Tootfinder

When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance
Peizhang Shao, Linrui Xu, Jinxi Wang, Wei Zhou, Xingyu Wu
https://arxiv.org/abs/2507.07748

When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance
This paper establishes the first comprehensive review of Large Language Models (LLMs) applied within the legal domain. It pioneers an innovative dual lens taxonomy that integrates legal reasoning frameworks and professional ontologies to systematically unify historical research and contemporary breakthroughs. Transformer-based LLMs, which exhibit emergent capabilities such as contextual reasoning and generative argumentation, surmount traditional limitations by dynamically capturing legal seman…

Taxonomy of migration scenarios for Qiskit refactoring using LLMs
Jos\'e Manuel Su\'arez, Lu\'is Mariano Bibb\'o, Joaqu\'in Bogado, Alejandro Fernandez
https://arxiv.org/abs/2506.07135

Taxonomy of migration scenarios for Qiskit refactoring using LLMs
As quantum computing advances, quantum programming libraries' heterogeneity and steady evolution create new challenges for software developers. Frequent updates in software libraries break working code that needs to be refactored, thus adding complexity to an already complex landscape. These refactoring challenges are, in many cases, fundamentally different from those known in classical software engineering due to the nature of quantum computing software. This study addresses these challenges b…

Remote Renewable Energy Hubs: a Taxonomy
Victor Dachet, Antoine Dubois, Bardhyl Miftari, Rapha\"el Fonteneau, Damien Ernst
https://arxiv.org/abs/2507.07659

Remote Renewable Energy Hubs: a Taxonomy
Serving the energy demand with renewable energy is hindered by its limited availability near load centres (i.e. places where the energy demand is high). To address this challenge, the concept of Remote Renewable Energy Hubs (RREH) emerges as a promising solution. RREHs are energy hubs located in areas with abundant renewable energy sources, such as sun in the Sahara Desert or wind in Greenland. In these hubs, renewable energy sources are used to synthetise energy molecules. To produce specific …

What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies
Yaoqi Huang, Julie Stephany Berrio, Mao Shan, Stewart Worrall
https://arxiv.org/abs/2507.06513

What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies
Advances in vision-based sensors and computer vision algorithms have significantly improved the analysis and understanding of traffic scenarios. To facilitate the use of these improvements for road safety, this survey systematically categorizes the critical elements that demand attention in traffic scenarios and comprehensively analyzes available vision-driven tasks and datasets. Compared to existing surveys that focus on isolated domains, our taxonomy categorizes attention-worthy traffic entit…

Blockchain-Enabled Federated Learning
Murtaza Rangwala, Venugopal K R, Rajkumar Buyya
https://arxiv.org/abs/2508.06406 https://arxiv.org/pdf/2508.06406

Blockchain-Enabled Federated Learning
Blockchain-enabled federated learning (BCFL) addresses fundamental challenges of trust, privacy, and coordination in collaborative AI systems. This chapter provides comprehensive architectural analysis of BCFL systems through a systematic four-dimensional taxonomy examining coordination structures, consensus mechanisms, storage architectures, and trust models. We analyze design patterns from blockchain-verified centralized coordination to fully decentralized peer-to-peer networks, evaluating tr…

Short-Term Gains, Long-Term Gaps: The Impact of GenAI and Search Technologies on Retention
Mahir Akgun, Sacip Toker
https://arxiv.org/abs/2507.07357 https:…

Short-Term Gains, Long-Term Gaps: The Impact of GenAI and Search Technologies on Retention
The rise of Generative AI (GenAI) tools, such as ChatGPT, has transformed how students access and engage with information, raising questions about their impact on learning outcomes and retention. This study investigates how GenAI (ChatGPT), search engines (Google), and e-textbooks influence student performance across tasks of varying cognitive complexity, based on Bloom's Taxonomy. Using a sample of 123 students, we examined performance in three tasks: [1] knowing and understanding, [2] applyin…

Economic Policy Taxonomy
Rem Sadykhov, Geoff Goodell, Philip Treleaven
https://arxiv.org/abs/2507.03233 https://arxiv.org/pdf/2507.03…

