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 24

FALCON: Transforming Cyber Threat Intelligence into Deployable IDS Rules with Self-Reflection

arXiv:2508. 18684v2 Announce Type: replace-cross Abstract: Signature-based Intrusion Detection Systems (IDS) detect malicious activity by matching network or host events against predefined rules.

By Shaswata Mitra, Subash Neupane, Martin Duclos, Sudip Mittal, Aritran Piplai, Md Rayhanur Rahman, Edward Zieglar, Shahram Rahimi

llms rag agents safety

arXiv AI

Jun 24

DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects

arXiv:2606. 24779v1 Announce Type: cross Abstract: Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability.

By Shiyu Li, Ziqi Yan, Zhihao Wu, Jielong Lu, Weiran Liao, Jiajun Yu, Genjie Li, Zeyu Chu, Jiajun Bu, Haishuai Wang

llms rag agents benchmarks

arXiv Machine Learning

Jun 24

Verifiable Foundation Models for Robot Safety

arXiv:2606. 23754v1 Announce Type: cross Abstract: Deploying foundation models for robot control raises a central challenge: the expressive power that enables rich, multimodal perception also makes these models opaque and difficult to analyze formally, rendering them intractable for existing verification tools.

By Davide Corsi, Kyungmin Kim, Roy Fox

rag robotics multimodal safety

arXiv Machine Learning

Jun 24

Scalable Physics-Inspired Transformers for Spin Glasses

arXiv:2606. 22984v2 Announce Type: replace-cross Abstract: Efficient sampling of the Boltzmann distribution in frustrated spin glasses is central to statistical mechanics and combinatorial optimization.

By Lu Zhong, Wenli Duan, Jing Liu, Pan Zhang, Ying Tang

llms rag

arXiv AI

Jun 24

G$^3$VLA: Geometric inductive bias for Vision-Language-Action Models

arXiv:2606. 24472v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have made rapid progress in generalist robot manipulation by harnessing semantic knowledge from pretrained vision-language backbones, but their visual tokens remain grounded in 2D image coordinates rather than the calibrated geometry of the robot's cameras -- a mismatch especially pronounced in multi-camera setups, where views are coupled by known intrinsics and extrinsics yet processed as independent images.

By Yue Peng, Yongzhe Zhao, Artur Habuda, Khuyen Pham, Yanheng Zhu, Tran Nguyen Le, Fares Abu-Dakka, Li Guo

rag robotics multimodal safety

arXiv Machine Learning

Jun 24

Does My Embedding Reflect That $A = B$? Evaluating Mathematical Equivalence in Embedding Models

arXiv:2606. 23959v1 Announce Type: cross Abstract: Because mathematics is highly abstract, a single statement can take very different forms depending on what subfield it is framed in.

By Jiaying Ye, Samarth Rao, Leo Carlin, Kedar Chintalapati, Saharsh Bhargava, Rachit Jaiswal, Michael Zhou, Jared Darlington, Jarod Alper, Vasily Ilin, Henry Kvinge

rag benchmarks

arXiv AI

Jun 24

Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems

arXiv:2606. 24416v1 Announce Type: new Abstract: Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective.

By Bingnan Xiao, Chenhao Yang, Wei Ni, Xin Wang, Tony Q. S. Quek

rag agents

arXiv AI

Jun 24

Event-Grounded Question Answering over Long Audio via Structured Retrieval

arXiv:2602. 14612v4 Announce Type: replace-cross Abstract: Answering natural-language questions over multi-hour audio requires both event recognition and temporal grounding.

By Kartik Hegde, Arvind Krishna Sridhar, Naveen Vakada, Yinyi Guo, Erik Visser

llms rag nlp benchmarks data-engineering

arXiv Machine Learning

Jun 24

An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data

arXiv:2606. 24459v1 Announce Type: new Abstract: Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultaneously in industrial environments.

By Jinghan Wang, Feng Cheng, Wentao Wu, Hang Li, Gaoliang Peng, Tianchen Liu

llms rag fine-tuning benchmarks safety

arXiv Machine Learning

Jun 24

Selective Rotary Position Embedding

arXiv:2511. 17388v3 Announce Type: replace-cross Abstract: Position information is essential for language modeling.

