Retrieval-augmented generation

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

3,085 stories · RSS feed

arXiv Machine Learning

Jul 27

LunarFM: A Shared Multimodal Representation of the Moon's Surface

arXiv:2607. 22408v1 Announce Type: new Abstract: The renewed global focus on lunar exploration, driven by the prospect of in-situ resource utilization and a sustained human presence on the Moon, has created growing demand for accurate, large-scale characterization of the lunar surface.

By Marc Girona-Mata, Jakob Gawlikowski, Sumit Goski, Gautier Bardi de Fourtou, Valentin T. Bickel, Ben Moseley, Abigail Calzada-Diaz, Sylvester Kaczmarek, Ra\'ul Ramos-Poll\'an

rag multimodal

arXiv Machine Learning

Jul 27

IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data

arXiv:2607. 22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem.

By Masashi Sode, Gianmarco Pinton

llms rag computer-vision fine-tuning

arXiv Machine Learning

Jul 27

Data eccentricity, asymptotics of Gaussian RBF reproducing kernel Hilbert space, and kernel PCA

arXiv:2607. 21823v1 Announce Type: new Abstract: We show that, up to isotropic scaling, the Gaussian RBF reproducing kernel Hilbert space (RKHS) is asymptotically isometric to Euclidean space in the large bandwidth limit.

By Sergio A. Alvarez

rag

arXiv Machine Learning

Jul 27

Unbiased Open World Regularization for Fair Self-Supervised Learning

arXiv:2607. 22149v1 Announce Type: new Abstract: Despite recent advances, self-supervised learning (SSL) models and Joint-Embedding Predictive Architectures (JEPAs) remain susceptible to learning spurious biases in the dataset.

By L{\'e}o Nicollier (CB, ATT), Marc Pic (ATT), Pablo Mus{\'e} (CB, IFUMI), Enric Meinhardt-Llopis (CB), Gabriele Facciolo (CB)

rag diffusion benchmarks safety

arXiv Machine Learning

Jul 27

DCS: A Unified Conditional Sensitivity Framework for Cross-Modal Copyright Infringement Detection

arXiv:2607. 22035v1 Announce Type: new Abstract: Currently, most foundation models can reproduce or strongly depend on copyrighted training content, but output similarity alone is insufficient for infringement detection, because similar outputs may also arise from public-domain concepts, common stylistic conventions, or ordinary statistical generalization.

By Xiafeng Man

llms rag diffusion fine-tuning multimodal safety

arXiv Machine Learning

Jul 27

Toward Goal-Agnostic Joint-Embedding Predictive Control of Partial Differential Equations

arXiv:2607. 21644v1 Announce Type: new Abstract: We present a goal-agnostic control framework for partial differential equations (PDEs) built around a joint-embedding predictive architecture (JEPA).

By Jonathan Gallagher, Roberto Guglielmi

rag diffusion benchmarks

arXiv Machine Learning

Jul 27

Encoding Invisible Causation for Bridge Diagnostic Agents: Triple-Guided Retrieval-Augmented Fine-Tuning with QLoRA

arXiv:2607. 21680v1 Announce Type: new Abstract: Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the naked eye.

By Takato Yasuno

llms rag agents fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 27

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models

arXiv:2505. 23378v3 Announce Type: replace Abstract: Speaker-dependent modelling can substantially improve performance in speech-based health monitoring applications.

By Roseline Polle, Agnes Norbury, Alexandra Livia Georgescu, Nicholas Cummins, Stefano Goria

llms rag

arXiv Machine Learning

Jul 27

Reasoning Denoiser: Denoising Reasoning Traces for Hallucination Detection in Large Reasoning Models

arXiv:2607. 22098v1 Announce Type: cross Abstract: Large reasoning models (LRMs) generate long reasoning traces before producing final answers.

By Junlin Fang, Do Nguyen-Thanh, Xiaogang Xu, Zhen Fang, Sean Du

rag benchmarks safety

arXiv Machine Learning

Jul 27

On the Identifiability of Controlled World Models

arXiv:2607. 22430v1 Announce Type: new Abstract: Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to planning and control.