Economic Policy Taxonomy
This paper proposes a framework for categorizing economic policies in a form of a tree taxonomy. The purpose of this approach is to construct an exhaustive and standardized list of actions that a governing authority has access to and can change to control an economy. This is advantageous from two perspectives: by having an exhaustive list of tools, it becomes easier to construct "complete" models (i.e., models that take in all empirical data and aim to simulate economic dynamics) of an economy …

SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting
Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt
https://arxiv.org/abs/2508.03000

SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting
The growing demand for corporate sustainability transparency, particularly under new regulations like the EU Taxonomy, necessitates precise data extraction from large, unstructured corporate reports. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, requires high-quality, domain-specific question-answering (QA) datasets to excel at particular domains. To address this, we introduce SustainableQA, a novel dataset and a scalable pipeline for generating a comprehensive …

FRaN-X: FRaming and Narratives-eXplorer
Artur Muratov, Hana Fatima Shaikh, Vanshikaa Jani, Tarek Mahmoud, Zhuohan Xie, Daniil Orel, Aaryamonvikram Singh, Yuxia Wang, Aadi Joshi, Hasan Iqbal, Ming Shan Hee, Dhruv Sahnan, Nikolaos Nikolaidis, Purifica\c{c}\~ao Silvano, Dimitar Dimitrov, Roman Yangarber, Ricardo Campos, Al\'ipio Jorge, Nuno Guimar\~aes, Elisa Sartori, Nicolas Stefanovitch, Giovanni Da San Martino, Jakub Piskorski, Preslav Nakov

FRaN-X: FRaming and Narratives-eXplorer
We present FRaN-X, a Framing and Narratives Explorer that automatically detects entity mentions and classifies their narrative roles directly from raw text. FRaN-X comprises a two-stage system that combines sequence labeling with fine-grained role classification to reveal how entities are portrayed as protagonists, antagonists, or innocents, using a unique taxonomy of 22 fine-grained roles nested under these three main categories. The system supports five languages (Bulgarian, English, Hindi, R…

The Role of Humour in Software Engineering -- A Literature Review and Preliminary Taxonomy
Dulaji Hidellaarachchi, John Grundy, Rashina Hoda
https://arxiv.org/abs/2507.03527

The Role of Humour in Software Engineering -- A Literature Review and Preliminary Taxonomy
Humour has long been recognized as a key factor in enhancing creativity, group effectiveness, and employee well-being across various domains. However, its occurrence and impact within software engineering (SE) teams remains under-explored. This paper introduces a comprehensive, literature review-based taxonomy exploring the characterisation and use of humour in SE teams, with the goal of boosting productivity, improving communication, and fostering a positive work environment while emphasising …

What Comes After Harm? Mapping Reparative Actions in AI through Justice Frameworks
Sijia Xiao, Haodi Zou, Alice Qian Zhang, Deepak Kumar, Hong Shen, Jason Hong, Motahhare Eslami
https://arxiv.org/abs/2506.05687

What Comes After Harm? Mapping Reparative Actions in AI through Justice Frameworks
As Artificial Intelligence (AI) systems are integrated into more aspects of society, they offer new capabilities but also cause a range of harms that are drawing increasing scrutiny. A large body of work in the Responsible AI community has focused on identifying and auditing these harms. However, much less is understood about what happens after harm occurs: what constitutes reparation, who initiates it, and how effective these reparations are. In this paper, we develop a taxonomy of AI harm rep…

Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy
Muhammed Golec, Maha AlabdulJalil
https://arxiv.org/abs/2506.04290 https://

Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy
Large Language Models (LLM), which have developed in recent years, enable credit risk assessment through the analysis of financial texts such as analyst reports and corporate disclosures. This paper presents the first systematic review and taxonomy focusing on LLMbased approaches in credit risk estimation. We determined the basic model architectures by selecting 60 relevant papers published between 2020-2025 with the PRISMA research strategy. And we examined the data used for scenarios such as …

A Comprehensive Survey on Bio-Inspired Algorithms: Taxonomy, Applications, and Future Directions
Shriyank Somvanshi, Md Monzurul Islam, Syed Aaqib Javed, Gaurab Chhetri, Kazi Sifatul Islam, Tausif Islam Chowdhury, Sazzad Bin Bashar Polock, Anandi Dutta, Subasish Das
https://arxiv.org/abs/2506.04238…

A Comprehensive Survey on Bio-Inspired Algorithms: Taxonomy, Applications, and Future Directions
Bio-inspired algorithms (BIAs) utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems. This survey categorizes BIAs into eight groups: evolutionary, swarm intelligence, physics-inspired, ecosystem and plant-based, predator-prey, neural-inspired, human-inspired, and hybrid approaches, and reviews their core principles, strengths, and limitations. We illustrate the usage of these algorithms in mac…

Taxonomy of Faults in Attention-Based Neural Networks
Sigma Jahan, Saurabh Singh Rajput, Tushar Sharma, Mohammad Masudur Rahman
https://arxiv.org/abs/2508.04925 https://

Taxonomy of Faults in Attention-Based Neural Networks
Attention mechanisms are at the core of modern neural architectures, powering systems ranging from ChatGPT to autonomous vehicles and driving a major economic impact. However, high-profile failures, such as ChatGPT's nonsensical outputs or Google's suspension of Gemini's image generation due to attention weight errors, highlight a critical gap: existing deep learning fault taxonomies might not adequately capture the unique failures introduced by attention mechanisms. This gap leaves practitione…

This https://arxiv.org/abs/2501.09023 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Cyber-Physical Security Vulnerabilities Identification and Classification in Smart Manufacturing -- A Defense-in-Depth Driven Framework and Taxonomy
The increasing cybersecurity threats to critical manufacturing infrastructure necessitate proactive strategies for vulnerability identification, classification, and assessment. Traditional approaches, which define vulnerabilities as weaknesses in computational logic or information systems, often overlook the physical and cyber-physical dimensions critical to manufacturing systems, comprising intertwined cyber, physical, and human elements. As a result, existing solutions fall short in addressin…

Understanding Everything as Code: A Taxonomy and Conceptual Model
Haoran Wei, Nazim Madhavji, John Steinbacher
https://arxiv.org/abs/2507.05100 https://

Understanding Everything as Code: A Taxonomy and Conceptual Model
Background: Everything as Code (EaC) is an emerging paradigm aiming to codify all aspects of modern software systems. Despite its growing popularity, comprehensive industry standards and peer-reviewed research clarifying its scope and guiding its adoption remain scarce. Aims: This study systematically analyzes existing knowledge and perceptions of EaC, clarifies its scope and boundaries, and provides structured guidance for researchers and practitioners. Method: We conducted a large-scale multi…

Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act
Rui-Jie Yew, Bill Marino, Suresh Venkatasubramanian
https://arxiv.org/abs/2506.01931 https:…

Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act
The shape of AI regulation is beginning to emerge, most prominently through the EU AI Act (the "AIA"). By 2027, the AIA will be in full effect, and firms are starting to adjust their behavior in light of this new law. In this paper, we present a framework and taxonomy for reasoning about "avoision" -- conduct that walks the line between legal avoidance and evasion -- that firms might engage in so as to minimize the regulatory burden the AIA poses. We organize these avoision strategies around th…

TaxaPLN: a taxonomy-aware augmentation strategy for microbiome-trait classification including metadata
Alexandre Chaussard, Anna Bonnet, Sylvain Le Corff, Harry Sokol
https://arxiv.org/abs/2507.03588

TaxaPLN: a taxonomy-aware augmentation strategy for microbiome-trait classification including metadata
The gut microbiome plays a crucial role in human health, making it a corner stone of modern biomedical research. To study its structure and dynamics, machine learning models are increasingly used to identify key microbial patterns associated with disease and environmental factors. However, microbiome data present unique challenges due to their compositionality, high-dimensionality, sparsity, and high variability, which can obscure meaningful signals. Besides, the effectiveness of machine learni…

This https://arxiv.org/abs/2505.21907 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy
AI copilots, context-aware, AI-powered systems designed to assist users in tasks such as software development and content creation, are becoming integral to modern workflows. As these systems grow in capability and adoption, personalization has emerged as a cornerstone for ensuring usability, trust, and productivity. Central to this personalization is preference optimization: the ability of AI copilots to detect, interpret, and align with individual user preferences. While personalization techn…

Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
Ziheng Zhao, Lisong Dai, Ya Zhang, Yanfeng Wang, Weidi Xie
https://arxiv.org/abs/2506.03238

Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
Automated interpretation of CT images-particularly localizing and describing abnormal findings across multi-plane and whole-body scans-remains a significant challenge in clinical radiology. This work aims to address this challenge through four key contributions: (i) On taxonomy, we collaborate with senior radiologists to propose a comprehensive hierarchical classification system, with 404 representative abnormal findings across all body regions; (ii) On data, we contribute a dataset containing …

Finance Agent Benchmark: Benchmarking LLMs on Real-world Financial Research Tasks
Antoine Bigeard, Langston Nashold, Rayan Krishnan, Shirley Wu
https://arxiv.org/abs/2508.00828 …

Finance Agent Benchmark: Benchmarking LLMs on Real-world Financial Research Tasks
Artificial Intelligence (AI) technology has emerged as a transformative force in financial analysis and the finance industry, though significant questions remain about the full capabilities of Large Language Model (LLM) agents in this domain. We present the Finance Agent Benchmark, featuring challenging and diverse real-world finance research problems that require LLMs to perform complex analysis using recent SEC filings. We construct the benchmark using a taxonomy of nine financial task catego…

A visual analytics tool for taxonomy-based trajectory data exploration
Ivan A. Hanono Cozzetti, Ahmad Abdou
https://arxiv.org/abs/2507.22899 https://arxiv.…

A visual analytics tool for taxonomy-based trajectory data exploration
The analysis of spatio-temporal data presents significant challenges due to the complexity and heterogeneity of movement patterns. This project proposes a data analytics tool that combines data visualization and statistical computation to facilitate spatio-temporal data analysis through a multi-level approach. The tool categorizes moving objects into distinct taxonomies using Machine Learning models, adding meaningful structure to the analysis. Two case studies demonstrate the methodology's eff…

LLM-Assisted Cheating Detection in Korean Language via Keystrokes
Dong Hyun Roh, Rajesh Kumar, An Ngo
https://arxiv.org/abs/2507.22956 https://arxiv.org/pd…

LLM-Assisted Cheating Detection in Korean Language via Keystrokes
This paper presents a keystroke-based framework for detecting LLM-assisted cheating in Korean, addressing key gaps in prior research regarding language coverage, cognitive context, and the granularity of LLM involvement. Our proposed dataset includes 69 participants who completed writing tasks under three conditions: Bona fide writing, paraphrasing ChatGPT responses, and transcribing ChatGPT responses. Each task spans six cognitive processes defined in Bloom's Taxonomy (remember, understand, ap…

Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers
Zhijian Xu, Yilun Zhao, Manasi Patwardhan, Lovekesh Vig, Arman Cohan
https://arxiv.org/abs/2507.02694

Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers
Peer review is fundamental to scientific research, but the growing volume of publications has intensified the challenges of this expertise-intensive process. While LLMs show promise in various scientific tasks, their potential to assist with peer review, particularly in identifying paper limitations, remains understudied. We first present a comprehensive taxonomy of limitation types in scientific research, with a focus on AI. Guided by this taxonomy, for studying limitations, we present LimitGe…

This https://arxiv.org/abs/2505.18924 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point CLoud Active Learning
We present a novel active learning framework for 3D point cloud semantic segmentation that, for the first time, integrates large language models (LLMs) to construct hierarchical label structures and guide uncertainty-based sample selection. Unlike prior methods that treat labels as flat and independent, our approach leverages LLM prompting to automatically generate multi-level semantic taxonomies and introduces a recursive uncertainty projection mechanism that propagates uncertainty across hier…

On the Architectural Split and Radio Intelligence Controller Placement in Integrated O-RAN-enabled Non-Terrestrial Networks
Jorge Baranda, Marius Caus, Luis Blanco, Cristian J. Vaca-Rubio, Engin Zeydan, Kapal Dev, Zheng Li, Tomaso DeCola
https://arxiv.org/abs/2507.02680

On the Architectural Split and Radio Intelligence Controller Placement in Integrated O-RAN-enabled Non-Terrestrial Networks
The integration of Terrestrial Networks (TNs) with Non-Terrestrial Networks (NTNs) poses unique architectural and functional challenges due to heterogeneous propagation conditions, dynamic topologies and limited on-board processing capabilities. This paper examines architectural and functional split strategies that are consistent with O-RAN principles for future integrated TN-NTN systems. A taxonomy of split options is proposed that distributes RAN and core functions between satellites and grou…

This https://arxiv.org/abs/2505.08992 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

