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 7

DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation

arXiv:2607. 04927v1 Announce Type: cross Abstract: World Action Models (WAMs) provide a promising alternative to Vision-Language-Action (VLA) policies by using video-based world modeling as dense supervision for robot action learning.

By Jian Zhu, Jianjun Zhang, Taiyi Su, Tianbin Liu, Zhangyuan Wang, Kai Xie, Zitai Huang, Chong Ma, Youzhang He, Tianjian Wang, Hanyang Wang, Weihao Ding, Yi Xu

rag diffusion robotics multimodal

arXiv AI

Jul 7

Hierarchical Evidence-Driven Reasoning for Long Document Understanding

arXiv:2607. 04625v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) streamlines long-document understanding by leveraging retrieval mechanisms to restrict input images to a highly curated subset.

By Junyu Xiong, Yonghui Wang, Rongjian Gu, Chenyu Liu, Bing Yin, Wengang Zhou, Houqiang Li

rag multimodal benchmarks

arXiv AI

Jul 7

LCPNet: Latent Consistent Proximal Unfolding Network for Infrared Small Target Detection

arXiv:2607. 04603v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) aims to identify long distance small targets from complex infrared backgrounds, and is a fundamental task in remote sensing.

By Tianfang Zhang, Fengyi Wu, Lei Li, Chang Liu, Zhenming Peng, Huaping Zhang, Xiangyang Ji

rag benchmarks

arXiv AI

Jul 7

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations

arXiv:2607. 04557v1 Announce Type: cross Abstract: Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles.

By Dongmin Bang, Sugyun An, Inyoung Sung, Ilho Yun, Sun Kim, Sangseon Lee

rag diffusion benchmarks

arXiv AI

Jul 7

HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy

arXiv:2607. 04353v1 Announce Type: cross Abstract: Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups.

By Julius Riel, Vishwa Mohan Singh, Sai Anirudh Aryasomayajula, Anuun Chinbat, Hannes Leonhard, Moritz Ladenburger, Frederik Alexander, Vishisht Choudhary, Fabio Laredo, Giacomo Masserdotti, Thorben Prein, Carsten Marr, Amirhossein Kardoost

rag diffusion computer-vision efficiency

arXiv AI

Jul 7

One Framework for All: Cross-Modal Membership Inference for Generative Models

arXiv:2607. 04339v1 Announce Type: cross Abstract: Large generative models across text-to-text, text-to-image, and image-to-text modalities have been shown to pose significant privacy risks.

By Dayong Ye, Tainqing Zhu, Kun Gao, Junhao Liu, Yichuan Chen, Shuai Zhou, Hengzhu Liu, Bo Liu, Wanlei Zhou

rag diffusion fine-tuning multimodal benchmarks safety

arXiv AI

Jul 7

Detecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)

arXiv:2607. 04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why.

By Mohamed Aly Bouke

rag nlp benchmarks safety

arXiv AI

Jul 7

Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci

arXiv:2607. 04088v1 Announce Type: cross Abstract: LongEval-Sci evaluates scientific retrieval under collection change, where a system should be effective on the current corpus and remain usable as documents accumulate over time.

By Yingdong Yang, Haijian Wu

rag benchmarks

arXiv AI

Jul 7

Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings

arXiv:2607. 03806v1 Announce Type: cross Abstract: Audio foundation models are widely adopted as general-purpose feature extractors, yet the internal structure of their learned representations remains insufficiently understood.

By H\'ector Martel, Joe Hennessy-Priest, Taemin Cho

rag multimodal

arXiv AI

Jul 7

A Failure-Mode Benchmark for Polymorphic Sybil Poisoning in RAG

arXiv:2607. 03739v1 Announce Type: cross Abstract: We release a benchmark and failure-mode-aware evaluation framework for grounded QA under coordinated retrieval poisoning.

