Tootfinder

Bitcoin Cross-Chain Bridge: A Taxonomy and Its Promise in Artificial Intelligence of Things
Guojun Tang, Carylyne Chan, Ning Nan, Spencer Yang, Jiayu Zhou, Henry Leung, Mohammad Mamun, Steve Drew
https://arxiv.org/abs/2509.10413

Bitcoin Cross-Chain Bridge: A Taxonomy and Its Promise in Artificial Intelligence of Things
Bitcoin's limited scripting capabilities and lack of native interoperability mechanisms have constrained its integration into the broader blockchain ecosystem, especially decentralized finance (DeFi) and multi-chain applications. This paper presents a comprehensive taxonomy of Bitcoin cross-chain bridge protocols, systematically analyzing their trust assumptions, performance characteristics, and applicability to the Artificial Intelligence of Things (AIoT) scenarios. We categorize bridge design…

A Taxonomy of Response Strategies to Toxic Online Content: Evaluating the Evidence
Lisa Schirch, Kristina Radivojevic, Cathy Buerger
https://arxiv.org/abs/2509.09921 https://

A Taxonomy of Response Strategies to Toxic Online Content: Evaluating the Evidence
Toxic Online Content (TOC) includes messages on digital platforms that are harmful, hostile, or damaging to constructive public discourse. Individuals, organizations, and LLMs respond to TOC through counterspeech or counternarrative initiatives. There is a wide variation in their goals, terminology, response strategies, and methods of evaluating impact. This paper identifies a taxonomy of online response strategies, which we call Online Discourse Engagement (ODE), to include any type of online …

On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomy
Aline Mangold, Juliane Zietz, Susanne Weinhold, Sebastian Pannasch
https://arxiv.org/abs/2510.12201

On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomy
As AI becomes more common in everyday living, there is an increasing demand for intelligent systems that are both performant and understandable. Explainable AI (XAI) systems aim to provide comprehensible explanations of decisions and predictions. At present, however, evaluation processes are rather technical and not sufficiently focused on the needs of human users. Consequently, evaluation studies involving human users can serve as a valuable guide for conducting user studies. This paper presen…

3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review
Salma Galaaoui, Eduardo Valle, David Picard, Nermin Samet
https://arxiv.org/abs/2509.12197 https://

3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review
In this paper, we present a comprehensive review of 3D human pose estimation and human mesh recovery from in-the-wild LiDAR point clouds. We compare existing approaches across several key dimensions, and propose a structured taxonomy to classify these methods. Following this taxonomy, we analyze each method's strengths, limitations, and design choices. In addition, (i) we perform a quantitative comparison of the three most widely used datasets, detailing their characteristics; (ii) we compile u…

Acoustic Overspecification in Electronic Dance Music Taxonomy
Weilun Xu, Tianhao Dai, Oscar Goudet, Xiaoxuan Wang
https://arxiv.org/abs/2509.11474 https://…

Acoustic Overspecification in Electronic Dance Music Taxonomy
Electronic Dance Music (EDM) classification typically relies on industry-defined taxonomies with numerous subgenres, yet the acoustic basis for these distinctions remains unclear. Current approaches use supervised learning with prescribed genre labels, assuming their validity without systematic evaluation. In this paper, we propose an unsupervised approach to discover the natural acoustic structure of EDM independent of commercial labels. Our method combines novel tempogram-based features captu…

My wife and son started talking about what they think are differences of something being a game, a sport or a competition. They just said their opinions and started doing something else.
I, on the other hand, immediately started to build up a taxonomy and rules for their classification and they left me hanging with an uncomplete system! HOW CAN THEY STOP WITH SO MANY OPEN ENDS???!!

Automatic Generation of a Cryptography Misuse Taxonomy Using Large Language Models
Yang Zhang, Wenyi Ouyang, Yi Zhang, Liang Cheng, Chen Wu, Wenxin Hu
https://arxiv.org/abs/2509.10814

Automatic Generation of a Cryptography Misuse Taxonomy Using Large Language Models
The prevalence of cryptographic API misuse (CAM) is compromising the effectiveness of cryptography and in turn the security of modern systems and applications. Despite extensive efforts to develop CAM detection tools, these tools typically rely on a limited set of predefined rules from human-curated knowledge. This rigid, rule-based approach hinders adaptation to evolving CAM patterns in real practices. We propose leveraging large language models (LLMs), trained on publicly available cryptogr…

Towards a Taxonomy of Sustainability Requirements for Software Design
Mandira Roy, Novarun Deb, Nabendu Chaki, Agostino Cortesi
https://arxiv.org/abs/2510.08990 https://

Towards a Taxonomy of Sustainability Requirements for Software Design
Software systems are a significant contributor to global sustainability concerns, demanding that environmental, social, technical, and economic factors be systematically addressed from the initial requirements engineering phase. Although existing research provides various sustainability requirements (SRs), these contributions are often fragmented, specific to certain dimensions, or limited to particular application domains, resulting in a critical lack of a unified, comprehensive taxonomy for t…

Analyzing Information-Seeking Behaviors in a Hakka AI Chatbot: A Cognitive-Pragmatic Study
Chu-Hsuan Lee, Chen-Chi Chang, Hung-Shin Lee, Yun-Hsiang Hsu, Ching-Yuan Chen
https://arxiv.org/abs/2509.11591

Analyzing Information-Seeking Behaviors in a Hakka AI Chatbot: A Cognitive-Pragmatic Study
With many endangered languages at risk of disappearing, efforts to preserve them now rely more than ever on using technology alongside culturally informed teaching strategies. This study examines user behaviors in TALKA, a generative AI-powered chatbot designed for Hakka language engagement, by employing a dual-layered analytical framework grounded in Bloom's Taxonomy of cognitive processes and dialogue act categorization. We analyzed 7,077 user utterances, each carefully annotated according to…

Integral Signatures of Activation Functions: A 9-Dimensional Taxonomy and Stability Theory for Deep Learning
Ankur Mali, Lawrence Hall, Jake Williams, Gordon Richards
https://arxiv.org/abs/2510.08456

Integral Signatures of Activation Functions: A 9-Dimensional Taxonomy and Stability Theory for Deep Learning
Activation functions govern the expressivity and stability of neural networks, yet existing comparisons remain largely heuristic. We propose a rigorous framework for their classification via a nine-dimensional integral signature S_sigma(phi), combining Gaussian propagation statistics (m1, g1, g2, m2, eta), asymptotic slopes (alpha_plus, alpha_minus), and regularity measures (TV(phi'), C(phi)). This taxonomy establishes well-posedness, affine reparameterization laws with bias, and closure under …

MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science
Junkai Zhang, Jingru Gan, Xiaoxuan Wang, Zian Jia, Changquan Gu, Jianpeng Chen, Yanqiao Zhu, Mingyu Derek Ma, Dawei Zhou, Ling Li, Wei Wang
https://arxiv.org/abs/2510.12171

MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science
Large Language Models (LLMs) have demonstrated remarkable abilities in scientific reasoning, yet their reasoning capabilities in materials science remain underexplored. To fill this gap, we introduce MatSciBench, a comprehensive college-level benchmark comprising 1,340 problems that span the essential subdisciplines of materials science. MatSciBench features a structured and fine-grained taxonomy that categorizes materials science questions into 6 primary fields and 31 sub-fields, and includes …

Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
Anastasija Nikiforova, Martin Lnenicka, Ulf Melin, David Valle-Cruz, Asif Gill, Cesar Casiano Flores, Emyana Sirait, Mariusz Luterek, Richard Michael Dreyling, Barbora Tesarova
https://arxiv.org/abs/2510.09634

Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
Despite Artificial Intelligence (AI) transformative potential for public sector services, decision-making, and administrative efficiency, adoption remains uneven due to complex technical, organizational, and institutional challenges. Responsible AI frameworks emphasize fairness, accountability, and transparency, aligning with principles of trustworthy AI and fair AI, yet remain largely aspirational, overlooking technical and institutional realities, especially foundational data and governance. …

An HPC Benchmark Survey and Taxonomy for Characterization
Andreas Herten, Olga Pearce, Filipe S. M. Guimar\~aes
https://arxiv.org/abs/2509.08347 https://ar…

An HPC Benchmark Survey and Taxonomy for Characterization
The field of High-Performance Computing (HPC) is defined by providing computing devices with highest performance for a variety of demanding scientific users. The tight co-design relationship between HPC providers and users propels the field forward, paired with technological improvements, achieving continuously higher performance and resource utilization. A key device for system architects, architecture researchers, and scientific users are benchmarks, allowing for well-defined assessment of ha…

Taxonomy of User Needs and Actions
Renee Shelby, Fernando Diaz, Vinodkumar Prabhakaran
https://arxiv.org/abs/2510.06124 https://arxiv.org/pdf/2510.06124

Taxonomy of User Needs and Actions
The growing ubiquity of conversational AI highlights the need for frameworks that capture not only users' instrumental goals but also the situated, adaptive, and social practices through which they achieve them. Existing taxonomies of conversational behavior either overgeneralize, remain domain-specific, or reduce interactions to narrow dialogue functions. To address this gap, we introduce the Taxonomy of User Needs and Actions (TUNA), an empirically grounded framework developed through iterati…

DriftBench: Defining and Generating Data and Query Workload Drift for Benchmarking
Guanli Liu, Renata Borovica-Gajic
https://arxiv.org/abs/2510.10858 https://

DriftBench: Defining and Generating Data and Query Workload Drift for Benchmarking
Data and workload drift are key to evaluating database components such as caching, cardinality estimation, indexing, and query optimization. Yet, existing benchmarks are static, offering little to no support for modeling drift. This limitation stems from the lack of clear definitions and tools for generating data and workload drift. Motivated by this gap, we propose a unified taxonomy for data and workload drift, grounded in observations from both academia and industry. Building on this foundat…

ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimization
Sunzhu Li, Zhiyu Lin, Shuling Yang, Jiale Zhao, Wei Chen
https://arxiv.org/abs/2510.12063 https:/…

ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimization
Large Reasoning Models (LRMs) are powerful, but they still suffer from inefficient and off-target reasoning. Currently, training-free methods are limited to either rigid heuristics or descriptive, non-actionable analyses. In this paper, we introduce ThinkPilot, a training-free framework that automatically optimizes LRMs reasoning. It uses an evolutionary process to generate think-prefixes, which are instructions that evolve driven by a taxonomy of reasoning behaviors to guide models toward supe…

METRICALARGS: A Taxonomy for Studying Metrical Poetry with LLMs
Chalamalasetti Kranti, Sowmya Vajjala
https://arxiv.org/abs/2510.08188 https://arxiv.org/pd…

METRICALARGS: A Taxonomy for Studying Metrical Poetry with LLMs
Prior NLP work studying poetry has focused primarily on automatic poem generation and summarization. Many languages have well-studied traditions of poetic meter which enforce constraints on a poem in terms of syllable and phoneme patterns. Such advanced literary forms offer opportunities for probing deeper reasoning and language understanding in Large Language Models (LLMs) and their ability to follow strict pre-requisites and rules. In this paper, we introduce MetricalARGS, the first taxonomy …

Crosslisted article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs
Wang, Wang, Yang, Patel, Zhao, Mo, Peng, Qian, Huang, Xiong, Liu, Nie

URLLC for 6G Enabled Industry 5.0: A Taxonomy of Architectures, Cross Layer Techniques, and Time Critical Applications
Abdikarim Mohamed Ibrahim, Rosdiadee Nordin, Yahya S. M. Khamayseh, Angela Amphawan, Muhammed Basheer Jasser
https://arxiv.org/abs/2510.08080

URLLC for 6G Enabled Industry 5.0: A Taxonomy of Architectures, Cross Layer Techniques, and Time Critical Applications
The evolution from Industry 4.0 to Industry 5.0 introduces stringent requirements for ultra reliable low latency communication (URLLC) to support human centric, intelligent, and resilient industrial systems. Sixth-generation (6G) wireless networks aim to meet these requirements through sub-millisecond end-to-end delays, microsecond level jitter, and near perfect reliability, enabled by advances such as terahertz (THz) communication, reconfigurable intelligent surfaces (RIS), multi-access edge c…

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities
Rajendramayavan Sathyam, Yueqi Li
https://arxiv.org/abs/2509.08302 https://

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities
Foundation models are revolutionizing autonomous driving perception, transitioning the field from narrow, task-specific deep learning models to versatile, general-purpose architectures trained on vast, diverse datasets. This survey examines how these models address critical challenges in autonomous perception, including limitations in generalization, scalability, and robustness to distributional shifts. The survey introduces a novel taxonomy structured around four essential capabilities for rob…

Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
Gaurab Chhetri, Shriyank Somvanshi, Pavan Hebli, Shamyo Brotee, Subasish Das
https://arxiv.org/abs/2510.10436

Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
Post-quantum cryptography (PQC) is moving from evaluation to deployment as NIST finalizes standards for ML-KEM, ML-DSA, and SLH-DSA. This survey maps the space from foundations to practice. We first develop a taxonomy across lattice-, code-, hash-, multivariate-, isogeny-, and MPC-in-the-Head families, summarizing security assumptions, cryptanalysis, and standardization status. We then compare performance and communication costs using representative, implementation-grounded measurements, and re…

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights
Cosmin-Andrei Hatfaludi, Alex Serban
https://arxiv.org/abs/2509.05142 https://

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights
Federated learning has the potential to unlock siloed data and distributed resources by enabling collaborative model training without sharing private data. As more complex foundational models gain widespread use, the need to expand training resources and integrate privately owned data grows as well. In this article, we explore the intersection of federated learning and foundational models, aiming to identify, categorize, and characterize technical methods that integrate the two paradigms. As a …

Specifying an Obligation Taxonomy in the Non-Markovian Situation Calculus
Kalonji Kalala, Iluju Kiringa, Tet Yeap
https://arxiv.org/abs/2509.22533 https://…

Specifying an Obligation Taxonomy in the Non-Markovian Situation Calculus
Over more than three decades, the Situation Calculus has established itself as an elegant, powerful, and concise formalism for specifying dynamical domains as well as for reasoning about the effects of actions of those domains both in the world and in the mental state of the modelled agents. Moreover, it has also been established that the preconditions of a given action and its effects may be determined entirely by the current situation alone, or they may be determined by past situations as wel…

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Yisong Zhang, Ran Cheng, Guoxing Yi, Kay Chen Tan
https://arxiv.org/abs/2509.08269

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Large Language Models (LLMs), with their strong understanding and reasoning capabilities, are increasingly being explored for tackling optimization problems, especially in synergy with evolutionary computation. Despite rapid progress, however, the field still lacks a unified synthesis and a systematic taxonomy. This survey addresses this gap by providing a comprehensive review of recent developments and organizing them within a structured framework. We classify existing research into two main s…

Doc2Query : Topic-Coverage based Document Expansion and its Application to Dense Retrieval via Dual-Index Fusion
Tzu-Lin Kuo, Wei-Ning Chiu, Wei-Yun Ma, Pu-Jen Cheng
https://arxiv.org/abs/2510.09557

Doc2Query++: Topic-Coverage based Document Expansion and its Application to Dense Retrieval via Dual-Index Fusion
Document expansion (DE) via query generation tackles vocabulary mismatch in sparse retrieval, yet faces limitations: uncontrolled generation producing hallucinated or redundant queries with low diversity; poor generalization from in-domain training (e.g., MS MARCO) to out-of-domain data like BEIR; and noise from concatenation harming dense retrieval. While Large Language Models (LLMs) enable cross-domain query generation, basic prompting lacks control, and taxonomy-based methods rely on domain-…

Saw an AI-enhanced news page that categorised the Embassy of China as "anti-establishment" and I think we maybe have taxonomy problem as much as an AI problem.

