arXiv AI July 21, 2026 By Seonok Kim

ColGraphRAG: Late-Interaction Evidence Retrieval for Multimodal GraphRAG

arXiv:2607. 16208v1 Announce Type: new Abstract: Graph-grounded multimodal question answering organizes text, tables, and images in a structured evidence graph, yet end-to-end accuracy depends on which multimodal assets are ranked highly enough to enter downstream reasoning; for graph-linked images, single-vector bi-encoder similarity can discard patch- and token-level structure needed for fine-grained alignment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 18

GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG

arXiv:2608. 15056v1 Announce Type: new Abstract: Multimodal retrieval-augmented generation (RAG) systems often rely on long unstructured contexts or aggressively expanded evidence graphs, which can introduce noisy evidence, weaken multi-hop reasoning, and increase unsupported generation.

By Zafar Ali, Asad Khan, Aalia Malik, Pavlos Kefalas

llms rag multimodal

arXiv AI

Sep 10

CrossModalQA: A Cross-modal and Multi-hop Benchmark for Multimodal Retrieval-augmented Generation

arXiv:2609.05518v1 Announce Type: cross Abstract: Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, m...

By Jiacheng Cai, Zijin Hong, Zheng Yuan, Huachi Zhou, Qinggang Zhang, Xiao Huang

llms rag multimodal benchmarks

arXiv AI

Sep 1

HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answering

arXiv:2608.29088v1 Announce Type: new Abstract: Multimodal question answering remains sensitive to noisy, incomplete, and weakly grounded evidence. Long unstructured contexts can introduce redundancy...

By Zafar Ali, Asad Khan, Nimbeshaho Thierry, Nabila Amir, Adam A. Q. Mohammed, Pavlos Kefalas

llms nlp fine-tuning efficiency multimodal

arXiv AI

Jul 29

HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document

arXiv:2607. 24861v1 Announce Type: cross Abstract: Question answering (QA) over complex documents requires models to retrieve and integrate evidence distributed across distant document regions and modalities.

By Xin He, Yili Wang, Wenqi Fan, Qing Li, Qinggang Zhang, Yi Chang, Xin Wang

rag nlp multimodal

arXiv Computation and Language

Sep 18

Less Is More: Graph-free Multimodal RAG via Multi-signal Late Fusion

The paper introduces TrioRAG, a graph-free multimodal retrieval-augmented generation framework that combines evidence from the question, an anchor image, and a VLM-enhanced query via late fusion. It also presents AutoQA, a benchmark featuring noisy web-sourced images that require reasoning across manuals. TrioRAG outperforms graph-based systems on three benchmarks while cutting costs and speeding up inference by 1.6–2.3×.

By Tithi Rakshit, Hongkuan Zhou, Lavdim Halilaj, Yuqicheng Zhu

rag nlp multimodal benchmarks

arXiv Computer Vision

4d ago

ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge

arXiv:2602.09839v2 Announce Type: replace Abstract: Existing multimodal retrieval benchmarks largely emphasize semantic matching on daily-life images and offer limited diagnostics of professional kno...

By Yijie Lin, Guofeng Ding, Haochen Zhou, Haobin Li, Mouxing Yang, Xi Peng

agents multimodal benchmarks

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Related stories

GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG

CrossModalQA: A Cross-modal and Multi-hop Benchmark for Multimodal Retrieval-augmented Generation

HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answering

HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document

Less Is More: Graph-free Multimodal RAG via Multi-signal Late Fusion

ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge