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 2

RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation

arXiv:2607. 00147v1 Announce Type: new Abstract: Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space.

By Deyang Jiang, Haoran Wu, Ziyi Wang, Yiming Rong, Yunlong Zhao, Ye Jin, Bo Xu

llms rag agents reinforcement-learning benchmarks

arXiv AI

Jul 2

Reading Order Inference for Complex Document Layouts

arXiv:2607. 01018v1 Announce Type: cross Abstract: Reading order inference remains a critical bottleneck in the digitization of complex historical manuscripts, where pages contain multiple spatially interleaved reading streams, the canonical example being the Glossa Ordinaria layout, in which a central text is surrounded by commentaries that wrap around it in non-rectangular, non-convex regions.

By Iddo Hakim, Sharva Gogawale, Omer Ventura, Gal Grudka, Daria Vasyutinsky-Shapira, Berat Kurar-Barakat, Nachum Dershowitz

llms rag computer-vision

arXiv Machine Learning

Jul 2

Zero-Shot Distracted Driver Detection via Vision Language Models with Double Decoupling

arXiv:2601. 08467v2 Announce Type: replace-cross Abstract: Distracted driving is a major cause of traffic collisions, calling for robust and scalable detection methods.

By Takamichi Miyata, Sumiko Miyata, Andrew Morris

llms rag computer-vision multimodal safety

arXiv AI

Jul 2

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning

arXiv:2607. 00341v1 Announce Type: cross Abstract: Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT).

By Hengyu Fu, Tianyu Guo, Zixuan Wang, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Stuart Russell, Song Mei

llms rag benchmarks

arXiv AI

Jul 2

Multi-Label Node Classification with Label Influence Propagation

arXiv:2607. 00671v1 Announce Type: cross Abstract: Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role.

By Yifei Sun, Zemin Liu, Bryan Hooi, Yang Yang, Rizal Fathony, Jia Chen, Bingsheng He

rag benchmarks

arXiv Machine Learning

Jul 2

Deep learning with missing data

arXiv:2504. 15388v3 Announce Type: replace-cross Abstract: In the context of multivariate nonparametric regression with missing covariates, we propose Pattern Embedded Neural Networks (PENNs), which can be applied in conjunction with any existing imputation technique.

By Tianyi Ma, Tengyao Wang, Richard J. Samworth

rag

arXiv Machine Learning

Jul 2

MindAU: EEG-Conditioned Facial Action Unit Editing via Dual-Stream Manifold Alignment

arXiv:2607. 00410v1 Announce Type: cross Abstract: Recent brain decoding studies have made substantial progress in reconstructing externally perceived visual content from neural signals.

By Zhenhang Li, Xin Zhou, Hao Deng, Lijun Yin

rag diffusion multimodal benchmarks safety

arXiv Machine Learning

Jul 2

Interpretable vs Learned Encoders for High-Cardinality Fraud Detection

arXiv:2607. 00477v1 Announce Type: new Abstract: A total of seven categorical encoding methods were tested on the IEEE-CIS fraud benchmark dataset (590,540 records, 3.

By Xiao Han, Jingjing Liu, Moxuan Zheng, Zhen Zhang, Chenyu Wu

rag benchmarks

arXiv Machine Learning

Jul 2

Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention

arXiv:2605. 11752v2 Announce Type: replace Abstract: Federated learning relies on effective client selection to alleviate the performance degradation caused by data heterogeneity.

By Qijun Hou, Yuchen Shi, Pingyi Fan, Khaled B. Letaief

rag reinforcement-learning

arXiv AI

Jul 2

Identifying Latent Concepts and Structures for Generalized Category Discovery

arXiv:2607. 00620v1 Announce Type: cross Abstract: Generalized Category Discovery (GCD) aims to recognize known classes while autonomously discovering novel ones in open-world settings.

By Boyang Dai, Chaoqi Chen, Yizhou Yu

rag safety

arXiv Machine Learning

Jul 2

FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling

arXiv:2605. 11428v2 Announce Type: replace Abstract: Exploratory analysis of high-dimensional data rarely stops at a single embedding.

