Tootfinder

Seeing the Forest Through the Trees: Knowledge Retrieval for Streamlining Particle Physics Analysis
James McGreivy, Blaise Delaney, Anja Beck, Mike Williams
https://arxiv.org/abs/2509.06855

Seeing the Forest Through the Trees: Knowledge Retrieval for Streamlining Particle Physics Analysis
Generative Large Language Models (LLMs) are a promising approach to structuring knowledge contained within the corpora of research literature produced by large-scale and long-running scientific collaborations. Within experimental particle physics, such structured knowledge bases could expedite methodological and editorial review. Complementarily, within the broader scientific community, generative LLM systems grounded in published work could make for reliable companions allowing non-experts to …

DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation
Xinyu Gao, Xiangtao Meng, Yingkai Dong, Zheng Li, Shanqing Guo
https://arxiv.org/abs/2509.06026

DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation
While Retrieval-Augmented Generation (RAG) effectively reduces hallucinations by integrating external knowledge bases, it introduces vulnerabilities to membership inference attacks (MIAs), particularly in systems handling sensitive data. Existing MIAs targeting RAG's external databases often rely on model responses but ignore the interference of non-member-retrieved documents on RAG outputs, limiting their effectiveness. To address this, we propose DCMI, a differential calibration MIA that miti…

I keep giving AI a chance, but it keeps generating bullshit no matter what I type. I’m always left searching for a human response to what should be established and reasonable responses from any knowledgeable source.
This affirms my feeling that AI tools are not knowledge bases.

Data Augmentation for Visualization Design Knowledge Bases
Hyeok Kim, Jeffrey Heer
https://arxiv.org/abs/2508.02216 https://arxiv.org/pdf/2508.02216…

Data Augmentation for Visualization Design Knowledge Bases
Visualization knowledge bases enable computational reasoning and recommendation over a visualization design space. These systems evaluate design trade-offs using numeric weights assigned to different features (e.g., binning a variable). Feature weights can be learned automatically by fitting a model to a collection of chart pairs, in which one chart is deemed preferable to the other. To date, labeled chart pairs have been drawn from published empirical research results; however, such pairs are …

Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
Xinran Li, Chenjia Bai, Zijian Li, Jiakun Zheng, Ting Xiao, Jun Zhang
https://arxiv.org/abs/2506.07232

Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities, making them promising tools for complex, multi-agent planning in embodied environments. However, despite LLMs' advanced abilities and the sophisticated modular design of agentic methods, existing LLM-based planning algorithms remain limited by weak adaptation capabilities to multi-agent embodied scenarios. We address this limitation by introducing a framework that enables LLM agents to learn and ev…

Knowledge engineering for open science: Building and deploying knowledge bases for metadata standards
Mark A. Musen, Martin J. O'Connor, Josef Hardi, Marcos Martinez-Romero
https://arxiv.org/abs/2507.22391

Knowledge engineering for open science: Building and deploying knowledge bases for metadata standards
Scientists strive to make their datasets available in open repositories, with the goal that they be findable, accessible, interoperable, and reusable (FAIR). Although it is hard for most investigators to remember all the guiding principles associated with FAIR data, there is one overarching requirement: The data need to be annotated with rich, discipline-specific, standardized metadata. The Center for Expanded Data Annotation and Retrieval (CEDAR) builds technology that enables scientists to en…

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Yoon Huh, Bumjun Kim, Wan Choi
https://arxiv.org/abs/2508.03248 https://

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Recent advancements in semantic communication have primarily focused on image transmission, where neural network (NN)-based joint source-channel coding (JSCC) modules play a central role. However, such systems often experience semantic communication errors due to mismatched knowledge bases between users and performance degradation from outdated models, necessitating regular model updates. To address these challenges in vector quantization (VQ)-based image semantic communication systems, we prop…

Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitation
Yufei Chen, Yao Wang, Haibin Zhang, Tao Gu
https://arxiv.org/abs/2507.23229

Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitation
Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge bases, but this advancement introduces significant privacy risks. Existing privacy attacks on RAG systems can trigger data leakage but often fail to accurately isolate knowledge-base-derived sentences within mixed responses. They also lack robustness when applied across multiple domains. This paper addresses these challenges by presenting a novel black-box attack framework that ex…

ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Zilu Dong, Xiangqing Shen, Zinong Yang, Rui Xia
https://arxiv.org/abs/2507.08427 …

ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Current knowledge editing methods for large language models (LLMs) struggle to maintain logical consistency when propagating ripple effects to associated facts. We propose ChainEdit, a framework that synergizes knowledge graph-derived logical rules with LLM logical reasoning capabilities to enable systematic chain updates. By automatically extracting logical patterns from structured knowledge bases and aligning them with LLMs' internal logics, ChainEdit dynamically generates and edits logically…

A Rule-Based Approach to Specifying Preferences over Conflicting Facts and Querying Inconsistent Knowledge Bases
Meghyn Bienvenu, Camille Bourgaux, Katsumi Inoue, Robin Jean
https://arxiv.org/abs/2508.07742

