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 14

Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion

arXiv:2506. 22036v2 Announce Type: replace Abstract: With the increasing multimodal knowledge privatization requirements, multimodal knowledge graphs in different institutes are usually decentralized, lacking of effective collaboration system with both stronger reasoning ability and transmission safety guarantees.

By Ying Zhang, Yu Zhao, Xuhui Sui, Baohang Zhou, Xiangrui Cai, Li Shen, Xiaojie Yuan, Dacheng Tao

rag diffusion efficiency multimodal benchmarks safety

arXiv AI

Jul 14

Tool-Adaptive LLM Reranker

arXiv:2607. 10555v1 Announce Type: cross Abstract: Generative Large Language Models (LLMs) have revolutionized information retrieval, yet their strictly parametric nature frequently leads to severe factual hallucinations when confronted with complex queries beyond their epistemic boundaries.

By Zichuan Liu, Ruijin Hua

llms rag agents reinforcement-learning efficiency benchmarks safety

arXiv AI

Jul 14

Constraint-Aware Hierarchical Search for Regulation-Driven Fine-Grained Classification

arXiv:2607. 10588v1 Announce Type: new Abstract: Tasks such as customs tariff classification, export control categorization, and standards-based equipment coding require assigning an input instance to a fine-grained class under an explicit regulatory hierarchy.

By Siyu Wang, Wei Tan, Lulu Chen

llms rag nlp benchmarks

arXiv Machine Learning

Jul 14

TabPFN beyond Tabular Data: Calibration and Accuracy on Multimodal Embeddings

arXiv:2607. 11007v1 Announce Type: new Abstract: Few-shot multimodal classification commonly attaches a lightweight head, such as $k$-nearest neighbors, logistic regression, or a linear SVM, to a frozen pretrained encoder.

By Jingxiang Zhang, Lujia Zhong, Zijie Zhu, Shuo Huang, Yuang Xu

rag multimodal

arXiv AI

Jul 14

MusicMark: A Robust Generative Watermarking Framework for Music Generation

arXiv:2607. 11117v1 Announce Type: cross Abstract: AI music generation has rapidly advanced alongside commercial platforms, raising the need for reliable watermarking for provenance and attribution.

By Seohwan Yun, Jeeyoung Yun, Yongjin Kim, Juyeon Lee, Sungwoong Kim

rag diffusion

arXiv AI

Jul 14

Towards Objective Dysgraphia Detection: A Multi-Branch Deep Learning Approach for Online Handwriting Analysis

arXiv:2607. 09826v1 Announce Type: cross Abstract: Dysgraphia is a specific learning disability that is prevalent among school-age children.

By Lydia Ouhib (LIASD), Yassine Ouzar (LIASD), Zo\'e Pinseel (LIASD), St\'ephane Bouilland (LIASD), Mehdi Ammi (LIASD)

rag

arXiv AI

Jul 14

GRASP: GRanularity-Aware Search Policy for Agentic RAG

arXiv:2607. 10463v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers.

By Varun Gandhi, Jaewook Lee, Shantanu Todmal, Franck Dernoncourt, Ryan Rossi, Zichao Wang, Andrew Lan

llms rag agents nlp reinforcement-learning benchmarks

arXiv AI

Jul 14

Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models

arXiv:2601. 07372v2 Announce Type: replace-cross Abstract: While Mixture-of-Experts (MoE) scales capacity via conditional computation, Transformers lack a native primitive for knowledge lookup, forcing them to inefficiently simulate retrieval through computation.

By Xin Cheng, Rui Tian, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Chengqi Deng, Shangyan Zhou, Chenggang Zhao, Zhewen Hao, Yukun Li, Han Zhang, Zhengyan Zhang, Yixu Wei, M. Y Xu, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang

llms rag efficiency benchmarks

arXiv AI

Jul 14

Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration

arXiv:2605. 00972v2 Announce Type: replace-cross Abstract: Earth system science is producing increasingly large, high-dimensional datasets from both physics-based and AI-driven models.

By Nihanth W. Cherukuru, Matt Rehme, Kirsten J. Mayer, David John Gagne, John Schreck, John Clyne, Charlie Becker

rag diffusion safety

arXiv AI

Jul 14

Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey

arXiv:2607. 09666v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data.