By Hongmin Li

rag benchmarks

arXiv Machine Learning

Jul 2

Amortized Maximum Inner Product Search with Learned Support Functions

arXiv:2603. 08001v3 Announce Type: replace Abstract: Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of a vector taken within a database (the keys) that best aligns with a given query.

By Theo X. Olausson, Jo\~ao Monteiro, Michal Klein, Marco Cuturi

rag benchmarks

arXiv Machine Learning

Jul 2

Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition

arXiv:2607. 00777v1 Announce Type: cross Abstract: Recognizing jazz standards from audio is a challenging form of tune-level music retrieval: different performances of the same standard may vary in tempo, key, arrangement, instrumentation, improvisational content, and even whether the head melody is present.

By \c{C}a\u{g}r{\i} Eser

rag

arXiv AI

Jul 2

Topological Void Analysis A Mathematical Framework for Systematic Technical Innovation Discovery in Knowledge Spaces

arXiv:2607. 00005v1 Announce Type: cross Abstract: Identifying where to innovate in a dense technical domain - such as operating systems or hardware/software co-design - is fundamentally a search problem in a high-dimensional knowledge space.

By Kris Pan

rag safety

arXiv Machine Learning

Jul 2

Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks

arXiv:2605. 00366v4 Announce Type: replace-cross Abstract: High-capacity associative memories based on Kernel Logistic Regression (KLR) exhibit strong storage capabilities, but the dynamical and geometric mechanisms underlying their stability remain poorly understood.

By Akira Tamamori

rag

arXiv Machine Learning

Jul 2

SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport

arXiv:2607. 00377v1 Announce Type: new Abstract: Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a paradigm that has attracted widespread attention.

By Yuting Zhang, Yanbei Liu, Zhitao Xiao, Lei Geng, Yanwei Pang, Xiao Wang

rag efficiency benchmarks

arXiv Machine Learning

Jul 2

CogTax: A Four-Level Cognitive Taxonomy for Command-Line Computing Education

arXiv:2607. 00140v1 Announce Type: cross Abstract: As computing education expands beyond traditional programming into operational domains such as systems administration and command-line environments, existing pedagogical frameworks struggle to capture a dimension that is critical in these contexts: the real-world consequences of learner actions.

By Manuel Alonso-Carracedo (Universidade de Vigo, Spain, IFCAE, Universidade de Vigo, Spain), Ruben Fernandez-Boullon (Universidade de Vigo, Spain, IFCAE, Universidade de Vigo, Spain), Pedro Celard (Universidade de Vigo, Spain, IFCAE, Universidade de Vigo, Spain), Francisco J. Rodriguez-Martinez (Universidade de Vigo, Spain, IFCAE, Universidade de Vigo, Spain), Lorena Otero-Cerdeira (Universidade de Vigo, Spain, IFCAE, Universidade de Vigo, Spain)

rag

arXiv AI

Jul 2

Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings

arXiv:2601. 01558v2 Announce Type: replace-cross Abstract: Predicting river flow in places without streamflow records is challenging because basins respond differently to climate, terrain, vegetation, and soils.

By Pengfei Qu, Wenyu Ouyang, Chi Zhang, Yikai Chai, Shuolong Xu, Lei Ye, Yongri Piao, Miao Zhang, Huchuan Lu

rag

arXiv AI

Jul 2

SchemaRAG: Dynamic Large Schema Reduction for LLM-driven Structured Information Extraction

arXiv:2607. 00008v1 Announce Type: cross Abstract: Extracting structured data from unstructured text using large language models (LLMs) becomes challenging when target schemas are large and complex.

By Sin Yu Bonnie Ho, Arlie Coles, Erik Larsson, Eric Marshall, Nathan Bodenstab, Paul Vozila

llms rag

arXiv AI

Jul 2

AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation

arXiv:2607. 00052v1 Announce Type: cross Abstract: GraphRAG is an extension of retrieval-augmented generation (RAG) that supports large language models (LLMs) by referring to graph-structured data as external knowledge.

By Bao Long Nguyen Huu, Atsushi Hashimoto

llms rag benchmarks

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