Adaptive Multimodal Fusion Architecture for Heterogeneous Embedding Spaces in Retrieval-Augmented Generation Systems

Retrieval-augmented generation systems integrate external information to mitigate hallucinations in large language models, yet existing multimodal retrieval-augmented generation implementations struggle with heterogeneous embedding spaces from diverse sources. This study introduces an adaptive multi...

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Bibliografiska uppgifter

Huvudupphov:	Timothy Dillan, Sani Muhamad Isa, Abba Suganda Girsang, Derwin Suhartono
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	IEEE 2026-01-01
Serie:	IEEE Access
Ämnen:	Multimodal fusion retrieval-augmented generation heterogeneous embeddings confidence calibration cross-modal attention embedding alignment
Länkar:	https://ieeexplore.ieee.org/document/11441356/
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