Retrieval-augmented generation

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

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arXiv AI

Jun 26

Geometric Fairness-Aware Routing for Federated Edge Networks

arXiv:2606. 26125v1 Announce Type: cross Abstract: Emerging 6G and edge-intelligent networks require effective and balanced routing algorithms among varied and spatially distributed devices.

By Ratun Rahman

rag efficiency benchmarks 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

Hybrid privacy-aware semantic search: SVD-truncated document geometry and CKKS-encrypted query reranking under a restricted threat model

arXiv:2606. 26373v1 Announce Type: cross Abstract: Dense embeddings power semantic search and retrieval-augmented generation, but embedding-inversion attacks can reconstruct source text from a vector: when a vector database leaks, the documents behind it leak too.

By Sergey Kurilenko

rag efficiency benchmarks safety

arXiv AI

Jun 26

Semantic Early-Stopping for Iterative LLM Agent Loops

arXiv:2606. 27009v1 Announce Type: new Abstract: Multi-agent large language model (LLM) loops, for example a Writer that drafts and a Critic that revises, are almost always terminated by a fixed iteration cap (max_iterations).

By Sahil Shrivastava

llms rag agents nlp

arXiv AI

Jun 26

Generative Retrieval via Diffusion Transformer with Metric-Ordered Sequence Training and Hybrid-Policy Preference Optimization

arXiv:2606. 26899v1 Announce Type: new Abstract: Embedding-based retrieval ranks items by their similarity to a query in a shared vector space and usually aims to return the highest-scoring items.

By Chenghao Liu, Yu Zhang, Zhongtao Jiang, Kun Xu, Zhenwei An, Renzhi Wang, Zhao Wang, Jiachen Zhang, Yuxiao Zhang, Kun Xu, Songfang Huang

llms rag diffusion fine-tuning benchmarks

arXiv Machine Learning

Jun 26

Listening Like a Judge: A Music-Aware Framework for Automatic Singing Performance Evaluation

arXiv:2606. 26451v1 Announce Type: cross Abstract: Automatic singing quality assessment (SQA) requires evaluating lyrical correctness and musical fidelity while handling expressive variations.

By Neelam Saini, Sourav Ghosh

rag fine-tuning multimodal 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

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 Machine Learning

Jun 26

Tailor Made Embeddings for Quantum Machine Learning

arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.

By Aldo Lamarre, Dominik \v{S}afr\'anek

rag

arXiv AI

Jun 26

Vulnerability of Natural Language Classifiers to Evolutionary Generated Adversarial Text

arXiv:2606. 27215v1 Announce Type: new Abstract: Deep learning models have achieved impressive performance across various fields but remain vulnerable to adversarial inputs, particularly in NLP, where such attacks can have significant real-world consequences.

By Manjinder Singh, Alexander E. I. Brownlee, Mohamed Elawady

llms rag nlp benchmarks safety

arXiv AI

Jun 26

How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?

arXiv:2606. 26346v1 Announce Type: new Abstract: Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-domain evaluations remain largely limited to static knowledge recall.

By David Akinpelu, Akintonde Abbas, Rereloluwa Alimi, Ayodeji Lana

llms rag agents benchmarks safety

arXiv AI

Jun 26

KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction

arXiv:2606. 26179v1 Announce Type: cross Abstract: While WGS-based AMR prediction has reached high accuracy, existing models lack a mechanism to ground neural attributions in established biological pathways.

By Naman Garg, Sarika Jain, Sourav Yadav, Bharat K. Bhargava, Ghanapriya Singh, Abhishek Srivastava, Parimal Kar

rag safety

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

arXiv AI

Jun 26

ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models

arXiv:2606. 26986v1 Announce Type: cross Abstract: Open Relation Extraction (OpenRE) requires a model to extract unseen relations between head and tail entities from unstructured text for real-world applications.

By Xin Lin, Liang Zhang, Guoqi Ma, Hongyao Tu, Jinsong Su

llms rag

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 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

MORL-A2C: Multi-Objective Reinforcement Learning Reranker for Optimizing Healthiness in MOPI-HFRS

arXiv:2606. 23603v2 Announce Type: replace Abstract: Unhealthy dietary behavior continues to be a persistent public health issue in the United States, exacerbated by recommendation systems that prioritize user preference without considering nutritional health.

By Aarya Vasantlal, Joshua Zolla, Chuxu Zhang

rag reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 26

Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors

arXiv:2510. 00586v3 Announce Type: replace Abstract: Existing data poisoning attacks on retrieval-augmented generation (RAG) systems scale poorly because they require costly optimization of poisoned documents for each target phrase.

By Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang, Yun-Nung Chen

rag safety

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 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

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