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 30

ReactiveBFM: Reactive Closed-Loop Motion Planning Towards Universal Humanoid Whole-Body Control

arXiv:2606. 30362v1 Announce Type: cross Abstract: While current Behavior Foundation Models (BFMs) provide robust control priors for humanoids, they only execute pre-defined reference motions.

By Xiao Chen, Weishuai Zeng, Xiaojie Niu, Zirui Wang, Jianan Li, Huayi Wang, Furui Xu, Jiahe Chen, Weixiang Zhong, Lihe Ding, Kailin Li, Jiangmiao Pang, Tai Wang, Tianfan Xue, Jingbo Wang

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arXiv AI

Jun 30

Em-Garde: A Propose-Match Framework for Proactive Streaming Video Understanding

arXiv:2603. 19054v2 Announce Type: replace-cross Abstract: Recent advances in Streaming Video Understanding has enabled a new interaction paradigm where models respond proactively to user queries.

By Yikai Zheng, Xin Ding, Yifan Yang, Shiqi Jiang, Hao Wu, Qianxi Zhang, Weijun Wang, Ting Cao, Yunxin Liu

rag

arXiv AI

Jun 30

AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering

arXiv:2606. 29090v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty.

By Ansh Kamthan

llms rag nlp

arXiv AI

Jun 30

High-Dimensional Concentration and Retrieval Instability in Embedding Spaces: Implications for Retrieval-Augmented Generation

arXiv:2606. 28330v1 Announce Type: cross Abstract: Embedding-based retrieval systems rely on the assumption that geometric proximity in highdimensional representation spaces reflects semantic relevance.

By Ernesto Lopez Fune (DE)

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arXiv AI

Jun 30

ConCise: Training-Free Conclusion-Chain State Compression for Cost-Efficient Multi-Step RAG Services

arXiv:2606. 28361v1 Announce Type: cross Abstract: Multi-step retrieval-augmented generation (RAG) has been widely deployed as LLM-powered web services for complex question answering, where iterative retrieval-reasoning rounds deliver strong multi-hop accuracy.

By Kuan Yan, Zhiqing Tang, Tian Wang, Weijia Jia

llms rag nlp efficiency

arXiv AI

Jun 30

BioProVLA-Agent: An Affordable, Protocol-Driven, Vision-Enhanced VLA-Enabled Embodied Multi-Agent System with Closed-Loop-Capable Reasoning for Biological Laboratory Manipulation

arXiv:2605. 07306v2 Announce Type: replace-cross Abstract: Biological laboratory automation can reduce repetitive manual work and improve reproducibility, but reliable embodied execution in wet-lab environments remains challenging.

By Zhaohui Du, Zhe Wang, Hongmei Fei, Xiwen Cao, Ting Xiao, Qi Wang, Huanbo Jin, Jiaming Gu, Quan Lu, Zhe Liu

llms rag agents robotics multimodal benchmarks

arXiv AI

Jun 30

Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach

arXiv:2604. 19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools.

By Pritam Kar, Gouri Lakshmi S, Saptarshi Bej

rag diffusion benchmarks

arXiv AI

Jun 30

Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss

arXiv:2606. 29901v1 Announce Type: cross Abstract: Sound event detection (SED) is a core module for acoustic environmental analysis, yet its performance is often limited by scarce labeled data.

By Nian Shao, Xian Li, Xiaofei Li

rag fine-tuning

arXiv AI

Jun 30

NEURON: A Neuro-symbolic System for Grounded Clinical Explainability

arXiv:2605. 01189v2 Announce Type: replace Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability.

By Anuradha Chandrasekaran, Dimitrios Zikos, Mutlu Mete, Alan Pang, Brady D. Lund, Kewei Sha

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arXiv Machine Learning

Jun 30

Room Scene Discovery and Grouping in Unstructured Vacation Rental Image Collections

arXiv:2507. 00263v2 Announce Type: replace-cross Abstract: The rapid growth of vacation rental (VR) platforms has led to an increasing volume of property images, often uploaded without structured categorization.

