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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Towards Data Science

Jul 16

Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation

Enterprise Document Intelligence [Vol. 1 #6quater] - Question parsing takes one messy string and writes four typed pieces, each read by a different downstream call The post Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation appeared first on Towards Data Science .

By Kezhan Shi

rag

arXiv AI

Jul 16

AI-accelerated End-to-End Framework for Rapid Professional Upskilling

arXiv:2607. 14044v1 Announce Type: new Abstract: By 2030, 59 of every 100 workers will need reskilling or upskilling, yet the average time to close an enterprise skills gap grew from roughly 3 days in 2014 to 36 days in 2018.

By Tam Nguyen, Hung Nguyen, Robert Ogburn

rag agents

arXiv Machine Learning

Jul 16

Screening of Biosecurity Features in Metagenomic Data with Evo 2 Probes

arXiv:2607. 14070v1 Announce Type: cross Abstract: Genomic foundation models such as Evo 2 learn rich sequence representations, but their value for biosecurity screening is largely unexplored.

By Jeremy Guntoro, Alexander Dack, Dylan Danno, Michaela Jan\v{c}ovi\v{c}ov\'a, Kri\v{z}an Jurinovi\'c, Vanessa Smilansky

rag fine-tuning

arXiv AI

Jul 16

Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Evaluation of an Agentic AI Tutoring System

arXiv:2607. 13370v1 Announce Type: cross Abstract: This paper is an extension of a paper presented at the ICAART 2026 conference, which introduced LEA (Learning Engagement Assistant), an adaptive AI tutoring agent combining course-specific Retrieval-Augmented Generation (RAG) with structured Knowledge Component (KC) models across integrated Chat, Tutor, and Quiz modes.

By Teri Rumble, Javad Zarrin, P. George Lovell, Ruth Falconer

rag agents

arXiv Machine Learning

Jul 16

Accuracy-Preserving Stability Regularization for Large-Scale Retail Demand Forecasting

arXiv:2607. 13331v1 Announce Type: new Abstract: Retail demand forecasts are reused across replenishment, capacity, labor, and transportation planning cycles.

By Jize Li, Jiani He, Dishu Yang, Dingyan Shang, Jingjing Liu, Shiqi Huang

rag

arXiv AI

Jul 16

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning

arXiv:2607. 13555v1 Announce Type: cross Abstract: Bioacoustic call-type classification relies on costly expert annotation.

By Shiqi Zhang, Marius Fai{\ss}, Ariana Strandburg-Peshkin, Tuomas Virtanen

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arXiv AI

Jul 16

Spectral-Informed Neural Networks Outperform Spectral Methods in High-dimensional PDEs

arXiv:2607. 13566v1 Announce Type: cross Abstract: For low-dimensional problems ($d\leq3$), spectral methods can achieve exceptionally high accuracy.

By Tianchi Yu, Ivan Oseledets

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arXiv Machine Learning

Jul 16

Hierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis

arXiv:2607. 01145v2 Announce Type: replace Abstract: Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution.

By Siwon Kim

llms rag computer-vision benchmarks

arXiv Machine Learning

Jul 16

DAGR: State-Conditioned Goal Representations via Difference-Aware Goal Cross-Attention

arXiv:2607. 13731v1 Announce Type: new Abstract: Goal-conditioned reinforcement learning hinges on how the goal is encoded.

By Xing Lei, Wenyan Yang, Xuetao Zhang, Donglin Wang

rag reinforcement-learning robotics safety

arXiv AI

Jul 16

Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

arXiv:2607. 13558v1 Announce Type: new Abstract: Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring.

By Xixuan Hao, Yutian Jiang, Jiabo Liu, Yihang Yang, Guangyin Jin, Song Gao, Yuxuan Liang

rag agents reinforcement-learning multimodal

arXiv AI

Jul 16

Designing Safety-Constrained LLM Systems for Public Health Information Access

arXiv:2607. 13038v1 Announce Type: cross Abstract: We present the design and implementation of a safety constrained large language model (LLM) system for public health information access, focusing on maternal and child health (MCH) resource navigation.

