An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models
Summary: Presents MQA: an interactive retrieval-augmented LLM system with a multi-modal retrieval framework and navigation-graph index for efficient multimodal search. Uses contrastive learning to weight modalities and a pluggable coordinator pipeline to swap embeddings, graph indexes, and LLMs. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Mengzhao Wang
- 2. Haotian Wu
- 3. Xiangyu Ke
- 4. Yunjun Gao
- 5. Xiaoliang Xu
- 6. Lu Chen
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,235 | ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries | 2025 | VLDB | 4.3690661e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 495 | Milvus: A Purpose-Built Vector Data Management System | 2021 | SIGMOD | 0.00021767688 |
| 2,690 | Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment | 2024 | SIGMOD | 8.293714e-05 |
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