By Chengcheng Sun, Jiayun Tian, Cheng Zhai, Zhixiao Wang, Yajie Song, Xiaobin Rui, Jian Zhang, Philip S. Yu

rag

arXiv AI

Jul 14

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization

arXiv:2607. 10127v1 Announce Type: cross Abstract: Evolutionary program search guided by Large Language Models (LLMs) has emerged as a powerful paradigm for automated scientific discovery.

By Xuanzhou Chen, Taoli Cheng

llms rag reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 14

RAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLM

arXiv:2607. 11683v1 Announce Type: cross Abstract: Graph retrieval-augmented generation (GraphRAG) enhances large language models with structured knowledge, yet existing systems construct knowledge graphs in a single extraction pass, producing noisy entities and brittle retrieval.

By Mikhail Komarov, Ivan Bondarenko, Stanislav Shtuka, Oleg Sedukhin, Roman Shuvalov, Yana Dementyeva, Matvey Solovyov, Nikolay O. Nikitin

llms rag nlp

arXiv Machine Learning

Jul 14

From Performance to Representational Adequacy: A Representational Bootstrap Framework for Adaptive Biological Systems

arXiv:2606. 01374v3 Announce Type: replace Abstract: Observable performance is commonly used to characterize biological systems, yet aggregated outputs may remain insufficient for uniquely resolving observational conditions, and richer multivariate representations may retain substantial ambiguity.

By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

rag

arXiv Machine Learning

Jul 14

AuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation Workflows

arXiv:2607. 09682v1 Announce Type: new Abstract: AI systems are increasingly used to assist consequential decisions in regulated domains such as auditing, finance, and healthcare.

By Vimal Nakrani

rag

arXiv Machine Learning

Jul 14

A Multi-Agent Framework for Zero-Dimensional Reduced-Order Model Planning

arXiv:2607. 10994v1 Announce Type: new Abstract: Zero-dimensional reduced-order models (0D ROMs) are central to multi-dimensional design workflows for high-end complex equipment.

By Bingteng Sun, Hao Yin, Yiling Chen, Renjie Xiao, Lei Xie, Shanyou Wang, Ruonan Wang, Shubao Chen, Qingzong Xu, Lin Lu, Qiang Du, Junqiang Zhu

llms rag agents benchmarks

arXiv AI

Jul 14

Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos

arXiv:2607. 11523v1 Announce Type: cross Abstract: When should an intelligent assistant speak up without being asked?

By Gong Sitong, Tianyu Yan, Caixin Kang, Bo Zheng, Xiang Ruan, Huchuan Lu, Kaipeng Zhang, Yoichi Sato, Yifei Huang

rag agents benchmarks safety

arXiv AI

Jul 14

When Sensing Varies with Contexts: Context Probing for Tactile Few-Shot Class-Incremental Learning

arXiv:2603. 25115v2 Announce Type: replace Abstract: Few-shot class-incremental learning (FSCIL) aims to recognize novel classes from only a few labeled samples while retaining previously learned knowledge.

By Yifeng Lin, Aiping Huang, Wenxi Liu, Si Wu, Tiesong Zhao, Zechao Li, Zheng-Jun Zha

rag benchmarks

arXiv Machine Learning

Jul 14

Observable Performance Does Not Fully Reflect Adaptive System Organization: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint

arXiv:2605. 00778v2 Announce Type: replace Abstract: In biomechanical systems, observable performance is often used as a proxy for underlying organization, although similar outputs may arise from different adaptive configurations.

By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

rag

arXiv Machine Learning

Jul 14

Retrieval with Multiple Query Vectors through Anomalous Pattern Detection

arXiv:2605. 01965v2 Announce Type: replace Abstract: A classical vector retrieval problem typically considers a \emph{single} query embedding vector as input and retrieves the most similar embedding vectors from a vector database.

By Allassan Tchangmena A Nken, Baimam Boukar Jean Jacques, Miriam Rateike, Celia Cintas, Skyler Speakman

rag

arXiv AI

Jul 14

CRINN: Contrastive Reinforcement Learning for Approximate Nearest Neighbor Search

arXiv:2508. 02091v3 Announce Type: replace-cross Abstract: Approximate nearest-neighbor search (ANNS) algorithms have become increasingly critical for recent AI applications, particularly in retrieval-augmented generation (RAG) and agent-based LLM applications.

By Xiaoya Li, Albert Wang, Guoyin Wang, Chris Shum, Jiwei Li

llms rag agents reinforcement-learning benchmarks

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