By Vignesh Ram Nithin Kappagantula, Shayan Hassantabar

llms rag multimodal

arXiv AI

Jun 30

XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation

arXiv:2412. 15529v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is not only contextually relevant but also accurate and current.

By Qili Zhang, Qianren Mao, Yangyifei Luo, Yashuo Luo, Hanwen Hao, Zhilong Cao, Weifeng Jiang, Zhijun Chen, Junnan Liu, Feng Yan, Xiaolong Wang, Jinlong Zhang, Zhenting Huang, Zhixing Tan, Jie Sun, Bo Li, Jianxin Li, Philip S. Yu

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arXiv Machine Learning

Jun 30

FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks

arXiv:2606. 30336v1 Announce Type: new Abstract: We introduce FlexTab, a flexible encoder-decoder architecture for in-context learning on tabular data that pairs a single, task-agnostic encoder with a suite of task-specific decoders.

By Marek Polewczyk, Maximilian Schambach, Marco Spinaci, Sam Thelin, Johannes H\"ohne

llms rag benchmarks

arXiv AI

Jun 30

Estimating Grammatical Gender Directions in Contextual Embeddings under Controlled and Natural Contexts

arXiv:2606. 30152v1 Announce Type: cross Abstract: Contextual language models conflate grammatical gender and social semantic bias in gendered languages such as Spanish.

By Huanping Xiao, Yingji Li

llms rag safety

arXiv AI

Jun 30

Relevance Is Not Permission: Warranted Attention for Value Contributions

arXiv:2606. 30139v1 Announce Type: new Abstract: Relevance is not permission.

By Minwoo Yu, Young-guk Ha

rag

arXiv Machine Learning

Jun 30

Inference-time optimization for experiment-grounded protein ensemble generation

arXiv:2602. 24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data.

By Advaith Maddipatla, Anar Rzayev, Marco Pegoraro, Martin Pacesa, Paul Schanda, Ailie Marx, Sanketh Vedula, Alex M. Bronstein

rag diffusion benchmarks safety

arXiv Machine Learning

Jun 30

Spectral Embedding via Chebyshev Bases for Robust DeepONet Approximation

arXiv:2512. 09165v2 Announce Type: replace Abstract: Deep Operator Networks (DeepONets) have emerged as a powerful framework for data-driven operator learning, providing flexible surrogates for nonlinear mappings arising in partial differential equations (PDEs).

By Muhammad Abid, Omer San

rag diffusion benchmarks safety

arXiv AI

Jun 30

SCARCE: Scalable Cascade Analysis for Rare-event Characterisation via Embeddings

arXiv:2606. 29623v1 Announce Type: new Abstract: Rare events govern the safety profile of modern AI systems, yet their probabilities are extremely difficult to estimate: direct Monte Carlo requires prohibitive sample budgets.

By Yingjie Wang, Yi Dong, Edmund Lau, Jie Meng, Taylor T Johnson, Xiaowei Huang

llms rag safety

arXiv Machine Learning

Jun 30

Zero-Label Driving Scenario Complexity Detection via Joint Embedding Predictive Architecture

arXiv:2606. 28383v1 Announce Type: cross Abstract: Identifying complex and safety-critical driving scenarios in large unlabelled datasets is an important but expensive problem.

By Santosh Jaiswal

rag agents benchmarks safety

arXiv AI

Jun 30

DEEPMED Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification

arXiv:2606. 29746v1 Announce Type: new Abstract: Navigating the deluge of heterogeneous medical data, from academic literature (PubMed) to clinical guidelines (Web) and private knowledge bases, remains a critical bottleneck for evidence-based medicine.

By Maolin Liu, Fanyu Xu, Ruoqing Xu, Jiahang Zhang, Hao Wang, Rui Wang

rag agents

arXiv AI

Jun 30

The Crowded Embedding Space: A Mean-Field Mechanism for Emergent Marginalization in Retrieval-Augmented Agents

arXiv:2606. 28343v1 Announce Type: cross Abstract: Retrieval-augmented generative agents rely on retrieval for grounding, yet are typically evaluated on a query-by-query basis.

By Shwan Ashrafi, Dan Roth

rag agents safety

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