Data Quality Taxonomy for Data Monetization
Eduardo Vyhmeister, Bastien Pietropoli, Andrea Visentin
https://arxiv.org/abs/2510.00089 https://arxiv.org/pdf/…

Data Quality Taxonomy for Data Monetization
This chapter presents a comprehensive taxonomy for assessing data quality in the context of data monetisation, developed through a systematic literature review. Organising over one hundred metrics and Key Performance Indicators (KPIs) into four subclusters (Fundamental, Contextual, Resolution, and Specialised) within the Balanced Scorecard (BSC) framework, the taxonomy integrates both universal and domain-specific quality dimensions. By positioning data quality as a strategic connector across t…

Structuring Automotive Data for Systems Engineering: A Taxonomy-Based Approach
Carl Philipp Hohl, Philipp Reis, Tobias Sch\"urmann, Stefan Otten, Eric Sax
https://arxiv.org/abs/2510.00963

Structuring Automotive Data for Systems Engineering: A Taxonomy-Based Approach
Vehicle data is essential for advancing data-driven development throughout the automotive lifecycle, including requirements engineering, design, verification, and validation, and post-deployment optimization. Developers currently collect data in a decentralized and fragmented manner across simulations, test benches, and real-world driving, resulting in data silos, inconsistent formats, and limited interoperability. This leads to redundant efforts, inefficient integration, and suboptimal use of …

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
Siddharth Agarwal, Maria A. Rodriguez, Rajkumar Buyya
https://arxiv.org/abs/2510.02404

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
The serverless cloud computing model offers a framework where the service provider abstracts the underlying infrastructure management from developers. In this serverless model, FaaS provides an event-driven, function-oriented computing service characterised by fine-grained, usage-based pricing that eliminates cost for idle resources. Platforms like AWS Lambda, Azure Functions, and Cloud Run Functions require developers to configure their function(s) with minimum operational resources for its su…

3D and 4D World Modeling: A Survey
Lingdong Kong, Wesley Yang, Jianbiao Mei, Youquan Liu, Ao Liang, Dekai Zhu, Dongyue Lu, Wei Yin, Xiaotao Hu, Mingkai Jia, Junyuan Deng, Kaiwen Zhang, Yang Wu, Tianyi Yan, Shenyuan Gao, Song Wang, Linfeng Li, Liang Pan, Yong Liu, Jianke Zhu, Wei Tsang Ooi, Steven C. H. Hoi, Ziwei Liu
https://arxiv.org/abs/…

3D and 4D World Modeling: A Survey
World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphasizes generative methods for 2D image and video data, they overlook the rapidly growing body of work that leverages native 3D and 4D representations such as RGB-D imagery, occupancy grids, and LiDAR point clouds for large-scale scene modeling. At the same time, the absence of a standardized definition and taxonomy for …

Huckleberries (Vaccinium membranaceum) and wild blueberries (V. deliciosum) along the trail to Snow Lake last week.
https://flic.kr/p/2roeRnH
Taxonomy is tricky: Both of these species have "blueberry" and "huckleberry" among their recognized common names.

Stabilization and Regaining Periodicity in Modular Laplacian Dynamics
Ma{\l}gorzata Nowak-K\k{e}pczyk
https://arxiv.org/abs/2509.05815 https://arxiv.org/pd…

Stabilization and Regaining Periodicity in Modular Laplacian Dynamics
We study discrete Laplacians on two-dimensional lattices under modular iterations, focusing on the emergence of nontrivial large-scale patterns. While purely binary or constant modular sequences quickly collapse into strict periodicity, the insertion of a single non-binary step k yields qualitatively new behavior. Through extensive computer-assisted exploration we identify a taxonomy of long-lived figures - rugs, quasi-carpets, and carpets - whose occurrence depends systematically on seed symme…

Privacy in the Age of AI: A Taxonomy of Data Risks
Grace Billiris, Asif Gill, Madhushi Bandara
https://arxiv.org/abs/2510.02357 https://arxiv.org/pdf/2510.…

Privacy in the Age of AI: A Taxonomy of Data Risks
Artificial Intelligence (AI) systems introduce unprecedented privacy challenges as they process increasingly sensitive data. Traditional privacy frameworks prove inadequate for AI technologies due to unique characteristics such as autonomous learning and black-box decision-making. This paper presents a taxonomy classifying AI privacy risks, synthesised from 45 studies identified through systematic review. We identify 19 key risks grouped under four categories: Dataset-Level, Model-Level, Infras…

Blue Stragglers and Friends: Initial Evolutionary Pathways in Close Low-Mass Binaries: Literature Review
Robert D. Mathieu, Onno R. Pols
https://arxiv.org/abs/2509.20531 https:/…

Blue Stragglers and Friends: Initial Evolutionary Pathways in Close Low-Mass Binaries: Literature Review
The scope of this literature review is observations of the products of first-stage evolution for binaries having components with M < 2 $M_\odot$. A taxonomy for these products comprises dwarfs ("blue stragglers"), giants ("yellow stragglers"), subdwarf B stars, and giant-like stars ("sub-subgiants" and "red stragglers"). This literature review is organized according to this taxonomy within three distinct environments: open star clusters, globular star clusters, and the Galactic field. This li…

Aegis: Taxonomy and Optimizations for Overcoming Agent-Environment Failures in LLM Agents
Kevin Song, Anand Jayarajan, Yaoyao Ding, Qidong Su, Zhanda Zhu, Sihang Liu, Gennady Pekhimenko
https://arxiv.org/abs/2508.19504

Aegis: Taxonomy and Optimizations for Overcoming Agent-Environment Failures in LLM Agents
Large Language Models (LLMs) agents augmented with domain tools promise to autonomously execute complex tasks requiring human-level intelligence, such as customer service and digital assistance. However, their practical deployment is often limited by their low success rates under complex real-world environments. To tackle this, prior research has primarily focused on improving the agents themselves, such as developing strong agentic LLMs, while overlooking the role of the system environment in …

SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversations
Jina Suh, Lindy Le, Erfan Shayegani, Gonzalo Ramos, Judith Amores, Desmond C. Ong, Mary Czerwinski, Javier Hernandez
https://arxiv.org/abs/2509.16437

SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversations
Empathy is increasingly recognized as a key factor in human-AI communication, yet conventional approaches to "digital empathy" often focus on simulating internal, human-like emotional states while overlooking the inherently subjective, contextual, and relational facets of empathy as perceived by users. In this work, we propose a human-centered taxonomy that emphasizes observable empathic behaviors and introduce a new dataset, Sense-7, of real-world conversations between information workers and …

Taxonomy of defects in semi-dry transferred CVD graphene
N. Reckinger, B. Hackens
https://arxiv.org/abs/2508.15549 https://arxiv.org/pdf/2508.15549

