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

Jun 16

When Do We Need LLMs? A Diagnostic for Language-Driven Bandits

arXiv:2604. 05859v2 Announce Type: replace Abstract: We study Contextual Multi-Armed Bandits (CMABs) for non-episodic decision-making problems where the context includes both textual and numerical information (e.

By Uljad Berdica, Fernando Acero, Anton Ipsen, Parisa Zehtabi, Michael Cashmore, Manuela Veloso

llms rag reinforcement-learning

arXiv AI

Jun 16

RAID: Semantic Graph Diffusion for True Cold-Start and Cross-Lingual Forecasting

arXiv:2606. 16925v1 Announce Type: new Abstract: Time-series foundation models show strong transfer performance when given a non-empty history window.

By Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R., Arun Prakash, S. Senthilkumar

rag diffusion efficiency

arXiv AI

Jun 16

Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments

arXiv:2606. 15766v1 Announce Type: new Abstract: A central pedagogical value evaluated in AI tutor benchmarks is scaffolding: guiding students through graduated steps toward a solution.

By Alexandra Neagu, Jeffrey T. H. Wong, Marcus Messer, Rhodri Nelson, Peter B. Johnson

llms rag benchmarks safety

arXiv AI

Jun 16

AI-Driven Framework for Adaptive Water Network Management with Proof-of-Concept Implementation: Addressing Non-Revenue Water in Jordan

arXiv:2606. 15709v1 Announce Type: new Abstract: Jordan faces severe water scarcity with 50\% of water produced is lost to leakage, theft and metering issues also known as non-revenue water (NRW).

By Mohammed Fasha, Nahel Al-Maayta, Bilal Sowan, Mohammad Athamneh, Husam Barham

llms rag agents safety

arXiv AI

Jun 16

ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing

arXiv:2606. 15315v1 Announce Type: new Abstract: Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user preferences into routing algorithms.

By Tingting Yang, Chenhao Xue, Jun Chen

llms rag fine-tuning

arXiv AI

Jun 16

Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

arXiv:2606. 14941v1 Announce Type: new Abstract: Time series forecasting models often benefit from historical patterns.

By Shiqiao Zhou, Zipeng Wu, Holger Sch\"oner, Edouard Fouch\'e, IAG Wilson, Shuo Wang

rag multimodal benchmarks

arXiv AI

Jun 16

CONCORD: Asynchronous Sparse Aggregation for Device-Cloud RAG under Document Isolation

arXiv:2606. 15179v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has emerged as a pivotal technique for improving language models by incorporating external knowledge at inference time.

By Xuedong Hu, Zhiqing Tang, Zhi Yao, Tian Wang, Weijia Jia

llms rag efficiency safety

arXiv AI

Jun 16

Hierarchical Modeling of ICD Codes in EHR Foundation Models

arXiv:2606. 15447v1 Announce Type: new Abstract: Electronic health record foundation models typically treat ICD diagnosis codes as flat tokens, overlooking the clinically meaningful hierarchical structure that captures disease families, subcategories, and fine-grained diagnostic detail.

By Megha Thukral, Dong Gyun Kang, Rudra Pratap Singh, Shruthi Kashinath Hiremath, Katrin H\"ansel, Thomas Pl\"otz

llms rag safety

arXiv AI

Jun 16

daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization

arXiv:2606. 16497v1 Announce Type: cross Abstract: GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective.

By Dayuan Fu, Mohan Jiang, Tongyu Wang, Dian Yang, Jiarui Hu, Liming Liu, Jinlong Hou, Pengfei Li

llms rag agents nlp reinforcement-learning fine-tuning

arXiv AI

Jun 16

Metacognitive Myopia in Large Language Models

arXiv:2408. 05568v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally embedded stereotypes, influence moral judgments, or amplify positive evaluations of majority groups.

By Florian Scholten, Tobias R. Rebholz, Mandy H\"utter

llms rag agents

arXiv Machine Learning

Jun 16

Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction

arXiv:2605. 09697v3 Announce Type: replace-cross Abstract: In many real-world computer vision applications, including medical imaging and industrial inspection, binary classification tasks are characterized by a severe scarcity of positive samples.

