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

Jul 31

Balancing Privacy and Efficiency: Music Information Retrieval via Additive Homomorphic Encryption

arXiv:2508. 07044v2 Announce Type: replace-cross Abstract: Modern music retrieval runs on vector embeddings, and once these embeddings are shared for search or matching they can be copied, probed, or used to train generative models.

By William Zerong Wang, Dongfang Zhao

rag safety

arXiv AI

Jul 31

The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness

arXiv:2503. 10647v2 Announce Type: replace-cross Abstract: This study evaluated the diagnostic reliability of two Large Language Models (LLMs), Google Gemini 2.

By Krishna Subedi

llms rag robotics

arXiv Machine Learning

Jul 31

Scalable Drift Monitoring in Medical Imaging AI

arXiv:2410. 13174v3 Announce Type: replace-cross Abstract: The integration of artificial intelligence (AI) into medical imaging has advanced clinical diagnostics but poses challenges in managing model drift and ensuring long-term reliability.

By Jameson Merkow, Felix J. Dorfner, Xiyu Yang, Alexander Ersoy, Giridhar Dasegowda, Mannudeep Kalra, Matthew P. Lungren, Christopher P. Bridge, Ivan Tarapov

rag multimodal

arXiv AI

Jul 31

BioPro: Towards Difference-Aware Gender Fairness for Vision-Language Models

arXiv:2512. 00807v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) inherit significant social biases from their training data, notably in gender representation.

By Yujie Lin, Jiayao Ma, Qingguo Hu, Wenbo Li, Genji Li, Derek Wong, Jinsong Su

llms rag diffusion multimodal safety

arXiv Machine Learning

Jul 31

ReToken: One Token to Improve Vision-Language Models for Visual Retrieval

arXiv:2607. 28627v1 Announce Type: cross Abstract: Long visual context poses a challenge for vision-language models: performance degrades as the number of distractors grows, and processing all tokens at once is computationally infeasible under GPU memory constraints.

By Yao Xiao, Reuben Tan, Zhen Zhu, Yuqun Wu, Jianfeng Gao, Derek Hoiem

llms rag efficiency multimodal benchmarks

arXiv AI

Jul 31

Progressive Multimodal Alignment for Continual Instruction Tuning

arXiv:2607. 26947v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) rely on a projector to align visual representations with the language embedding space, making it central to cross-modal understanding.

By Duzhen Zhang, Yahan Yu, Qiaoyi Su, Jiahua Dong, Tielin Zhang

llms rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jul 31

SDO: Structure-Aware Data Organization for Efficient LLM Post-Training

arXiv:2607. 27273v1 Announce Type: new Abstract: Post-training of large language models is expensive, and existing efficiency improvements mainly focus on selecting informative samples or designing training schedules.

By Jinliang Gao, Ning Yang, Hai Wang, Baili Xiao, Pin Lyu

llms rag reinforcement-learning fine-tuning

arXiv AI

Jul 31

ARC-Encoder: learning compressed text representations for large language models

arXiv:2510. 20535v2 Announce Type: replace-cross Abstract: Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs.

By Hippolyte Pilchen, Edouard Grave, Patrick P\'erez

llms rag fine-tuning benchmarks

arXiv AI

Jul 31

IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations

arXiv:2607. 26075v1 Announce Type: cross Abstract: We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines.

By David Kaleko, Sergey Ivanov, Md Mofijul Islam

llms rag agents computer-vision benchmarks

arXiv AI

Jul 31

Anatomy Contextualized Adaption of CT Foundation Models

arXiv:2607. 27154v1 Announce Type: cross Abstract: CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-grained anatomical signals.

By Roshan Kenia, Stephanie L McNamara, William Lotter

llms rag multimodal safety

arXiv Machine Learning

Jul 31

Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games

arXiv:2607. 27536v1 Announce Type: cross Abstract: Learning a strategic task changes more than what is directly taught: fine-tuning on one game can either enhance or degrade an agent's ability to reason in another.

By Joshua Caiata, Sreepriya Pulyassary, Xiang Li, Kate Larson

llms rag agents fine-tuning

arXiv Machine Learning

Jul 31

ARES: Anomaly Recognition Model For Edge Streams

arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.

By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer

rag diffusion

arXiv AI

Jul 31

GroupRAG: Cognitively Inspired Group-Aware Retrieval and Reasoning via Knowledge-Driven Problem Structuring

arXiv:2603. 26807v2 Announce Type: replace-cross Abstract: The performance of language models is commonly limited by insufficient knowledge and constrained reasoning.

By Xinyi Duan, Yuanrong Tang, Jiangtao Gong

llms rag

arXiv Machine Learning

Jul 31

Rao-Blackwellized Score Matching on Manifolds

arXiv:2605. 25567v3 Announce Type: replace-cross Abstract: We study denoising score matching (DSM) when data are drawn from an embedded manifold $M \subset \mathbb{R}^D$.

By Divit Rawal

rag safety

arXiv Machine Learning

Jul 31

DAS-PMVC: A Framework for Partial Multi-View Clustering via Dual Alignment and Structure Enhancement

arXiv:2607. 27761v1 Announce Type: new Abstract: In recent years, multi-view clustering has attracted widespread research interest.

By Shubin Ma, Liang Zhao, Chuanye He, Zhenjiao Liu, Liang Zou, Lin Yuanbo Wu, Yu Shao

rag diffusion benchmarks safety

arXiv Machine Learning

Jul 31

Good Rankers, Bad Objectives: Bilinear Contrastive Critics under Expressive Policy Search

arXiv:2607. 27422v1 Announce Type: new Abstract: Good action rankings do not make a contrastive critic safe to maximize.

By Ayushman Singh, Siddharth Aphale

rag robotics

arXiv AI

Jul 31

FinCacheServe: Dependency-Consistent Answer Reuse for Cost-Efficient RAG Serving over Mutable Enterprise Documents

arXiv:2607. 26076v1 Announce Type: cross Abstract: Retrieval-augmented generation services over mutable enterprise documents repeatedly execute semantically equivalent analysis requests.

By Lingteng Zeng, Yifan Jin

llms rag

arXiv Machine Learning

Jul 31

DS@GT ARC at ImageCLEFmedical 2026: Architectural Diversity for Concept Detection and Foundation-Model Scaling for Caption Prediction in Medical Image Analysis

arXiv:2607. 27763v1 Announce Type: cross Abstract: We describe the DS@GT submissions to the ImageCLEFmedical Caption 2026 challenge, which continues a long-running benchmark on the ROCOv2 dataset with two tracks: Concept Detection (Task 1), assigning UMLS Concept Unique Identifiers (CUIs) to radiology images, and Caption Prediction (Task 2), generating natural-language captions.

By Bowen Wang, Youwen Zhang, Ritesh Mehta

rag fine-tuning benchmarks

arXiv Machine Learning

Jul 31

Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification

arXiv:2607. 28428v1 Announce Type: new Abstract: We introduce Kohn--Sham Spectral Embedding (KSSE), a physics-inspired energy-based model replacing dense CNN classifiers with a sparse-graph spectral embedding evaluated at the Nishimori temperature of an associated Random-Bond Ising Model.

By V. S. Usatyuk, D. A. Sapozhnikov, S. I. Egorov

rag computer-vision

arXiv Machine Learning

Jul 31

TopoFormer: Topology Meets Attention for Graph Learning

arXiv:2607. 28259v1 Announce Type: new Abstract: We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences.

By Md Joshem Uddin, Astrit Tola, Cuneyt Gurcan Akcora, Baris Coskunuzer

llms rag benchmarks

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