Taxonomy of defects in semi-dry transferred CVD graphene
Post-transfer in-depth morphological characterization of graphene grown by chemical vapor deposition (CVD) is of great importance to evaluate the quality and to understand the origin of defects of the transferred sheets. Herein, a semi-dry transfer technique is used to peel off millimeter-sized CVD graphene flakes from polycrystalline copper foils and transfer them onto SiO2/Si substrates. We take advantage of the unique feature of this semi-dry process: it preserves the copper substrate, enabl…

AI Education in Higher Education: A Taxonomy for Curriculum Reform and the Mission of Knowledge
Tian Zheng
https://arxiv.org/abs/2509.23363 https://arxiv.o…

AI Education in Higher Education: A Taxonomy for Curriculum Reform and the Mission of Knowledge
Artificial intelligence (AI) is reshaping higher education, yet current debates often feel tangled, mixing concerns about pedagogy, operations, curriculum, and the future of work without a shared framework. This paper offers a first attempt at a taxonomy to organize the diverse narratives of AI education and to inform discipline-based curricular discussions. We place these narratives within the enduring responsibility of higher education: the mission of knowledge. This mission includes not only…

Taxonomy-aware Dynamic Motion Generation on Hyperbolic Manifolds
Luis Augenstein, No\'emie Jaquier, Tamim Asfour, Leonel Rozo
https://arxiv.org/abs/2509.21281 https://

Taxonomy-aware Dynamic Motion Generation on Hyperbolic Manifolds
Human-like motion generation for robots often draws inspiration from biomechanical studies, which often categorize complex human motions into hierarchical taxonomies. While these taxonomies provide rich structural information about how movements relate to one another, this information is frequently overlooked in motion generation models, leading to a disconnect between the generated motions and their underlying hierarchical structure. This paper introduces the \ac{gphdm}, a novel approach that …

Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation
Agneet Chatterjee, Rahim Entezari, Maksym Zhuravinskyi, Maksim Lapin, Reshinth Adithyan, Amit Raj, Chitta Baral, Yezhou Yang, Varun Jampani
https://arxiv.org/abs/2509.26555

Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation
Recent advances in video generation have enabled high-fidelity video synthesis from user provided prompts. However, existing models and benchmarks fail to capture the complexity and requirements of professional video generation. Towards that goal, we introduce Stable Cinemetrics, a structured evaluation framework that formalizes filmmaking controls into four disentangled, hierarchical taxonomies: Setup, Event, Lighting, and Camera. Together, these taxonomies define 76 fine-grained control nodes…

The New Quant: A Survey of Large Language Models in Financial Prediction and Trading
Weilong Fu
https://arxiv.org/abs/2510.05533 https://arxiv.org/pdf/2510…

The New Quant: A Survey of Large Language Models in Financial Prediction and Trading
Large language models are reshaping quantitative investing by turning unstructured financial information into evidence-grounded signals and executable decisions. This survey synthesizes research with a focus on equity return prediction and trading, consolidating insights from domain surveys and more than fifty primary studies. We propose a task-centered taxonomy that spans sentiment and event extraction, numerical and economic reasoning, multimodal understanding, retrieval-augmented generation,…

Government Office for Science (GO SCIENCE) has released a pdf chart of its Emerging Technology Taxonomy.
https://foresightprojects.blog.gov.uk/2025/09/04/helping-government-to-understand-the-technologies-of-the-future/

Helping government to understand the technologies of the future – Futures, Foresight and Horizon Scanning
Technology development has the potential to surprise even the most astute of experts. With emerging technologies evolving so rapidly, how do we make sure that everyone is on the same page when it comes to understanding them? A challenge that

Think this is the first time I've seen a "dangerous person" category on ABC Emergency. ( I'm not really a taxonomy nerd 🤓)
https://www.abc.net.au/emergency/warning/vic/AUREMER-10c2b0d87fedc5b1ea15a7eb922e46a0

BloomAPR: A Bloom's Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions
Yinghang Ma (Jack), Jiho Shin (Jack), Leuson Da Silva (Jack), Zhen Ming (Jack), Jiang, Song Wang, Foutse Khomh, Shin Hwei Tan
https://arxiv.org/abs/2509.25465

BloomAPR: A Bloom's Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions
Recent advances in large language models (LLMs) have accelerated the development of AI-driven automated program repair (APR) solutions. However, these solutions are typically evaluated using static benchmarks such as Defects4J and SWE-bench, which suffer from two key limitations: (1) the risk of data contamination, potentially inflating evaluation results due to overlap with LLM training data, and (2) limited ability to assess the APR capabilities in dynamic and diverse contexts. In this paper,…

KTBox: A Modular LaTeX Framework for Semantic Color, Structured Highlighting, and Scholarly Communication
Bhaskar Mangal, Ashutosh Bhatia, Yashvardhan Sharma, Kamlesh Tiwari, Rashmi Verma
https://arxiv.org/abs/2510.01961

KTBox: A Modular LaTeX Framework for Semantic Color, Structured Highlighting, and Scholarly Communication
The communication of technical insight in scientific manuscripts often relies on ad-hoc formatting choices, resulting in inconsistent visual emphasis and limited portability across document classes. This paper introduces ktbox, a modular LaTeX framework that unifies semantic color palettes, structured highlight boxes, taxonomy trees, and author metadata utilities into a coherent system for scholarly writing. The framework is distributed as a set of lightweight, namespaced components: ktcolor.st…

Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation
Wenyuan Chen, Fateme Nateghi Haredasht, Kameron C. Black, Francois Grolleau, Emily Alsentzer, Jonathan H. Chen, Stephen P. Ma
https://arxiv.org/abs/2509.22565

Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation
Asynchronous patient-clinician messaging via EHR portals is a growing source of clinician workload, prompting interest in large language models (LLMs) to assist with draft responses. However, LLM outputs may contain clinical inaccuracies, omissions, or tone mismatches, making robust evaluation essential. Our contributions are threefold: (1) we introduce a clinically grounded error ontology comprising 5 domains and 59 granular error codes, developed through inductive coding and expert adjudicati…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Diffusion Models for Low-Light Image Enhancement: A Multi-Perspective Taxonomy and Performance An...
Eashan Adhikarla, Yixin Liu, Brian D. Davison

Bias Mitigation for AI-Feedback Loops in Recommender Systems: A Systematic Literature Review and Taxonomy
Theodor Stoecker, Samed Bayer, Ingo Weber
https://arxiv.org/abs/2509.00109

Bias Mitigation for AI-Feedback Loops in Recommender Systems: A Systematic Literature Review and Taxonomy
Recommender systems continually retrain on user reactions to their own predictions, creating AI feedback loops that amplify biases and diminish fairness over time. Despite this well-known risk, most bias mitigation techniques are tested only on static splits, so their long-term fairness across multiple retraining rounds remains unclear. We therefore present a systematic literature review of bias mitigation methods that explicitly consider AI feedback loops and are validated in multi-round simul…

Rethinking Denial-of-Service: A Conditional Taxonomy Unifying Availability and Sustainability Threats
Mark Dorsett, Scott Man, Tim Koussas
https://arxiv.org/abs/2508.19283 https…

Rethinking Denial-of-Service: A Conditional Taxonomy Unifying Availability and Sustainability Threats
This paper proposes a unified, condition-based framework for classifying both legacy and cloud-era denial-of-service (DoS) attacks. The framework comprises three interrelated models: a formal conditional tree taxonomy, a hierarchical lattice structure based on order theory, and a conceptual Venn diagram. At its core, the taxonomy introduces six observable conditions (C0-C5) grounded in real-world attack behaviours, including source distribution, traffic volume, infrastructure targeting, and fin…

