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 Machine Learning

Jul 15

Contrastive Joint-Embedding Prediction for Representation Learning in Structural MRI

arXiv:2607. 11962v1 Announce Type: cross Abstract: Self-supervised learning offers a compelling approach for medical imaging, where labeled data are scarce and acquisition costs are high.

By Fabian Mager, Lars Kai Hansen

rag computer-vision fine-tuning

arXiv AI

Jul 15

An Omnilingual-ASR-Based Speech-LLM System for the 2nd MLC-SLM Challenge

arXiv:2607. 12468v1 Announce Type: cross Abstract: We describe our submission to Task 1 of the 2nd MLCSLM Challenge: a cascaded diarization-then-recognition system that combines DiariZen-Large-s80 (WavLM-Large) segmentation, CAM++ embedding-based two-speaker clustering, and a LoRA-adapted omniASR LLM 7B v2 recognizer, with no oracle segmentation or speaker labels at test time.

By Shuming Fang, Shuifei Zeng

llms rag computer-vision fine-tuning multimodal

arXiv Machine Learning

Jul 15

SlimPer: Make Personalization Model Slim and Smart

arXiv:2607. 12281v1 Announce Type: cross Abstract: Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely on per-token autoregressive prediction, which justifies maintaining large intermediate tensors that scale with sequence length.

By Siqi Wang, Xianjie Chen, Shaofeng Deng, Albert Chen, Romil Shah, Jiawei Huang, Zhaoqin Wang, Zhang Zhang, Yiqun Liu, Meilei Jiang, Anish Dubey, Moyan Mei, Tongxin Wang, Nathan Berrebbi, Misael Manjarres, Armand Sauzay, Shardul Kothapalli, Aryaman Vinchhi, Kevin Johnstone, Juheon Lee, Gufan Yin, Ziheng Huang, Justin Lin, Mert Terzihan, Yilin Qi, Cynthia Yang, Colin Peppler, Qi Ding, Ruohan Sun, Ge Song, Litao Deng, Parichay Kapoor, Matt Ma, Huihui Cheng, Jiyuan Zhang, Yanli Zhao, Yiping Han, Fangqiu Han, Ning Yao, Arun Singh, Jordan Edwards, Zhengyu Su, Abhishek Kumar, Guangdeng Liao, Ankit Asthana

llms rag multimodal safety

arXiv AI

Jul 15

First, do NOHARM: a medical safety benchmark and randomized study of physician and AI teaming on clinical consultations

arXiv:2512. 01241v4 Announce Type: replace-cross Abstract: Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.

By David Wu, Fateme Nateghi Haredasht, Saloni Kumar Maharaj, Priyank Jain, Jessica Tran, Matthew Gwiazdon, Arjun Rustagi, Jenelle Jindal, Jacob M. Koshy, Vinay Kadiyala, Anup Agarwal, Bassman Tappuni, Brianna French, Sirus Jesudasen, Christopher V. Cosgriff, Rebanta Chakraborty, Jillian Caldwell, Susan Ziolkowski, David J. Iberri, Robert Diep, Rahul S. Dalal, Kira L. Newman, Kristin Galetta, J. Carl Pallais, Nancy Wei, Kathleen M. Buchheit, David I. Hong, Vartan Pahalyants, Ernest Y. Lee, Allen Shih, Tamara B. Kaplan, Vishnu Ravi, Sarita Khemani, Thomas A. Buckley, April S. Liang, Daniel Shirvani, Advait Patil, Nicholas Marshall, Kanav Chopra, Joel Koh, Adi Badhwar, Anastasia Perez, Austin J. Schoeffler, Mahbuba Tusty, Chase M. Walton, Liam G. McCoy, David J. H. Wu, Yingjie Weng, Sumant Ranji, Kevin Schulman, Nigam H. Shah, Jason Hom, Arnold Milstein, Arjun K. Manrai, Adam Rodman, Jonathan H. Chen, Ethan Goh

llms rag agents benchmarks safety

arXiv AI

Jul 15

FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis

arXiv:2607. 11464v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) addresses the limitations of Large Language Models (LLMs) when providing responses to domain-specific questions.

By Marlena Fl\"uh, Soo-Yon Kim, Carolin Victoria Schneider, Sandra Geisler

llms rag nlp

arXiv AI

Jul 15

The TIME Machine: On The Power of Motion for Efficient Perception

arXiv:2605. 23045v2 Announce Type: replace-cross Abstract: Video representation learning has seen tremendous progress in recent years.

By Mantas Skackauskas, Xinyue Hao, Laura Sevilla-Lara

rag benchmarks

arXiv Machine Learning

Jul 15

Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence

arXiv:2607. 12916v1 Announce Type: new Abstract: In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations.

By Blanca Cano-Camarero, \'Angela Fern\'andez-Pascual, Jos\'e R. Dorronsoro

rag benchmarks

arXiv AI

Jul 15

QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Design

arXiv:2607. 11916v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) with evolutionary computation has emerged as a powerful paradigm for automated heuristic design in combinatorial optimization.

