arXiv AI July 7, 2026 By Wei Liu, Eric Krokos, Kirsten Whitley, Rebecca Faust, Chris North

Scalable Semantic Steering of Embedding Projections

arXiv:2607. 03978v1 Announce Type: cross Abstract: Low-dimensional projections support interactive visual analysis of high-dimensional data embeddings, but their structure often does not align with analyst-defined semantic relationships.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 21

MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs

arXiv:2507. 20804v3 Announce Type: replace Abstract: Large Language Models (LLMs) suffer from hallucinations due to their static parametric knowledge.

By Xueyao Wan, Hang Yu

llms rag multimodal benchmarks safety

arXiv AI

Aug 7

ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

arXiv:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.

By Jiafan Li, Mengxue Yang, Jiaqi Zhu, Liang Chang, Ying Li, Hongan Wang

llms rag multimodal

arXiv Machine Learning

Jun 11

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs

arXiv:2606. 11898v1 Announce Type: cross Abstract: Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenarios, such as citation networks, e-commerce platforms, social media, and web pages.

By Hengyi Feng, Zeang Sheng, Meiyi Qiang, Meiyi Qiang, Wentao Zhang

llms rag benchmarks safety

arXiv AI

Jul 29

Tokens are All You Need: Dual-purpose Semantic IDs for Achieving LLM-Level I/O Efficiency in recommendation systems

arXiv:2607. 24865v1 Announce Type: cross Abstract: Large-scale recommendation systems face "Memory Wall" bottlenecks due to massive, dense embedding tables.

By Baolei Li, Yiping Yuan, Yilin Zheng, Likang Yin, Ling Liu, Fabio Soldo, Romer Rosales, Xinyang Yi, Lichan Hong

llms rag computer-vision efficiency

arXiv AI

Jul 24

KeySI: An Interaction Framework for Tuning Text Embeddings Based on Human Feedback

arXiv:2607. 20556v1 Announce Type: new Abstract: In large-scale text analysis tasks, pre-trained language models are often used to embed text corpora for downstream analysis.

By Yan Zhu, Y. Chen, Rebecca Faust

llms rag safety

arXiv AI

Jul 21

LookME: Lookup-Based Multimodal Embeddings for Layer Injection in Vision-Language Models

arXiv:2607. 16305v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have achieved strong progress in multimodal understanding.

By Zeyu Xu, Xingzhong Hou, Pengkai Guo, Siling Lin, Xiao Xu, Menghua Zhai, Haoyu Chen, Yunke Zhang, Fei Huang

llms rag multimodal benchmarks

Related stories

MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs

ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs

Tokens are All You Need: Dual-purpose Semantic IDs for Achieving LLM-Level I/O Efficiency in recommendation systems

KeySI: An Interaction Framework for Tuning Text Embeddings Based on Human Feedback

LookME: Lookup-Based Multimodal Embeddings for Layer Injection in Vision-Language Models