Retrieval-augmented generation

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

6,643 stories · RSS feed

arXiv Machine Learning

Jun 26

Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork

arXiv:2606. 26772v1 Announce Type: new Abstract: Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD, which repeatedly inject high-dimensional noise in parameter space throughout training.

By Naoki Nishikawa, Shokichi Takakura, Satoshi Hasegawa

rag diffusion fine-tuning safety

arXiv AI

Jun 26

The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation

arXiv:2512. 10388v3 Announce Type: replace-cross Abstract: Conventional Sequential Recommender Systems (SRS) typically assign unique hash IDs (HID) to construct item embeddings, which mainly capture collaborative signals from historical user-item interactions.

By Ziwei Liu, Yejing Wang, Wanyu Wang, Wang Zejian, Qidong Liu, Zijian Zhang, Chong Chen, Wei Huang, Xiangyu Zhao

rag efficiency benchmarks safety

arXiv Machine Learning

Jun 26

Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding

arXiv:2606. 27114v1 Announce Type: new Abstract: Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and debiasing under unobserved confounding scenarios.

By Haoran Zhang, Chuanpu Li, Yuxin Fu, Bin Tong, Guan Wang, Bo Zheng, Feng Zhou

rag benchmarks safety

arXiv Machine Learning

Jun 26

RecallRisk-BERT: A Multi-Task Framework for Post-Report Medical Device Recall Triage

arXiv:2606. 27174v1 Announce Type: new Abstract: Medical device recalls are a critical regulatory mechanism for protecting patient safety.

By Ali Semih Atalay, Sevgi Yigit-Sert

rag safety

arXiv Machine Learning

Jun 26

Explaining Temporal Graph Neural Networks via Feature-induced Information Flow

arXiv:2606. 27201v1 Announce Type: new Abstract: Event-based Temporal Graph Neural Networks (ETGNNs) have demonstrated strong performance across a wide range of applications, including social network analysis, epidemic tracing, recommender systems, and political event forecasting.

By Ping Xiong, Thomas Schnake, Klaus-Robert M\"uller, Shinichi Nakajima

rag

arXiv Machine Learning

Jun 26

What Survives When You Compress a Recursive Reasoner for the Edge?

arXiv:2606. 26488v1 Announce Type: new Abstract: Recursive reasoning models can solve complex structured tasks with only a few million parameters by repeatedly updating a latent state.

By Pearse Jim, Steven Kolawole, Opegbemi Matthias Busoye, Glory Bagai, Virginia Smith

rag efficiency

arXiv Machine Learning

Jun 26

A Generalization Theory for JEPA-Based World Models

arXiv:2606. 27014v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have recently emerged as a promising paradigm for world modeling by learning predictive dynamics in a latent space rather than generating future observations at the input level.

By Jingyi Cui, Qi Zhang, Hongwei Wen, Yisen Wang

rag diffusion

arXiv AI

Jun 26

Kalman Prototypical Networks for Few-shot Fault Detection in Combined Cycle Gas Turbines

arXiv:2606. 26710v1 Announce Type: new Abstract: Combined-cycle gas turbines (CCGTs) play a key role in modern power generation, offering both high efficiency and reduced environmental impact.

By Mohammed Ayalew Belay, Lucas Ferreira Bernardino, Adil Rasheed, Rub\'en M. Monta\~n\'es, Pierluigi Salvo Rossi

rag

arXiv AI

Jun 26

MIRROR: Novelty-Constrained Memory-Guided MCTS Red-Teaming for Agentic RAG

arXiv:2606. 26793v1 Announce Type: cross Abstract: Multimodal agentic retrieval-augmented generation (RAG) systems expand the attack surface beyond prompt injection to include text poisoning, image injection, direct-query attacks, and orchestrator-level tool manipulation.

By Inderjeet Singh, Andr\'es Murillo, Motoyoshi Sekiya, Yuki Unno, Junichi Suga

rag agents robotics multimodal benchmarks safety

arXiv AI

Jun 26

Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledge

arXiv:2606. 26511v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) gives agents access to accumulated knowledge, but has no model of time.