By Xiangteng Zhang, Yang Guan, Bo Zhang, Ya-Qin Zhang, Shengbo Eben Li

rag

arXiv Machine Learning

Jul 27

LatentFlow: Visual Analytics for Latent Space Analysis in Molecular Graph Neural Networks

arXiv:2607. 21941v1 Announce Type: new Abstract: Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and the outcomes of their reactions.

By Shiyi Liu, Jiaqing Chen, Nicholas Hadler, Rostyslav Hnatyshyn, Michael W. Mahoney, Talita Perciano, John F. Hartwig, Gunther H. Weber, Ross Maciejewski

rag diffusion

arXiv Machine Learning

Jul 27

Phylogenetic signal in marine mammal and bird vocalizations captured by audio foundation models: the limited benefit of domain-specific pretraining

arXiv:2607. 22458v1 Announce Type: new Abstract: Do learned audio embeddings encode structure that nobody told them to encode?

By V\'ictor Rinc\'on Yepes

rag

arXiv Machine Learning

Jul 27

Relaxed activation analysis of dataflow networks - A clock calculus for machine learning and real-time scheduling

arXiv:2607. 21797v1 Announce Type: cross Abstract: Previous work has shown that the simple dataflow primitives of the Lustre language allow the natural, semantically unambiguous, and compact representation of machine learning (ML) applications, including models featuring complex conditional execution and recurrent state.

By William Gaudelier, Albert Cohen, Dumitru Potop Butucaru

rag

arXiv Machine Learning

Jul 27

Quantifying Political Partisanship for Cross-Platform Analyses

arXiv:2607. 21842v1 Announce Type: cross Abstract: Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content.

By Fathima Ameen, Christopher G. Healey

llms rag safety

arXiv Machine Learning

Jul 27

From Seasonality to Semantics: Benchmarking a Hybrid Probabilistic Forecasting System for Roadblocks in Bolivia

arXiv:2607. 21785v1 Announce Type: cross Abstract: Roadblocks in Bolivia are a social conflict phenomenon with devastating economic impacts, estimated at losses equivalent to 4% of the national Gross Domestic Product.

By Rodrigo Vargas Sainz, Christian Ber\'on Curti

rag nlp benchmarks

arXiv Machine Learning

Jul 27

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification

arXiv:2604. 26675v2 Announce Type: replace-cross Abstract: We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding while the readout determines how this representation is exploited.

By Ralntion Komini, Aikaterini Mandilara, Georgios Maragkopoulos, Dimitris Syvridis

rag

arXiv Machine Learning

Jul 27

Approximation and learning of anisotropic and mixed smooth functions by deep ReLU neural networks

arXiv:2605. 31152v2 Announce Type: replace-cross Abstract: This paper studies how efficiently deep ReLU neural networks can approximate and learn smooth functions.

By Yunfei Yang, Jun Fan

rag

arXiv Machine Learning

Jul 27

Reflector: Arrangement-Aware Harmonic Retrieval for Sample-Based Composition

arXiv:2607. 22413v1 Announce Type: cross Abstract: Sample retrieval tools can help composers find harmonically compatible material, but querying from a fixed reference sample becomes less informative as arrangements evolve and the harmonic context shifts with each musical decision.

By Austin Rockman

rag

arXiv Machine Learning

Jul 27

SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation

arXiv:2605. 03534v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) grounds answers in retrieved passages, yet relevance does not guarantee sufficiency: a topical passage may still fail to justify the answer.

By Jingxi Qiu, Zeyu Han, Cheng Huang

llms rag benchmarks safety

arXiv Machine Learning

Jul 27

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era

arXiv:2607. 22068v1 Announce Type: cross Abstract: Multi-branch architectures and CNN-Transformer fusion have long been regarded as effective ways to improve vehicle re-identification (Re-ID) by combining complementary representations.

By Yu Wang, Hongyu Yang

llms rag computer-vision benchmarks

Get the daily briefing