Retrieval-augmented generation

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

2,965 stories · RSS feed

arXiv AI

Aug 7

Integrating Implicit and Explicit Relational Biases through Graph-Based Multiple Instance Learning: A Case Study in Skin Lesion Diagnosis

arXiv:2608. 06037v1 Announce Type: new Abstract: Relational inductive biases are essential for capturing structural dependencies among data.

By Rafa{\l} Buler (Gda\'nsk University of Technology), Jakub Buler (Gda\'nsk University of Technology), Maciej Bobowicz (Medical University of Gda\'nsk), Micha{\l} Grochowski (Gda\'nsk University of Technology)

rag computer-vision benchmarks

arXiv AI

Aug 7

Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations

arXiv:2608. 06305v1 Announce Type: new Abstract: Retrieval-augmented generation over long documents is dominated by one design: chunk the text, embed the chunks, and surface the top-k nearest neighbours of the query.

By Sagar Tamang, Ayush Vyas, Tabarakul Hazarika

rag agents

arXiv AI

Aug 7

Simulator-Grounded Large Language Models for Industrial Causal Reasoning: Tool-Use, Structured Injection, and Plant-Portable Retrieval for Wastewater Treatment Decision Support

arXiv:2608. 05151v1 Announce Type: cross Abstract: Wastewater operators need answers grounded in how their plant's variables interact and how fast effects propagate, not in generic pretraining text, when asking causal questions such as "why is N2O rising?

By Gary Simethy, Daniel Ortiz Arroyo, Petar Durdevic

llms rag nlp benchmarks

arXiv AI

Aug 7

Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors

arXiv:2608. 06300v1 Announce Type: new Abstract: Automatic speaking assessment systems are increasingly deployed in high-stakes settings to mark second language (L2) learners' speaking tests, making it critical to show that their scores depend on speaking proficiency rather than irrelevant speaker attributes such as first language (L1) or age.

By Arya Labroo, Mengjie Qian, Kate Knill

llms rag diffusion multimodal 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 AI

Aug 7

Big, Bright, or Invisible: A Frozen-Feature Benchmark of 3D CT Foundation Models

arXiv:2608. 05960v1 Announce Type: cross Abstract: Routine CT interpretation is inherently comprehensive, capturing incidental findings across the entire scan volume.

By Maulik Chevli, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Philip M\"uller

llms rag nlp multimodal benchmarks safety

arXiv Machine Learning

Aug 7

Verifiable Regularity Criterion for Conditional Expectation Operators and Conditional Mean Embeddings with Applications to Nonparametric Regression, Bayesian Inverse Problems, and Koopman Operators

arXiv:2608. 06155v1 Announce Type: cross Abstract: Conditional expectation operators (CEOs) and their associated conditional mean embeddings (CMEs) play a central role across applied mathematics and machine learning, appearing in nonparametric regression, Bayesian inverse problems, and Koopman operator theory.

By Maximiliano Hertel, Ilja Klebanov, Manuel Schaller, Karl Worthmann

rag

arXiv AI

Aug 7

Task-Conditional Flow Matching for Balanced Multilingual Text Embedding Adaptation

arXiv:2608. 05785v1 Announce Type: cross Abstract: Multilingual text embedding models are commonly adapted using a single training objective across diverse tasks, despite different tasks requiring fundamentally different optimization strategies.

By Tirth Bhatt, Naren Kumar S, Mayank Singh

rag diffusion benchmarks

arXiv Machine Learning

Aug 7

Robust Context-Aware Detection of Malicious Instructions in Text

arXiv:2608. 05430v1 Announce Type: cross Abstract: The remarkable instruction-following ability of modern LLMs has enabled their practical use as the minds of agents that can autonomously complete increasingly complex tasks.

By Buzhao Liu, Xinhang Ma, Yevgeniy Vorobeychik

llms rag agents computer-vision benchmarks safety

arXiv Machine Learning

Aug 7

Where Privacy Risk Lives in English-Source Multilingual RAG: A Stage-Decomposed Audit Across Five Query Languages

arXiv:2608. 05163v1 Announce Type: cross Abstract: A common assumption holds that switching to a non-English language makes a multilingual RAG system easier to attack for personal information.