Sci2Pol: Evaluating and Fine-tuning LLMs on Scientific-to-Policy Brief Generation
Weimin Wu, Alexander C. Furnas, Eddie Yang, Gefei Liu, Akhil Pandey Akella, Xuefeng Song, Dashun Wang, Han Liu
https://arxiv.org/abs/2509.21493

Sci2Pol: Evaluating and Fine-tuning LLMs on Scientific-to-Policy Brief Generation
We propose Sci2Pol-Bench and Sci2Pol-Corpus, the first benchmark and training dataset for evaluating and fine-tuning large language models (LLMs) on policy brief generation from a scientific paper. We build Sci2Pol-Bench on a five-stage taxonomy to mirror the human writing process: (i) Autocompletion, (ii) Understanding, (iii) Summarization, (iv) Generation, and (v) Verification. It features 18 tasks in multiple-choice and open-ended formats. Specifically, for the Generation stage, we show that…

Developing signal processing skills: A proposal for the teaching of filters
Fernanda Zapata Bascu\~n\'an, Daniel Col\'on, Marcelo Ar\'aoz
https://arxiv.org/abs/2509.25355

Developing signal processing skills: A proposal for the teaching of filters
In accordance with Bloom's taxonomy, a four-level evaluation abstraction was generated with the objective of structuring and hierarchizing curricula knowledge, allowing students to dominate a subject and progressively reach the top of Bloom's pyramid. The evaluation was conducted with 10 university students who analyzed and optimized a filtering process using MATLAB simulation while constrained by time. The results demonstrate that this novel teaching method facilitates a more profound comprehe…

Crosslisted article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- A Taxonomy of Prompt Defects in LLM Systems
Haoye Tian, Chong Wang, BoYang Yang, Lyuye Zhang, Yang Liu

A Taxonomy of Hierarchical Multi-Agent Systems: Design Patterns, Coordination Mechanisms, and Industrial Applications
David J. Moore
https://arxiv.org/abs/2508.12683 https://

A Taxonomy of Hierarchical Multi-Agent Systems: Design Patterns, Coordination Mechanisms, and Industrial Applications
Hierarchical multi-agent systems (HMAS) organize collections of agents into layered structures that help manage complexity and scale. These hierarchies can simplify coordination, but they also can introduce trade-offs that are not always obvious. This paper proposes a multi-dimensional taxonomy for HMAS along five axes: control hierarchy, information flow, role and task delegation, temporal layering, and communication structure. The intent is not to prescribe a single "best" design but to provi…

HOGraspFlow: Exploring Vision-based Generative Grasp Synthesis with Hand-Object Priors and Taxonomy Awareness
Yitian Shi, Zicheng Guo, Rosa Wolf, Edgar Welte, Rania Rayyes
https://arxiv.org/abs/2509.16871

HOGraspFlow: Exploring Vision-based Generative Grasp Synthesis with Hand-Object Priors and Taxonomy Awareness
We propose Hand-Object\emph{(HO)GraspFlow}, an affordance-centric approach that retargets a single RGB with hand-object interaction (HOI) into multi-modal executable parallel jaw grasps without explicit geometric priors on target objects. Building on foundation models for hand reconstruction and vision, we synthesize $SE(3)$ grasp poses with denoising flow matching (FM), conditioned on the following three complementary cues: RGB foundation features as visual semantics, HOI contact reconstructio…

Surgeons Are Indian Males and Speech Therapists Are White Females: Auditing Biases in Vision-Language Models for Healthcare Professionals
Zohaib Hasan Siddiqui, Dayam Nadeem, Mohammad Masudur Rahman, Mohammad Nadeem, Shahab Saquib Sohail, Beenish Moalla Chaudhry
https://arxiv.org/abs/2510.06280

Surgeons Are Indian Males and Speech Therapists Are White Females: Auditing Biases in Vision-Language Models for Healthcare Professionals
Vision language models (VLMs), such as CLIP and OpenCLIP, can encode and reflect stereotypical associations between medical professions and demographic attributes learned from web-scale data. We present an evaluation protocol for healthcare settings that quantifies associated biases and assesses their operational risk. Our methodology (i) defines a taxonomy spanning clinicians and allied healthcare roles (e.g., surgeon, cardiologist, dentist, nurse, pharmacist, technician), (ii) curates a profe…

A Taxonomy of Prompt Defects in LLM Systems
Haoye Tian, Chong Wang, BoYang Yang, Lyuye Zhang, Yang Liu
https://arxiv.org/abs/2509.14404 https://arxiv.org/p…

A Taxonomy of Prompt Defects in LLM Systems
Large Language Models (LLMs) have become key components of modern software, with prompts acting as their de-facto programming interface. However, prompt design remains largely empirical and small mistakes can cascade into unreliable, insecure, or inefficient behavior. This paper presents the first systematic survey and taxonomy of prompt defects, recurring ways that prompts fail to elicit their intended behavior from LLMs. We organize defects along six dimensions: (1) Specification and Intent, …

Metrics, KPIs, and Taxonomy for Data Valuation and Monetisation - A Systematic Literature Review
Eduardo Vyhmeister, Bastien Pietropaoli, Alejando Martinez Molina, Montserrat Gonzalez-Ferreiro, Gabriel Gonzalez-Castane, Jordi Arjona Aroca, Andrea Visentin
https://arxiv.org/abs/2508.18331

Metrics, KPIs, and Taxonomy for Data Valuation and Monetisation - A Systematic Literature Review
Data valuation and data monetisation are complex subjects but essential to most organisations today. Unfortunately, they still lack standard procedures and frameworks for organisations to follow. In this survey, we introduce the reader to the concepts by providing the definitions and the background required to better understand data, monetisation strategies, and finally metrics and KPIs used in these strategies. We have conducted a systematic literature review on metrics and KPIs used in data v…

LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
Xixun Lin, Yucheng Ning, Jingwen Zhang, Yan Dong, Yilong Liu, Yongxuan Wu, Xiaohua Qi, Nan Sun, Yanmin Shang, Pengfei Cao, Lixin Zou, Xu Chen, Chuan Zhou, Jia Wu, Shirui Pan, Bin Wang, Yanan Cao, Kai Chen, Songlin Hu, Li Guo
https://arxiv.org/abs/250…

LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
Driven by the rapid advancements of Large Language Models (LLMs), LLM-based agents have emerged as powerful intelligent systems capable of human-like cognition, reasoning, and interaction. These agents are increasingly being deployed across diverse real-world applications, including student education, scientific research, and financial analysis. However, despite their remarkable potential, LLM-based agents remain vulnerable to hallucination issues, which can result in erroneous task execution a…

A Survey on Evaluation Metrics for Music Generation
Faria Binte Kader, Santu Karmaker
https://arxiv.org/abs/2509.00051 https://arxiv.org/pdf/2509.00051

A Survey on Evaluation Metrics for Music Generation
Despite significant advancements in music generation systems, the methodologies for evaluating generated music have not progressed as expected due to the complex nature of music, with aspects such as structure, coherence, creativity, and emotional expressiveness. In this paper, we shed light on this research gap, introducing a detailed taxonomy for evaluation metrics for both audio and symbolic music representations. We include a critical review identifying major limitations in current evaluati…

Between Markov and restriction: Two more monads on categories for relations
Cipriano Junior Cioffo, Fabio Gadducci, Davide Trotta
https://arxiv.org/abs/2508.20054 https://