By Donghyun Lee (Dongguk University), Juntae Kim (Dongguk University)

rag multimodal benchmarks

arXiv AI

Jul 7

Latent Clarity: Bridging World-Model Kinematics to Semantic Manifolds for Video Anomaly Anticipation

arXiv:2607. 03558v1 Announce Type: cross Abstract: Continuous video anomaly detection is dominated by reactive Multiple Instance Learning (MIL) that collapses spatiotemporal features into scalar scores.

By Abu Anas Ibn Samad

rag diffusion efficiency

arXiv AI

Jul 7

PedestrianDiffusion: Multimodal Generative Denoising and Dense State Estimation for Inertial Navigation

arXiv:2607. 03349v1 Announce Type: cross Abstract: The accuracy of consumer-grade inertial navigation is bottlenecked by the stochastic noise of Micro-Electro-Mechanical Systems (MEMS).

By I-Hao Lu, Dongsoo Han

rag diffusion multimodal benchmarks

arXiv AI

Jul 7

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation

arXiv:2607. 03068v1 Announce Type: cross Abstract: Semi-supervised semantic segmentation (SSSS) has long turned on one question, which pseudo-labels to trust, and answered it with ever more careful confidence filtering.

By Ebenezer Tarubinga

rag computer-vision

arXiv AI

Jul 7

A Retrieval-Augmented Framework for Detecting and Resolving Pragmatic Ambiguities in Natural Language Requirements

arXiv:2607. 04436v1 Announce Type: cross Abstract: Natural language requirements (NLRs) are essential for bridging communication gaps among diverse stakeholders in software development.

By Pavithra PM Nair, Preethu Rose Anish

llms rag safety

arXiv AI

Jul 7

Safe Inference-Time Alignment via Lagrangian Reward Augmentation

arXiv:2607. 02781v1 Announce Type: cross Abstract: Inference-time alignment steers a frozen language model during decoding using auxiliary reward signals, avoiding the cost of repeated weight updates.

By Yaswanth Chittepu, Ativ Joshi, Sohini Chintala, Scott Niekum

llms rag reinforcement-learning fine-tuning safety

arXiv AI

Jul 7

Toward Trustworthy Large Language Model Agents in Healthcare

arXiv:2607. 05055v1 Announce Type: new Abstract: Healthcare appointment scheduling remains a persistent operational bottleneck, driven by manual coordination, fragmented legacy systems, and high administrative overhead.

By Hadi Hasan, Safaa Salman, Adam Tai Abou Dargham, Ammar Mohanna, Ali Chehab

llms rag agents benchmarks safety

arXiv AI

Jul 7

MechMath Agent Team: LLM Driven Agents for Mathematical Research

arXiv:2607. 04394v1 Announce Type: new Abstract: AI reasoning has become a central focus in contemporary artificial intelligence, largely driven by the success of large language models.

By Yichuan Cao, Ruichen Qiu, Junqi Liu, Jiaqi Wang, Dakai Guo, Ruyong Feng, Lihong Zhi, Xiao-Shan Gao

llms rag agents

arXiv Machine Learning

Jul 7

Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models

arXiv:2602. 24264v2 Announce Type: replace-cross Abstract: Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems.

By Arnas Uselis, Andrea Dittadi, Seong Joon Oh

rag

arXiv Machine Learning

Jul 7

Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks

arXiv:2509. 14026v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions.

By Jiun-Cheng Jiang, Morris Yu-Chao Huang, Tianlong Chen, Hsi-Sheng Goan

rag computer-vision safety

arXiv Machine Learning

Jul 7

GlaBoost: A Multimodal Structured Framework for Glaucoma Risk Stratification

arXiv:2508. 03750v2 Announce Type: replace Abstract: Early and accurate glaucoma detection is critical to prevent irreversible vision loss, yet existing AI methods often rely on unimodal inputs and lack interpretability.

By Cheng Huang, Zeyu Han, Weizheng Xie, Karanjit Kooner, Tsengdar Lee, Jui-Kai Wang, Jia Zhang

llms rag multimodal safety

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