link prediction accuracy. C-MAG also provides practical guidelines for modality-aware fusion, preserving predictive performance in noisy, real-world settings.

cs.LG