Between Markov and restriction: Two more monads on categories for relations
The study of categories abstracting the structural properties of relations has been extensively developed over the years, resulting in a rich and diverse body of work. A previous paper offered a survey providing a modern and comprehensive presentation of these ``categories for relations'' as instances of gs-monoidal categories, showing how they arise as Kleisli categories of suitable symmetric monoidal monads. The end result was a taxonomy that organised numerous related concepts in the literat…

Evaluating Large Language Models for IUCN Red List Species Information
Shinya Uryu
https://arxiv.org/abs/2510.02830 https://arxiv.org/pdf/2510.02830…

Evaluating Large Language Models for IUCN Red List Species Information
Large Language Models (LLMs) are rapidly being adopted in conservation to address the biodiversity crisis, yet their reliability for species evaluation is uncertain. This study systematically validates five leading models on 21,955 species across four core IUCN Red List assessment components: taxonomy, conservation status, distribution, and threats. A critical paradox was revealed: models excelled at taxonomic classification (94.9%) but consistently failed at conservation reasoning (27.2% for s…

Security smells in infrastructure as code: a taxonomy update beyond the seven sins
Aicha War, Serge L. B. Nikiema, Jordan Samhi, Jacques Klein, Tegawende F. Bissyande
https://arxiv.org/abs/2509.18761

Generative AI for Crystal Structures: A Review
Pierre-Paul De Breuck, Hai-Chen Wang, Gian-Marco Rignanese, Silvana Botti, Miguel A. L. Marques
https://arxiv.org/abs/2509.02723 h…

Generative AI for Crystal Structures: A Review
As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases, even predict desired properties. This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials. We begin by introducing the fundamentals of generative modeling and invertible material descriptors. We then propose a taxonomy based on architect…

The State of the Art in Visualization Literacy
Matthew Varona, Karen Bonilla, Maryam Hedayati, Alark Joshi, Lane Harrison, Matthew Kay, Carolina Nobre
https://arxiv.org/abs/2509.01018

The State of the Art in Visualization Literacy
Research in visualization literacy explores the skills required to engage with visualizations. This state-of-the-art report surveys the current literature in visualization literacy to provide a comprehensive overview of the field. We propose a taxonomy of visualization literacy that organizes the field into competency themes and research categories. To address ambiguity surrounding the term ``visualization literacy'', we provide a framework for operationalizing visualization literacy based on a…

Test Case Generation from Bug Reports via Large Language Models: A Cognitive Layered Evaluation Framework
Irtaza Sajid Qureshi (Jack), Zhen Ming (Jack), Jiang
https://arxiv.org/abs/2510.05365

Test Case Generation from Bug Reports via Large Language Models: A Cognitive Layered Evaluation Framework
Large Language Models (LLMs) are increasingly applied to automated software testing, yet their ability to generalize beyond memorized patterns and reason about natural language bug reports remains unclear. We present a systematic evaluation of LLM reasoning in test case generation, structured around the cognitive layers of Bloom's taxonomy: \textit{Remember}, \textit{Understand}, \textit{Apply}, \textit{Analyze}, \textit{Evaluate}, and \textit{Create}, which progressively assess higher levels o…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Aegis: Taxonomy and Optimizations for Overcoming Agent-Environment Failures in LLM Agents
Kevin Song, Anand Jayarajan, Yaoyao Ding, Qidong Su, Zhanda Zhu, Sihang Liu, Gennady Pekhimenko

A Nascent Taxonomy of Machine Learning in Intelligent Robotic Process Automation
Lukas Laakmann, Seyyid A. Ciftci, Christian Janiesch
https://arxiv.org/abs/2509.15730 https://…

A Nascent Taxonomy of Machine Learning in Intelligent Robotic Process Automation
Robotic process automation (RPA) is a lightweight approach to automating business processes using software robots that emulate user actions at the graphical user interface level. While RPA has gained popularity for its cost-effective and timely automation of rule-based, well-structured tasks, its symbolic nature has inherent limitations when approaching more complex tasks currently performed by human agents. Machine learning concepts enabling intelligent RPA provide an opportunity to broaden th…

When MCP Servers Attack: Taxonomy, Feasibility, and Mitigation
Weibo Zhao, Jiahao Liu, Bonan Ruan, Shaofei Li, Zhenkai Liang
https://arxiv.org/abs/2509.24272 https://

When MCP Servers Attack: Taxonomy, Feasibility, and Mitigation
Model Context Protocol (MCP) servers enable AI applications to connect to external systems in a plug-and-play manner, but their rapid proliferation also introduces severe security risks. Unlike mature software ecosystems with rigorous vetting, MCP servers still lack standardized review mechanisms, giving adversaries opportunities to distribute malicious implementations. Despite this pressing risk, the security implications of MCP servers remain underexplored. To address this gap, we present the…

Multi-Robot Navigation in Social Mini-Games: Definitions, Taxonomy, and Algorithms
Rohan Chandra, Shubham Singh, Abhishek Jha, Dannon Andrade, Hriday Sainathuni, Katia Sycara
https://arxiv.org/abs/2508.13459

Multi-Robot Navigation in Social Mini-Games: Definitions, Taxonomy, and Algorithms
The ``Last Mile Challenge'' has long been considered an important, yet unsolved, challenge for autonomous vehicles, public service robots, and delivery robots. A central issue in this challenge is the ability of robots to navigate constrained and cluttered environments (e.g., doorways, hallways, corridor intersections), often while competing for space with other robots and humans. We refer to these environments as ``Social Mini-Games'' (SMGs). SMGs are tightly coupled, high-agency interactions …

A Practical Guideline and Taxonomy to LLVM's Control Flow Integrity
Sabine Houy, Bruno Kreyssig, Timothee Riom, Alexandre Bartel, Patrick McDaniel
https://arxiv.org/abs/2508.15386

A Practical Guideline and Taxonomy to LLVM's Control Flow Integrity
Memory corruption vulnerabilities remain one of the most severe threats to software security. They often allow attackers to achieve arbitrary code execution by redirecting a vulnerable program's control flow. While Control Flow Integrity (CFI) has gained traction to mitigate this exploitation path, developers are not provided with any direction on how to apply CFI to real-world software. In this work, we establish a taxonomy mapping LLVM's forward-edge CFI variants to memory corruption vulnerab…

Detecting Struggling Student Programmers using Proficiency Taxonomies
Noga Schwartz, Roy Fairstein, Avi Segal, Kobi Gal
https://arxiv.org/abs/2508.17353 https://

Detecting Struggling Student Programmers using Proficiency Taxonomies
Early detection of struggling student programmers is crucial for providing them with personalized support. While multiple AI-based approaches have been proposed for this problem, they do not explicitly reason about students' programming skills in the model. This study addresses this gap by developing in collaboration with educators a taxonomy of proficiencies that categorizes how students solve coding tasks and is embedded in the detection model. Our model, termed the Proficiency Taxonomy Model…

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Ming Hu, Chenglong Ma, Wei Li, Wanghan Xu, Jiamin Wu, Jucheng Hu, Tianbin Li, Guohang Zhuang, Jiaqi Liu, Yingzhou Lu, Ying Chen, Chaoyang Zhang, Cheng Tan, Jie Ying, Guocheng Wu, Shujian Gao, Pengcheng Chen, Jiashi Lin, Haitao Wu, Lulu Chen, Fengxiang Wang, Yuanyuan Zhang, Xiangyu Zhao, Feilong Tang, Encheng Su, Junzhi Ning, Xinyao Liu, Ye Du, Changkai Ji, Cheng Tang, Huihui Xu, Ziyang Chen, Ziyan Hu…