By Ben Torkian, Jun Zhou

llms rag safety data-engineering

arXiv AI

Jul 16

RAGthoven at SemEval-2026 Task 1: A Multi-Stage Pipeline Walks Into a Benchmark and Barely Clears the Bar

arXiv:2607. 13189v1 Announce Type: cross Abstract: We present RAGthoven, our system for SemEval-2026 Task 1 (MWAHAHA), Subtask A (multilingual constrained humor generation in English, Spanish, and Chinese).

By Marek \v{S}uppa, Vikt\'oria Ondrejov\'a, Lucia Ganajov\'a, Gregor Karetka, Daniel Skala

llms rag agents benchmarks

arXiv AI

Jul 16

The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models

arXiv:2607. 13612v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) are the dominant design for latent world models, yet they are usually justified by empirical performance rather than a normative principle.

By Fabio Arnez, Alexandra Gomez-Villa

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arXiv Machine Learning

Jul 16

The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model

arXiv:2607. 13660v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyperspherical geometry.

By Zijie Yu, Gaowen Liu, Ramana Rao Kompella, Philip S. Yu, Yue Song

rag diffusion multimodal

arXiv AI

Jul 16

Classifying daily activities needs posture, reconstructing them needs motion

arXiv:2607. 13216v1 Announce Type: cross Abstract: Humans recognize movements effortlessly, even from noisy and complex visual input.

By Arefeh Farahmandi, Gunnar Blohm

rag

arXiv AI

Jul 16

Fre-Res: Frequency-Residual Video Token Compression for Efficient Video MLLMs

arXiv:2605. 16366v2 Announce Type: replace-cross Abstract: Video MLLMs face a persistent tension between spatial fidelity and temporal coverage: preserving fine-grained visual details requires many spatial tokens, while capturing short-lived events requires dense temporal sampling.

By Yigui Feng (The College of Computer Science, National University of Defense Technology, Changsha, Hunan, China), Qinglin Wang (The College of Computer Science, National University of Defense Technology, Changsha, Hunan, China), Yang Liu (The Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, Guangdong, China), Jie Liu (The College of Computer Science, National University of Defense Technology, Changsha, Hunan, China)

rag diffusion benchmarks

arXiv Machine Learning

Jul 16

Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach

arXiv:2511. 01680v4 Announce Type: replace-cross Abstract: Social scientists are increasingly turning to unstructured datasets to unlock new empirical insights, e.

By Jacob Carlson

rag safety

arXiv AI

Jul 16

Earthquaker-AI: A Retrieval-Augmented Generation Framework with Rubric-Based Assessment for Primary School Earthquake Education

arXiv:2607. 14046v1 Announce Type: new Abstract: This paper presents Earthquaker-AI, a hybrid educational framework building upon a previously implemented educational robotics project by integrating a conversational AI assistant based on Retrieval-Augmented Generation.

By Xanthi Kokkinou, Chaido Mizeli, Nafsika Koulaxidou, Marina Delianidi, Konstantinos Diamantaras

rag robotics safety

arXiv AI

Jul 16

Inverse-LLaVA: Rethinking Multimodal Alignment via Text-to-Vision Mapping

arXiv:2508. 12466v2 Announce Type: replace-cross Abstract: Traditional multimodal learning approaches rely on alignment pre-training to bridge vision and language modalities, typically by projecting visual features into discrete text token spaces using large-scale image--text data.

By Xuhui Zhan, Tyler Derr

llms rag multimodal benchmarks safety

arXiv AI

Jul 16

DIVE: Embedding Compression via Self-Limiting Gradient Updates

arXiv:2605. 20689v2 Announce Type: replace-cross Abstract: High-dimensional language-model embeddings increase storage and search costs, while supervised compressors can overfit when relevance labels are scarce.

By Dongfang Zhao

rag efficiency benchmarks

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