By Neeraj Yadav

llms rag agents benchmarks

arXiv AI

Jun 26

MKG-RAG-Bench: Benchmarking Retrieval in Multimodal Knowledge Graph-Augmented Generation

arXiv:2606. 26458v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) over knowledge graphs has emerged as a promising approach for grounding large language models, yet existing benchmarks largely overlook the challenges of retrieval in multimodal knowledge graph RAG (MKG-RAG).

By Xiaochen Wang, Bao Hoang, Han Liu, Ting Wang, Fenglong Ma

llms rag multimodal benchmarks

arXiv Machine Learning

Jun 26

CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting

arXiv:2510. 20769v2 Announce Type: replace-cross Abstract: Accurate medium-range precipitation forecasting is essential for hydrometeorological risk management but remains challenging for both numerical weather prediction (NWP) systems and data-driven models.

By Tianyi Xiong, Haonan Chen, Kelly Mahoney, Jingyin Tang, Tim Smith, Janice Bytheway

llms rag safety

arXiv Machine Learning

Jun 26

Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees

arXiv:2606. 26432v1 Announce Type: new Abstract: Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price can increase predicted demand, implied willingness-to-pay estimates are frequently negative or implausible, and unavailable alternatives receive nonzero probability.

By Yingshuo Wang, Xian Sun, Yanhang Li, Zhichao Fan, Zexin Zhuang

rag

arXiv AI

Jun 26

Retrieval-Warmed Energy-Based Reasoning: A Five-Arm Ablation Methodology for Diffusion-as-Inference on Structured Reasoning Tasks

arXiv:2606. 26476v1 Announce Type: cross Abstract: Warm-started diffusion samplers accelerate iterative inference, but it is rarely clear which part of the pipeline carries the gain.

By Libo Sun, Po-Wei Harn, Zewei Zhang, Peixiong He, Xiao Qin

llms rag diffusion benchmarks safety

arXiv AI

Jun 26

Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents

arXiv:2606. 26627v1 Announce Type: cross Abstract: Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a user's behalf.

By Nada Lahjouji, Ashwin Gerard Colaco

llms rag agents benchmarks safety data-engineering

arXiv AI

Jun 26

ProvenAI: Provenance-Native Traces of Evidence in Generated Answers

arXiv:2606. 26449v1 Announce Type: cross Abstract: Retrieval-augmented systems routinely present citations alongside generated answers, yet a citation does not confirm that the corresponding source meaningfully shaped the output.

By Mohammad Faizan, Dalal Alharthi

rag agents nlp benchmarks

arXiv AI

Jun 26

Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting

arXiv:2606. 26487v1 Announce Type: cross Abstract: Large language models (LLMs) are attractive for context-aware time series forecasting because they can integrate heterogeneous textual signals, yet their discrete, language-oriented tokenization and embedding interfaces are misaligned with continuous numerical values, often harming numerical ordering and forecasting reliability.

By Defu Cao, Zijie Lei, Muyan Weng, Jiao Sun, Yan Liu

llms rag nlp benchmarks

arXiv Machine Learning

Jun 26

The Geometry of Updates: Fisher Alignment at Vocabulary Scale

arXiv:2606. 27242v1 Announce Type: new Abstract: Training-free source selection for LLM families with shared vocabularies arises in scientific string domains such as SMILES, protein, and genomic sequences, where candidate corpora share a tokenizer but differ in prediction targets.

By John Sweeney

llms rag nlp safety

arXiv AI

Jun 26

Generative AI and Copyright Infringement: A Legal-Technical Analysis of AI Music Generation Systems Under 17 U.S.C. Title 17

arXiv:2606. 26111v1 Announce Type: cross Abstract: Generative artificial intelligence (GenAI) has enabled users to synthesize music with text prompts, combining copyrighted lyrics, AI-composed melodies, and synthetic vocals that imitate real artists.

By Zuhaib Hussain Butt

llms rag diffusion

arXiv AI

Jun 26

KARLA: Knowledge-base Augmented Retrieval for Language Models

arXiv:2606. 26807v1 Announce Type: new Abstract: We propose a new method that allows an LLM to automatically pull in factual knowledge from a knowledge base during token generation.

By Francois Crespin (IP Paris, LTCI), Fabian M. Suchanek (IP Paris, LTCI), Nils Holzenberger

llms rag

Get the daily briefing