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the complex nature of scientific data. This survey presents a comprehensive, data-centric synthesis that reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate. We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge, emphasizing the…

A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Review
Grace Billiris, Asif Gill, Madhushi Bandara
https://arxiv.org/abs/2509.20418 https://

A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Review
Quantum Artificial Intelligence (QAI), the integration of Artificial Intelligence (AI) and Quantum Computing (QC), promises transformative advances, including AI-enabled quantum cryptography and quantum-resistant encryption protocols. However, QAI inherits data risks from both AI and QC, creating complex privacy and security vulnerabilities that are not systematically studied. These risks affect the trustworthiness and reliability of AI and QAI systems, making their understanding critical. This…

Benchmarking the Robustness of Agentic Systems to Adversarially-Induced Harms
Jonathan N\"other, Adish Singla, Goran Radanovic
https://arxiv.org/abs/2508.16481 https://

Benchmarking the Robustness of Agentic Systems to Adversarially-Induced Harms
Ensuring the safe use of agentic systems requires a thorough understanding of the range of malicious behaviors these systems may exhibit when under attack. In this paper, we evaluate the robustness of LLM-based agentic systems against attacks that aim to elicit harmful actions from agents. To this end, we propose a novel taxonomy of harms for agentic systems and a novel benchmark, BAD-ACTS, for studying the security of agentic systems with respect to a wide range of harmful actions. BAD-ACTS co…

Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data
Stephen Meisenbacher, Svetlozar Nestorov, Peter Norlander
https://arxiv.org/abs/2510.01470

Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data
Data from online job postings are difficult to access and are not built in a standard or transparent manner. Data included in the standard taxonomy and occupational information database (O*NET) are updated infrequently and based on small survey samples. We adopt O*NET as a framework for building natural language processing tools that extract structured information from job postings. We publish the Job Ad Analysis Toolkit (JAAT), a collection of open-source tools built for this purpose, and demo…

Axiomatic Choice and the Decision-Evaluation Paradox
Ben Abramowitz, Nicholas Mattei
https://arxiv.org/abs/2509.21836 https://arxiv.org/pdf/2509.21836

Axiomatic Choice and the Decision-Evaluation Paradox
We introduce a framework for modeling decisions with axioms that are statements about decisions, e.g., ethical constraints. Using our framework we define a taxonomy of decision axioms based on their structural properties and demonstrate a tension between the use of axioms to make decisions and the use of axioms to evaluate decisions which we call the Decision-Evaluation Paradox. We argue that the Decision-Evaluation Paradox arises with realistic axiom structures, and the paradox illuminates why…

A Survey of Affective Recommender Systems: Modeling Attitudes, Emotions, and Moods for Personalization
Tonmoy Hasan, Razvan Bunescu
https://arxiv.org/abs/2508.20289 https://

A Survey of Affective Recommender Systems: Modeling Attitudes, Emotions, and Moods for Personalization
Affective Recommender Systems are an emerging class of intelligent systems that aim to enhance personalization by aligning recommendations with users' affective states. Reflecting a growing interest, a number of surveys have been published in this area, however they lack an organizing taxonomy grounded in psychology and they often study only specific types of affective states or application domains. This survey addresses these limitations by providing a comprehensive, systematic review of affec…

Who Gets Left Behind? Auditing Disability Inclusivity in Large Language Models
Deepika Dash, Yeshil Bangera, Mithil Bangera, Gouthami Vadithya, Srikant Panda
https://arxiv.org/abs/2509.00963

Who Gets Left Behind? Auditing Disability Inclusivity in Large Language Models
Large Language Models (LLMs) are increasingly used for accessibility guidance, yet many disability groups remain underserved by their advice. To address this gap, we present taxonomy aligned benchmark1 of human validated, general purpose accessibility questions, designed to systematically audit inclusivity across disabilities. Our benchmark evaluates models along three dimensions: Question-Level Coverage (breadth within answers), Disability-Level Coverage (balance across nine disability categor…

Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering
Kun Zhu, Lizi Liao, Yuxuan Gu, Lei Huang, Xiaocheng Feng, Bing Qin
https://arxiv.org/abs/2509.19125

Has GPT-5 Achieved Spatial Intelligence? An Empirical Study
Zhongang Cai, Yubo Wang, Qingping Sun, Ruisi Wang, Chenyang Gu, Wanqi Yin, Zhiqian Lin, Zhitao Yang, Chen Wei, Xuanke Shi, Kewang Deng, Xiaoyang Han, Zukai Chen, Jiaqi Li, Xiangyu Fan, Hanming Deng, Lewei Lu, Bo Li, Ziwei Liu, Quan Wang, Dahua Lin, Lei Yang
https://arxiv.org/abs/2…

Has GPT-5 Achieved Spatial Intelligence? An Empirical Study
Multi-modal models have achieved remarkable progress in recent years. Nevertheless, they continue to exhibit notable limitations in spatial understanding and reasoning, which are fundamental capabilities to achieving artificial general intelligence. With the recent release of GPT-5, allegedly the most powerful AI model to date, it is timely to examine where the leading models stand on the path toward spatial intelligence. First, we propose a comprehensive taxonomy of spatial tasks that unifies …

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[11/12]:
- Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Constructio...
Chen, Haredasht, Black, Grolleau, Alsentzer, Chen, P.

A Taxonomy and Methodology for Proof-of-Location Systems
Eduardo Brito, Fernando Castillo, Liina Kamm, Amnir Hadachi, Ulrich Norbisrath
https://arxiv.org/abs/2508.14230 https://…

A Taxonomy and Methodology for Proof-of-Location Systems
Digital societies increasingly rely on trustworthy proofs of physical presence for services such as supply-chain tracking, e-voting, ride-sharing, and location-based rewards. Yet, traditional localization methods often lack cryptographic guarantees of where and when an entity was present, leaving them vulnerable to spoofing, replay, or collusion attacks. In response, research on Proof-of-Location (PoL) has emerged, with recent approaches combining distance bounding, distributed consensus, and p…

Pure Vision Language Action (VLA) Models: A Comprehensive Survey
Dapeng Zhang, Jin Sun, Chenghui Hu, Xiaoyan Wu, Zhenlong Yuan, Rui Zhou, Fei Shen, Qingguo Zhou
https://arxiv.org/abs/2509.19012

Pure Vision Language Action (VLA) Models: A Comprehensive Survey
The emergence of Vision Language Action (VLA) models marks a paradigm shift from traditional policy-based control to generalized robotics, reframing Vision Language Models (VLMs) from passive sequence generators into active agents for manipulation and decision-making in complex, dynamic environments. This survey delves into advanced VLA methods, aiming to provide a clear taxonomy and a systematic, comprehensive review of existing research. It presents a comprehensive analysis of VLA application…

Do Students Rely on AI? Analysis of Student-ChatGPT Conversations from a Field Study
Jiayu Zheng, Lingxin Hao, Kelun Lu, Ashi Garg, Mike Reese, Melo-Jean Yap, I-Jeng Wang, Xingyun Wu, Wenrui Huang, Jenna Hoffman, Ariane Kelly, My Le, Ryan Zhang, Yanyu Lin, Muhammad Faayez, Anqi Liu
https://arxiv.org/abs/2508.20244

Do Students Rely on AI? Analysis of Student-ChatGPT Conversations from a Field Study
This study explores how college students interact with generative AI (ChatGPT-4) during educational quizzes, focusing on reliance and predictors of AI adoption. Conducted at the early stages of ChatGPT implementation, when students had limited familiarity with the tool, this field study analyzed 315 student-AI conversations during a brief, quiz-based scenario across various STEM courses. A novel four-stage reliance taxonomy was introduced to capture students' reliance patterns, distinguishing A…

