Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

3,469 stories · RSS feed

arXiv AI

Jun 11

The Dynamics of Human and AI-Generated Language: How Semantics Fluctuates across Different Timescales

arXiv:2606. 11371v1 Announce Type: cross Abstract: Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content.

By Han-Jen Chang, Yasir \c{C}atal, Angelika Wolman, Agust\'in Ib\'a\~nez, David Smith, I-Wen Su, Kai-Yuan Cheng, Georg Northoff

llms rag

arXiv AI

Jun 11

Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development

arXiv:2602. 19718v2 Announce Type: replace-cross Abstract: The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the carbon footprint of development activities.

By Mateen A. Abbasi, Tommi J. Mikkonen, Petri J. Ihantola, Muhammad Waseem, Pekka Abrahamsson, Niko K. M\"akitalo

rag agents

arXiv AI

Jun 11

nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding

arXiv:2606. 12146v1 Announce Type: cross Abstract: Rotary Position Embedding (RoPE) is widely adopted in Transformer models, yet its extension to high-dimensional domains lacks a unified theoretical formulation.

By Boyang Li, Yulin Wu, Sizhe Xu, Nuoxian Huang, Zhonghang Yuan, Shangyi Guo, Shu Yang, Takahiro Yabe

llms rag

arXiv AI

Jun 11

DiffCold: A Diffusion-based Generative Model for Cold-Start Item Recommendation

arXiv:2606. 12245v1 Announce Type: cross Abstract: Cold-start item recommendation remains a persistent challenge in real-world systems due to the absence of interaction histories.

By Kangning Zhang, Yingjie Qin, Weinan Zhang, Yong Yu, Jianghao Lin

rag diffusion benchmarks safety

arXiv Machine Learning

Jun 11

Learning Patterns and Abstractions from Perceptual Sequences

arXiv:2503. 10973v2 Announce Type: replace Abstract: Cognition swiftly breaks high-dimensional sensory streams into familiar parts and uncovers their relations.

By Shuchen Wu

llms rag

arXiv AI

Jun 11

KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition

arXiv:2605. 19031v2 Announce Type: replace Abstract: Kolmogorov-Arnold Networks (KANs) have demonstrated an exceptional ability to learn complex functions on clean, low-dimensional data but struggle to maintain performance on noisy and imperfect real-world datasets.

By Mengxi Liu, Sizhen Bian, Vitor Fortes, Francisco Calatrava Nicolas, Daniel Gei{\ss}ler, Maximilian Kiefer-Emmanouilidis, Bo Zhou, Paul Lukowicz

rag benchmarks

arXiv Machine Learning

Jun 11

Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning

arXiv:2511. 14427v4 Announce Type: replace-cross Abstract: Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception.

By Rickmer Krohn, Vignesh Prasad, Gabriele Tiboni, Georgia Chalvatzaki

llms rag agents reinforcement-learning robotics multimodal

arXiv AI

Jun 11

MSUE: Multi-Modal Soccer Understanding Expert

arXiv:2606. 12106v1 Announce Type: cross Abstract: This paper presents our solution to the 2026 SoccerNet VQA Challenge.

By Litao Li, Yibo Yu, Yufeng Hu, Zhuo Yang, Jiali Wen, Yixin Chen, Yixi Zhou

llms rag nlp fine-tuning multimodal benchmarks

arXiv AI

Jun 11

When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelines

arXiv:2606. 11265v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate downstream model outputs through malicious knowledge injection.

By Xi Nie, Hongwei Li, Shenghao Wu, Mingxuan Li, Jiachen Li, Wenbo Jiang

rag benchmarks safety

arXiv Machine Learning

Jun 11

Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics

arXiv:2604. 23874v3 Announce Type: replace-cross Abstract: The differentiable physics paradigm may be leveraged as an a-posteriori approach for discovering turbulence closure models by embedding a neural network parameterization directly inside the solver and optimizing it given potentially sparse target data.