By Radhika Amar Desai, Modigari Narendra

rag computer-vision data-engineering

arXiv Machine Learning

Jun 16

Generative Modeling on Metric Graphs via Neural Optimal Transport

arXiv:2606. 16273v1 Announce Type: cross Abstract: We introduce, to our knowledge, the first deep generative modeling framework for probability distributions continuously supported on compact metric graphs.

By Alessandro Micheli, Yueqi Cao, Anthea Monod, Samir Bhatt

rag

arXiv AI

Jun 16

Mitigating Visual Hallucinations in Multimodal Systems through Retrieval-Augmented Reliability-Aware Inference

arXiv:2606. 15782v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-language understanding and natural-language response generation.

By Pratheswaran Hariharan, Haiping Xu, Donghui Yan

llms rag multimodal safety

arXiv Machine Learning

Jun 16

libhmm: A Modern C++20 Library for Hidden Markov Models with Correct MLE Emission M-Steps

arXiv:2605. 29208v2 Announce Type: replace-cross Abstract: We describe libhmm, a C++20 library for Hidden Markov Model parameter estimation, sequence decoding, and model selection.

By Gary Wolfman

rag benchmarks

arXiv AI

Jun 16

Disentangling Hallucinations: Orthogonal Semantic Projection for Robust Interpretability

arXiv:2606. 14758v1 Announce Type: cross Abstract: As Vision-Language Models are increasingly deployed in safety-critical applications, the trustworthiness of their explanations becomes crucial.

By Emirhan Bilgi\c{c}, Baptiste Caramiaux, Zhi Yan, Gianni Franchi

llms rag multimodal safety

arXiv Machine Learning

Jun 16

Active Learning with Low-Rank Structure for Data Selection

arXiv:2606. 16045v1 Announce Type: new Abstract: In the data selection problem, the objective is to choose a small, representative subset of data that can be used to efficiently train a machine learning model.

By Vincent Cohen-Addad, Sasidhar Kunapuli, Vahab Mirrokni, Mahdi Nikdan, David P. Woodruff, Samson Zhou

rag

arXiv AI

Jun 16

Pixel-TTS: Image based Text Rendering for Robust Text-to-Speech

arXiv:2606. 14750v1 Announce Type: cross Abstract: Recent advances in pixel-based text modeling show that representing text as images enables models to exploit visual cues for language understanding.

By Adarsh Arigala, Arjun Gangwar, S Umesh, Yova Kementchedjhieva

rag fine-tuning multimodal

arXiv AI

Jun 16

Discrete optimal transport is a strong audio adversarial attack

arXiv:2509. 14959v3 Announce Type: replace-cross Abstract: In this paper, we investigate discrete optimal transport (DOT) as a black-box attack against modern automatic speaker verification (ASV) and anti-spoofing countermeasure (CM) systems.

By Anton Selitskiy, Akib Shahriyar, Jishnuraj Prakasan

rag fine-tuning safety

arXiv AI

Jun 16

Near--Real-Time Conflict-Related Fire Detection in Sudan Using Unsupervised Deep Learning

arXiv:2512. 07925v4 Announce Type: replace-cross Abstract: Ongoing armed conflict in Sudan highlights the need for rapid monitoring of conflict-related fire-affected areas.

By Kuldip Singh Atwal, Dieter Pfoser, Daniel Rothbart

rag

arXiv AI

Jun 16

ArtNet: A JEPA-Like Articulatory Predictive Framework for Robust Zero-Shot Phoneme Recognition

arXiv:2606. 16595v1 Announce Type: cross Abstract: Zero-shot cross-lingual phoneme recognition is often hindered by the fragility of direct acoustic-to-symbol mapping, which is susceptible to language-specific variations.

By Zeqian Hu, Fuliang Weng, Shu Shang, Yaqian Zhou

rag safety

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