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Security smells in infrastructure as code: a taxonomy update beyond the seven sins
Aicha War, Serge L. B. Nikiema, Jordan Samhi, Jacques Klein, Tegawende F. Bissyande

The three main doctrines on the future of AI
Alex Amadori, Eva Behrens, Gabriel Alfour, Andrea Miotti
https://arxiv.org/abs/2509.20050 https://arxiv.org/pd…

The three main doctrines on the future of AI
This paper develops a taxonomy of expert perspectives on the risks and likely consequences of artificial intelligence, with particular focus on Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI). Drawing from primary sources, we identify three predominant doctrines: (1) The dominance doctrine, which predicts that the first actor to create sufficiently advanced AI will attain overwhelming strategic superiority sufficient to cheaply neutralize its opponents' defenses; (2…

Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering
Ziyou Li, Agnia Sergeyuk, Maliheh Izadi
https://arxiv.org/abs/2509.17096 https://

Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering
Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt…

Memory in Large Language Models: Mechanisms, Evaluation and Evolution
Dianxing Zhang, Wendong Li, Kani Song, Jiaye Lu, Gang Li, Liuchun Yang, Sheng Li
https://arxiv.org/abs/2509.18868

Memory in Large Language Models: Mechanisms, Evaluation and Evolution
Under a unified operational definition, we define LLM memory as a persistent state written during pretraining, finetuning, or inference that can later be addressed and that stably influences outputs. We propose a four-part taxonomy (parametric, contextual, external, procedural/episodic) and a memory quadruple (location, persistence, write/access path, controllability). We link mechanism, evaluation, and governance via the chain write -> read -> inhibit/update. To avoid distorted comparisons acr…

Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts
Dong Hyun Roh, Rajesh Kumar
https://arxiv.org/abs/2509.24807 https://

Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts
Keystroke dynamics is a promising modality for active user authentication, but its effectiveness under varying LLM-assisted typing and cognitive conditions remains understudied. Using data from 50 users and cognitive labels from Bloom's Taxonomy, we evaluate keystroke-based authentication in Korean across three realistic typing scenarios: bona fide composition, LLM content paraphrasing, and transcription. Our pipeline incorporates continuity-aware segmentation, feature extraction, and classific…

Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
Rui Shao, Wei Li, Lingsen Zhang, Renshan Zhang, Zhiyang Liu, Ran Chen, Liqiang Nie
https://arxiv.org/abs/2508.13073

Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
Robotic manipulation, a key frontier in robotics and embodied AI, requires precise motor control and multimodal understanding, yet traditional rule-based methods fail to scale or generalize in unstructured, novel environments. In recent years, Vision-Language-Action (VLA) models, built upon Large Vision-Language Models (VLMs) pretrained on vast image-text datasets, have emerged as a transformative paradigm. This survey provides the first systematic, taxonomy-oriented review of large VLM-based V…

What Makes AI Applications Acceptable or Unacceptable? A Predictive Moral Framework
Kimmo Eriksson, Simon Karlsson, Irina Vartanova, Pontus Strimling
https://arxiv.org/abs/2508.19317

What Makes AI Applications Acceptable or Unacceptable? A Predictive Moral Framework
As artificial intelligence rapidly transforms society, developers and policymakers struggle to anticipate which applications will face public moral resistance. We propose that these judgments are not idiosyncratic but systematic and predictable. In a large, preregistered study (N = 587, U.S. representative sample), we used a comprehensive taxonomy of 100 AI applications spanning personal and organizational contexts-including both functional uses and the moral treatment of AI itself. In particip…

Conf-Profile: A Confidence-Driven Reasoning Paradigm for Label-Free User Profiling
Yingxin Li, Jianbo Zhao, Xueyu Ren, Jie Tang, Wangjie You, Xu Chen, Kan Zhou, Chao Feng, Jiao Ran, Yuan Meng, Zhi Wang
https://arxiv.org/abs/2509.18864

Evaluation Guidelines for Empirical Studies in Software Engineering involving LLMs
Sebastian Baltes, Florian Angermeir, Chetan Arora, Marvin Mu\~noz Bar\'on, Chunyang Chen, Lukas B\"ohme, Fabio Calefato, Neil Ernst, Davide Falessi, Brian Fitzgerald, Davide Fucci, Marcos Kalinowski, Stefano Lambiase, Daniel Russo, Mircea Lungu, Lutz Prechelt, Paul Ralph, Christoph Treude, Stefan Wagner

Evaluation Guidelines for Empirical Studies in Software Engineering involving LLMs
Large language models (LLMs) are increasingly being integrated into software engineering (SE) research and practice, yet their non-determinism, opaque training data, and evolving architectures complicate the reproduction and replication of empirical studies. We present a community effort to scope this space, introducing a taxonomy of LLM-based study types together with eight guidelines for designing and reporting empirical studies involving LLMs. The guidelines present essential (must) criteria…

Building Data-Driven Occupation Taxonomies: A Bottom-Up Multi-Stage Approach via Semantic Clustering and Multi-Agent Collaboration
Nan Li, Bo Kang, Tijl De Bie
https://arxiv.org/abs/2509.15786

Building Data-Driven Occupation Taxonomies: A Bottom-Up Multi-Stage Approach via Semantic Clustering and Multi-Agent Collaboration
Creating robust occupation taxonomies, vital for applications ranging from job recommendation to labor market intelligence, is challenging. Manual curation is slow, while existing automated methods are either not adaptive to dynamic regional markets (top-down) or struggle to build coherent hierarchies from noisy data (bottom-up). We introduce CLIMB (CLusterIng-based Multi-agent taxonomy Builder), a framework that fully automates the creation of high-quality, data-driven taxonomies from raw job …

Crosslisted article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- Benchmarking Sociolinguistic Diversity in Swahili NLP: A Taxonomy-Guided Approach
Kezia Oketch, John P. Lalor, Ahmed Abbasi

MCPSecBench: A Systematic Security Benchmark and Playground for Testing Model Context Protocols
Yixuan Yang, Daoyuan Wu, Yufan Chen
https://arxiv.org/abs/2508.13220 https://

MCPSecBench: A Systematic Security Benchmark and Playground for Testing Model Context Protocols
Large Language Models (LLMs) are increasingly integrated into real-world applications via the Model Context Protocol (MCP), a universal, open standard for connecting AI agents with data sources and external tools. While MCP enhances the capabilities of LLM-based agents, it also introduces new security risks and expands their attack surfaces. In this paper, we present the first systematic taxonomy of MCP security, identifying 17 attack types across 4 primary attack surfaces. We introduce MCPSecB…

Scaling Success: A Systematic Review of Peer Grading Strategies for Accuracy, Efficiency, and Learning in Contemporary Education
Uchswas Paul, Ananya Mantravadi, Jash Shah, Shail Shah, Sri Vaishnavi Mylavarapu, M Parvez Rashid, Edward Gehringer
https://arxiv.org/abs/2508.11677

Scaling Success: A Systematic Review of Peer Grading Strategies for Accuracy, Efficiency, and Learning in Contemporary Education
Peer grading has emerged as a scalable solution for assessment in large and online classrooms, offering both logistical efficiency and pedagogical value. However, designing effective peer-grading systems remains challenging due to persistent concerns around accuracy, fairness, reliability, and student engagement. This paper presents a systematic review of 122 peer-reviewed studies on peer grading spanning over four decades. Drawing from this literature, we propose a comprehensive taxonomy that …