By Ashwin Suriyanarayanan, Dibyajyoti Chakraborty, Romit Maulik

rag

arXiv AI

Jun 11

RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways

arXiv:2606. 11275v1 Announce Type: cross Abstract: Rotary Position Embeddings (RoPE) make attention scores position-relative but leave the value pathway position-blind: the message sent by a value token is the same regardless of its distance from the query.

By Alejandro Garc\'ia-Castellanos, Maurice Weiler, Erik J Bekkers

llms rag computer-vision robotics

arXiv AI

Jun 11

Automated Creativity Evaluation of Language Models Across Open-Ended Tasks

arXiv:2606. 11762v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential.

By Min Sen Tan, Zachary Kit Chun Choy, Syed Ali Redha Alsagoff, Nadya Yuki Wangsajaya, Mohor Banerjee, Swaagat Bikash Saikia, Alvin Chan

llms rag agents benchmarks

arXiv Machine Learning

Jun 11

Measuring Semantic Progress in Multi-turn Dialogue via Information Gain

arXiv:2606. 12332v1 Announce Type: cross Abstract: Evaluating multi-turn dialogue is challenging because quality emerges across turns rather than within individual responses.

By Paul He, Shiva Kasiviswanathan, Dominik Janzing

llms rag safety

arXiv Machine Learning

Jun 11

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite

arXiv:2606. 11257v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generation.

By Zhiyuan Cheng, Longying Lai

llms rag efficiency benchmarks safety

arXiv AI

Jun 11

NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track

arXiv:2606. 11199v1 Announce Type: cross Abstract: We present NightFeats, a structured multi-agent retrieval-augmented generation (RAG) system submitted to the MMU-RAGent competition at NeurIPS 2025, where it was awarded Best Dynamic Evaluation in the text-to-text track.

By Quentin Fever, Naziha Aslam

llms rag agents benchmarks

arXiv Machine Learning

Jun 11

Learning Instance-Adaptive Low-Rank Orthogonal Subspaces for Clothes-Changing Person Re-Identification

arXiv:2606. 11661v1 Announce Type: cross Abstract: Clothes-changing person re-identification (CC-ReID) aims to recognize individuals despite drastic appearance changes caused by clothing variation.

By Dong-Woo Kim, Tae-Kyun Kim

llms rag multimodal benchmarks safety

arXiv Machine Learning

Jun 11

MLT-Dedup: Efficient Large-Scale Online Video Deduplication via Multi-Level Representations and Spatial-Temporal Matching

arXiv:2606. 12215v1 Announce Type: cross Abstract: The explosive growth of user-generated video content on online platforms is accompanied by the emergence of numerous near-duplicate videos--videos that are identical or highly similar but differ by partial edits.

By David Yuchen Wang, Haoying Li, Hailun Xu, Wei Chee Yew, Zirui Zhu, Sanjay Saha, Hao Hei, Kanchan Sarkar, Kun Xu

rag

arXiv Machine Learning

Jun 11

Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning

arXiv:2606. 11682v1 Announce Type: cross Abstract: Tabular-image multimodal learning aims to improve predictive modeling by jointly using structured tabular attributes and visual data.

By Jiaqi Luo

rag fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 11

Visualizing LLM Latent Space Geometry Through Dimensionality Reduction

arXiv:2511. 21594v3 Announce Type: replace Abstract: Large language models (LLMs) achieve state-of-the-art results across many natural language tasks, but their internal mechanisms remain difficult to interpret.

By Alex Ning, Vainateya Rangaraju, Yen-Ling Kuo

llms rag diffusion benchmarks

arXiv AI

Jun 11

Semantic search for 100M+ galaxy images using AI-generated captions

arXiv:2512. 11982v2 Announce Type: replace-cross Abstract: Finding scientifically interesting phenomena through slow manual labeling campaigns severely limits our ability to explore the billions of galaxy images produced by telescopes.

By Nolan Koblischke, Liam Parker, Francois Lanusse, Jo Bovy, Irina Espejo, Shirley Ho

llms rag multimodal benchmarks

Get the daily briefing