arXiv AI June 30, 2026 By Ernesto Lopez Fune (DE)

High-Dimensional Concentration and Retrieval Instability in Embedding Spaces: Implications for Retrieval-Augmented Generation

arXiv:2606. 28330v1 Announce Type: cross Abstract: Embedding-based retrieval systems rely on the assumption that geometric proximity in highdimensional representation spaces reflects semantic relevance.

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

Jun 3

$\mathbb{R}^{2k}$ is Theoretically Large Enough for Embedding-based Top-$k$ Retrieval

arXiv:2601. 20844v3 Announce Type: replace-cross Abstract: This paper studies the Minimal Embeddable Dimension (MED): the least dimension in which there exists a configuration of $m$ object vectors so that every subset of size at most $k$ is exactly retrieved by score comparison.

By Zihao Wang, Hang Yin, Lihui Liu, Hanghang Tong, Yangqiu Song, Ginny Wong, Simon See

llms rag

arXiv AI

Aug 25

Hypergraph Embedding Indexing for Efficient Dense Vector Retrieval

arXiv:2608.22980v1 Announce Type: cross Abstract: Dense vector retrieval has become the foundation of modern semantic search, yet existing approximate nearest neighbor (ANN) indexes treat an embeddin...

By Kishore Konda

rag

arXiv Machine Learning

Jul 24

Semantics at an Angle: When Cosine Similarity Works Until It Doesn't

arXiv:2504. 16318v3 Announce Type: replace Abstract: Cosine similarity is a standard comparison rule for learned representations in information retrieval, natural language processing, computer vision, and multimodal learning.

By Kisung You

rag computer-vision nlp multimodal safety

arXiv AI

Jun 6

HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation

arXiv:2602. 07739v2 Announce Type: replace-cross Abstract: Embedding geometry plays a fundamental role in retrieval quality, yet dense retrievers for retrieval-augmented generation (RAG) remain largely confined to Euclidean space.

By Hiren Madhu, Ngoc Bui, Ali Maatouk, Leandros Tassiulas, Smita Krishnaswamy, Menglin Yang, Sukanta Ganguly, Kiran Srinivasan, Rex Ying

llms rag benchmarks safety

arXiv AI

Sep 10

ECOKV: Geometry-Aware KV Cache Eviction via Complementary Diversity Metrics

arXiv:2609.06663v1 Announce Type: cross Abstract: Although multimodal Large Language Models (MLLMs) excel in diverse tasks, their scalability remains limited by the memory and computational overhead...

By Chin Ting Hsu, Yu-Syuan Xu, Ling Zou, Hsien-Kai Kuo, Wen-Huang Cheng

llms efficiency multimodal benchmarks

arXiv Computer Vision

3d ago

Spherical Interpolation for Backward-Compatible Multimodal Representations

arXiv:2609.39836v1 Announce Type: new Abstract: Contrastive vision-language models map visual and textual representations into a shared normalized embedding space, making cosine similarity the natura...

By Simone Ricci, Niccol\`o Biondi, Federico Pernici

llms rag multimodal benchmarks safety

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Related stories

$\mathbb{R}^{2k}$ is Theoretically Large Enough for Embedding-based Top-$k$ Retrieval

Hypergraph Embedding Indexing for Efficient Dense Vector Retrieval

Semantics at an Angle: When Cosine Similarity Works Until It Doesn't

HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation

ECOKV: Geometry-Aware KV Cache Eviction via Complementary Diversity Metrics

Spherical Interpolation for Backward-Compatible Multimodal Representations