By Sajad Movahedi, Timur Carstensen, Arshia Afzal, Frank Hutter, Antonio Orvieto, Volkan Cevher

llms rag

arXiv AI

Jun 24

Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation

arXiv:2606. 24042v1 Announce Type: new Abstract: Recommender systems often induce filter bubbles and semantic homogenization by monolithically optimizing for immediate user engagement.

By Cl\'audio L\'ucio Do Val Lopes, Lucca Machado da Silva, Andr\'e de Oliveira Brand\~ao

rag agents reinforcement-learning safety

arXiv AI

Jun 24

The Latent Bridge: A Continuous Slow-Fast Channel for Real-Time Game Agents

arXiv:2606. 24470v1 Announce Type: new Abstract: A real-time agent for general computer use - with games as the most demanding case - must act within tens of milliseconds while still planning over seconds.

By Bojie Li, Noah Shi

rag agents multimodal

arXiv Machine Learning

Jun 24

Which Spaces can be Embedded in $L_p$-type Reproducing Kernel Banach Space? A Characterization via Metric Entropy

arXiv:2410. 11116v4 Announce Type: replace-cross Abstract: In this paper, we establish a novel connection between the metric entropy growth and the embeddability of function spaces into reproducing kernel Hilbert/Banach spaces.

By Yiping Lu, Daozhe Lin, Qiang Du

rag

arXiv AI

Jun 24

ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning

arXiv:2606. 24601v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives.

By Anurag Akula, Satheesh K. Perepu, Abhishek Sarkar, Kaushik Dey

rag agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 24

When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

arXiv:2605. 08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input.

By Harshvardhan Saini, Samyak Jha, Yiming Tang, Dianbo Liu

llms rag agents fine-tuning multimodal benchmarks safety

arXiv AI

Jun 24

Evaluating the Interpretability of Sparse Autoencoders with Concept Annotations

arXiv:2606. 24716v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy metrics or qualitative inspection rather than measuring semantic correspondence.

By Jonas Klotz, Cassio F. Dantas, Pallavi Jain, Diego Marcos, Beg\"um Demir

llms rag benchmarks safety

arXiv AI

Jun 24

Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity

arXiv:2606. 24623v1 Announce Type: cross Abstract: Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts.

By Yuanhe Zhao, Tianyu Zhang, Huafei Xing, Derek F. Wong, Jianbin Li, Tao Fang

llms rag agents safety

arXiv AI

Jun 24

RASC+: Retrieval-Constrained LLM Adjudication for Clinical Value Set Authoring

arXiv:2606. 23992v1 Announce Type: cross Abstract: Clinical value sets define the standardized terminology codes used in quality measurement, phenotyping, cohort construction, and clinical decision support.

By Sumit Mukherjee

llms rag benchmarks safety

arXiv AI

Jun 24

Can Scale Save Us From Plasticity Loss in Large Language Models?

arXiv:2606. 24752v1 Announce Type: new Abstract: The loss of plasticity - the ability of a network to learn new information after having already learned older information - is a fundamental challenge in creating artificial neural networks capable of continual learning.

By J. Fernando Hernandez-Garcia, Tom\'as Figliolia, Beren Millidge

llms rag

arXiv AI

Jun 24

LemonHarness Technical Report

arXiv:2606. 24311v1 Announce Type: new Abstract: As large language model (LLM) agents are applied to longer tasks, they increasingly modify workspace state across multiple rounds of iteration.

By Kailong Ren, Fubo Sun, Jiachen Liu, Liu Yang, Zimo Yin, Jiaying Li, Congli Yin, Ming He, Yu Huo, Jiawei Liu, Zeping Chen, Yubin Huangfu, Ronghua Li, Yixuan Wu, Xing Su, Yanzhi Xu, Likang Wu, Hongke Zhao, Lei Zhang, Xiaohui Geng, Jianping Fan

llms rag agents

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