A Rule-Based Approach to Specifying Preferences over Conflicting Facts and Querying Inconsistent Knowledge Bases
Repair-based semantics have been extensively studied as a means of obtaining meaningful answers to queries posed over inconsistent knowledge bases (KBs). While several works have considered how to exploit a priority relation between facts to select optimal repairs, the question of how to specify such preferences remains largely unaddressed. This motivates us to introduce a declarative rule-based framework for specifying and computing a priority relation between conflicting facts. As the express…

Generating Actionable Robot Knowledge Bases by Combining 3D Scene Graphs with Robot Ontologies
Giang Nguyen, Mihai Pomarlan, Sascha Jongebloed, Nils Leusmann, Minh Nhat Vu, Michael Beetz
https://arxiv.org/abs/2507.11770

Generating Actionable Robot Knowledge Bases by Combining 3D Scene Graphs with Robot Ontologies
In robotics, the effective integration of environmental data into actionable knowledge remains a significant challenge due to the variety and incompatibility of data formats commonly used in scene descriptions, such as MJCF, URDF, and SDF. This paper presents a novel approach that addresses these challenges by developing a unified scene graph model that standardizes these varied formats into the Universal Scene Description (USD) format. This standardization facilitates the integration of these …

SE-VLN: A Self-Evolving Vision-Language Navigation Framework Based on Multimodal Large Language Models
Xiangyu Dong, Haoran Zhao, Jiang Gao, Haozhou Li, Xiaoguang Ma, Yaoming Zhou, Fuhai Chen, Juan Liu
https://arxiv.org/abs/2507.13152

SE-VLN: A Self-Evolving Vision-Language Navigation Framework Based on Multimodal Large Language Models
Recent advances in vision-language navigation (VLN) were mainly attributed to emerging large language models (LLMs). These methods exhibited excellent generalization capabilities in instruction understanding and task reasoning. However, they were constrained by the fixed knowledge bases and reasoning abilities of LLMs, preventing fully incorporating experiential knowledge and thus resulting in a lack of efficient evolutionary capacity. To address this, we drew inspiration from the evolution cap…

Digital gazetteers: review and prospects for place name knowledge bases
Kalana Wijegunarathna, Kristin Stock, Christopher B. Jones
https://arxiv.org/abs/2507.08553

Digital gazetteers: review and prospects for place name knowledge bases
Gazetteers typically store data on place names, place types and the associated coordinates. They play an essential role in disambiguating place names in online geographical information retrieval systems for navigation and mapping, detecting and disambiguating place names in text, and providing coordinates. Currently there are many gazetteers in use derived from many sources, with no commonly accepted standard for encoding the data. Most gazetteers are also very limited in the extent to which th…

Safeguarding Multimodal Knowledge Copyright in the RAG-as-a-Service Environment
Tianyu Chen, Jian Lou, Wenjie Wang
https://arxiv.org/abs/2506.10030 https:/…

Safeguarding Multimodal Knowledge Copyright in the RAG-as-a-Service Environment
As Retrieval-Augmented Generation (RAG) evolves into service-oriented platforms (Rag-as-a-Service) with shared knowledge bases, protecting the copyright of contributed data becomes essential. Existing watermarking methods in RAG focus solely on textual knowledge, leaving image knowledge unprotected. In this work, we propose AQUA, the first watermark framework for image knowledge protection in Multimodal RAG systems. AQUA embeds semantic signals into synthetic images using two complementary meth…

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Aakanksha Saha, Martina Lindorfer, Juan Caballero
https://arxiv.org/abs/2506.10645

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Indicators of Compromise (IOCs) such as IP addresses, file hashes, and domain names are commonly used for threat detection and attribution. However, IOCs tend to be short-lived as they are easy to change. As a result, the cybersecurity community is shifting focus towards more persistent behavioral profiles, such as the Tactics, Techniques, and Procedures (TTPs) and the software used by a threat group. However, the distinctiveness and completeness of such behavioral profiles remain largely unexp…

MExplore: an entity-based visual analytics approach for medical expertise acquisition
Xiao Pang, Yan Huang, Chang Liu, JiYuan Liu, MingYou Liu
https://arxiv.org/abs/2507.12337

MExplore: an entity-based visual analytics approach for medical expertise acquisition
Acquiring medical expertise is a critical component of medical education and professional development. While existing studies focus primarily on constructing medical knowledge bases or developing learning tools based on the structured, private healthcare data, they often lack methods for extracting expertise from unstructured medical texts. These texts constitute a significant portion of medical literature and offer greater flexibility and detail compared to structured data formats. Furthermore…

VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design
Leonardo Ferreira, Gustavo Moreira, Fabio Miranda
https://arxiv.org/abs/2508.07497 https://

VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design
Designing and building visual analytics (VA) systems is a complex, iterative process that requires the seamless integration of data processing, analytics capabilities, and visualization techniques. While prior research has extensively examined the social and collaborative aspects of VA system authoring, the practical challenges of developing these systems remain underexplored. As a result, despite the growing number of VA systems, there are only a few structured knowledge bases to guide their d…