By Nam Do Khanh, Nhat Nguyen Tran Minh, Dat Pham Vu Tuan, Long Doan, Binh Huynh Thi Thanh

llms rag benchmarks

arXiv Machine Learning

Jul 15

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions

arXiv:2604. 09523v2 Announce Type: replace Abstract: Training reinforcement-learning agents for cyber defense requires an environment that reflects the operational setting: noisy, partial observations, several defenders coordinating across a network, and an adaptive adversary realized through self-play.

By Igor Jankowski

rag agents reinforcement-learning

arXiv AI

Jul 15

Weakly Supervised Spatio-Temporal Candidate Discovery of Dairy Farm Sites from Seasonal Satellite Imagery

arXiv:2607. 12748v1 Announce Type: cross Abstract: Farm site discovery from satellite imagery is a spatiotemporal candidate ranking problem because farm evidence is distributed across pasture, field boundaries, roads, buildings, and seasonal vegetation patterns.

By Usman Haider, Fatima Khalid, Karl Mason

rag

arXiv Machine Learning

Jul 15

Analyzing Image Encoder Choices and Graph Homophily in GCN Frameworks for Breast Ultrasound Classification

arXiv:2607. 12054v1 Announce Type: cross Abstract: Breast ultrasound is widely used for screening, yet automated analysis remains challenging due to speckle noise, acquisition variability, and weak separation of benign and malignant cases in standard ultrasound imaging.

By Sabahattin Mert Daloglu, Ceren Coskun, Harvey Castro, Soner Hacihaliloglu, Ilker Hacihaliloglu

llms rag

arXiv Machine Learning

Jul 15

GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization

arXiv:2607. 11941v1 Announce Type: cross Abstract: Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and structured artifacts that degrade image quality.

By Md Imam Ahasan, Guangchao Yang, A F M Abdun Noor, Kah Ong Michael Goh, S. M. Hasan Mahmud, Md Mahfuzur Rahman

rag diffusion benchmarks

arXiv Machine Learning

Jul 15

ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series

arXiv:2607. 12391v1 Announce Type: new Abstract: We present a diffusion based model for asynchronous time series prediction, where the goal is to predict the next inter event time and event type.

By Saiyue Lyu, Zhitian Zhang, Ruizhi Deng, Thibaut Durand

llms rag diffusion

arXiv AI

Jul 15

Mobility-Aware Cache Framework for Scalable LLM-Based Human Mobility Simulation

arXiv:2602. 16727v2 Announce Type: replace Abstract: Simulating large-scale human mobility is fundamental to understanding population movement patterns and supporting real-world geospatial applications such as urban planning, epidemic response, and transportation analysis.

By Hua Yan, Heng Tan, Yingxue Zhang, Yu Yang

llms rag agents efficiency benchmarks

arXiv Machine Learning

Jul 15

Transforming LLMs into Efficient Cross-Encoders via Knowledge Distillation for RAG Reranking

arXiv:2607. 11933v1 Announce Type: cross Abstract: Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-time deployment.

By Shreeya Dasa Lakshminath, Shubhan S

llms rag fine-tuning efficiency benchmarks

arXiv AI

Jul 15

The One-Word Census: Answer-Choice Conformity Across 44 Language Models

arXiv:2607. 12796v1 Announce Type: cross Abstract: When a language model must pick one answer from a large space of equally valid options, which does it pick -- and how often is it the same answer every other model picks?

By Tapan Parikh

llms rag

arXiv AI

Jul 15

Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes

arXiv:2607. 12924v1 Announce Type: new Abstract: In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters.

By Jonas Ehrhardt, Ren\'e Heesch, Oliver Niggemann

rag agents reinforcement-learning efficiency benchmarks safety

arXiv AI

Jul 15

Matilda: Engine-Agnostic Search with Human Policy Guidance

arXiv:2606. 25176v2 Announce Type: replace Abstract: Chess engines have evolved from search-based systems optimized solely for strength to neural policies capable of modeling human decisions across much of the rating spectrum.

By Jason Carlson

llms rag benchmarks

arXiv AI

Jul 15

Comparing Semantic Navigation in Humans and Large Language Models using Natural Language Processing

arXiv:2607. 12195v1 Announce Type: cross Abstract: Semantic memory retrieval can be conceptualized as navigation through conceptual space.

By Gabriel Paris-Colombo, Rodrigo M. Cabral-Carvalho, Felipe D. Toro-Hern\'andez

llms rag nlp

arXiv AI

Jul 15

Cost-Governed RAG: Unified Per-Tenant Cost Attribution Across Retrieval and Generation in Multi-Tenant LLM Systems

arXiv:2607. 12188v1 Announce Type: new Abstract: Enterprise Retrieval-Augmented Generation (RAG) deployments face a critical governance gap: while LLM generation cost is metered per token, the retrieval layer - vector memory, similarity compute, and embedding API calls - remains an unattributed shared cost, enabling invisible cross-subsidization among tenants.

By Navnit Shukla

llms rag efficiency

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