By Yanhang Li, Zhichao Fan, Zexin Zhuang

llms rag benchmarks safety

arXiv AI

Aug 7

Quality Diversity for Reliable Data Driven Time-Use Optimization

arXiv:2608. 05230v1 Announce Type: cross Abstract: The daily allocation of the finite 24-hour time budget is strongly associated with physical, mental, and cognitive health.

By Aneta Neumann, Ty Stanford, Dorothea Dumuid, Frank Neumann

rag

arXiv Machine Learning

Aug 7

BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

arXiv:2608. 05928v1 Announce Type: new Abstract: Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individual genes.

By Yuhao Wang, Zelin Zang, Yuxuan Liu, Zhen Lei, Stan Z. Li

rag fine-tuning efficiency

arXiv Machine Learning

Aug 7

Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts

arXiv:2608. 06031v1 Announce Type: new Abstract: Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts.

By Quanxin Wang, Xuanting Xie, Bingheng Li, Xingtong Yu, Shuo Wang, Ruiyi Fang, Zhao Kang

llms rag benchmarks

arXiv AI

Aug 7

Universal Pathologies, Conditional Consequences: A Triple-Robustness Analysis of RAG for Multi-Hop Traceability

arXiv:2608. 05153v1 Announce Type: cross Abstract: GraphRAG underperforms vector RAG on citation precision in many reports, but where and why have remained corpus-bound.

By Meftun Akarsu, Burak Ozdemir

llms rag

arXiv AI

Aug 7

Text Steganography with Dynamic Codebook and Multimodal Large Language Model

arXiv:2604. 20269v2 Announce Type: replace-cross Abstract: With the popularity of the large language models (LLMs), text steganography has achieved remarkable performance.

By Jianxin Gao, Ruohan Lei, Wanli Peng

llms rag multimodal

arXiv Machine Learning

Aug 7

Align-RAG: Alignment Is All You Need for TSFM In-Context Learning

arXiv:2608. 05571v1 Announce Type: new Abstract: Retrieval-augmented forecasting promises to adapt frozen Time Series Foundation Models (TSFMs) to new domains without fine-tuning, but recent methods typically rely on learned fusion modules, i.

By Mohammad Asadi, Soheil Hor, Bardiya Akhbari, Jack W. O'Sullivan, Tahoura Nedaee, Layne C. Price, Raviteja Anantha, Euan Ashley, Ehsan Adeli

llms rag fine-tuning benchmarks safety

arXiv AI

Aug 7

Comparative Approaches to Agent Retrieval over Large Skill Libraries

arXiv:2608. 06196v1 Announce Type: new Abstract: Agents backed by large skill libraries must decide which skills to load and in what order.

By Indivara Kolluru, Nathan Sportsman

llms rag agents

arXiv Machine Learning

Aug 7

MoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis

arXiv:2605. 00025v3 Announce Type: replace-cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-impairing conditions.

By Yuanhao Chen, Peter Chin

llms rag benchmarks safety

arXiv Machine Learning

Aug 7

SR-JEPA: Learning Predictive Latent State in 3D Scenes

arXiv:2608. 05774v1 Announce Type: cross Abstract: Joint-embedding predictive architectures learn by predicting latent representations of missing observations, yet many masked JEPAs are evaluated primarily through the encoders they produce.

By Zihan Zhou, Qifu Wen, Xi Zeng

rag

arXiv AI

Aug 7

PD-GS: Phoneme-Driven 3DGS for Audio-Driven Talking Heads

arXiv:2608. 05218v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables fast, photorealistic talking-head rendering, yet accurate lip articulation remains elusive: mouth motion is often over-smoothed and may violate hard articulatory constraints such as bilabial closures, producing the notorious ``leaky mouth'' artifact.

By Ao Fu, Yi Zhou

rag multimodal safety