Dataflow & Tiling Strategies in Edge-AI FPGA Accelerators: A Comprehensive Literature Review
Edge-AI applications demand high-throughput, low-latency inference on FPGAs under tight resource and power constraints. This survey provides a comprehensive review of two key architectural decisions for FPGA-based neural network accelerators: (i) the dataflow (the order and manner in which data is moved and reused on chip), and (ii) the tiling/blocking strategy (how large tensors are partitioned to fit on-chip). We first present a broadened taxonomy of canonical dataflow styles: Weight-Stationa…

Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
Joydeep Chandra, Satyam Kumar Navneet
https://arxiv.org/abs/2507.20014 https://…

Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
As AI-driven dataspaces become integral to data sharing and collaborative analytics, ensuring privacy, performance, and policy compliance presents significant challenges. This paper provides a comprehensive review of privacy-preserving and policy-aware AI techniques, including Federated Learning, Differential Privacy, Trusted Execution Environments, Homomorphic Encryption, and Secure Multi-Party Computation, alongside strategies for aligning AI with regulatory frameworks such as GDPR and the EU…

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
Yuanchen Bei, Weizhi Zhang, Siwen Wang, Weizhi Chen, Sheng Zhou, Hao Chen, Yong Li, Jiajun Bu, Shirui Pan, Yizhou Yu, Irwin King, Fakhri Karray, Philip S. Yu
https://arxiv.org/abs/2506.18019

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
AI agents have experienced a paradigm shift, from early dominance by reinforcement learning (RL) to the rise of agents powered by large language models (LLMs), and now further advancing towards a synergistic fusion of RL and LLM capabilities. This progression has endowed AI agents with increasingly strong abilities. Despite these advances, to accomplish complex real-world tasks, agents are required to plan and execute effectively, maintain reliable memory, and coordinate smoothly with other age…

Prompt Injection Vulnerability of Consensus Generating Applications in Digital Democracy
Jairo Gudi\~no-Rosero, Cl\'ement Contet, Umberto Grandi, C\'esar A. Hidalgo
https://arxiv.org/abs/2508.04281

Prompt Injection Vulnerability of Consensus Generating Applications in Digital Democracy
Large Language Models (LLMs) are gaining traction as a method to generate consensus statements and aggregate preferences in digital democracy experiments. Yet, LLMs may introduce critical vulnerabilities in these systems. Here, we explore the impact of prompt-injection attacks targeting consensus generating systems by introducing a four-dimensional taxonomy of attacks. We test these attacks using LLaMA 3.1 8B and Chat GPT 4.1 Nano finding the LLMs more vulnerable to criticism attacks -- attacks…

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
Wei-Hsiang Huang, Chen-Wei Ke, Wei-Ning Chiu, Yu-Xuan Su, Chun-Chun Yang, Chieh-Yuan Cheng, Yun-Nung Chen, Pu-Jen Cheng
https://arxiv.org/abs/2505.23053

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
Large language models (LLMs) have introduced new paradigms for recommender systems by enabling richer semantic understanding and incorporating implicit world knowledge. In this study, we propose a systematic taxonomy that classifies existing approaches into two categories: (1) Pure LLM Recommenders, which rely solely on LLMs, and (2) Augmented LLM Recommenders, which integrate additional non-LLM techniques to enhance performance. This taxonomy provides a novel lens through which to examine the …

Assessing students understanding of the concept of electric potential difference based on the SOLO taxonomy in upper-secondary students for a targeted assessment
Rahmani Fateme, Ahmadi Kalateh Fatemeh
https://arxiv.org/abs/2507.13723

Assessing students understanding of the concept of electric potential difference based on the SOLO taxonomy in upper-secondary students for a targeted assessment
The concept of potential difference is fundamental to understanding electricity in upper secondary physics courses. However, students often find it challenging to grasp. To address this, we designed and implemented a test aimed at assessing students comprehension of potential difference. This researcher developed test, with a reliability coefficient of 0.74, was administered to 92 students in grades 11 and 12. To evaluate the test and gauge the depth of student learning, we employed the Structu…

Securing the Internet of Medical Things (IoMT): Real-World Attack Taxonomy and Practical Security Measures
Suman Deb, Emil Lupu, Emm Mic Drakakis, Anil Anthony Bharath, Zhen Kit Leung, Guang Rui Ma, Anupam Chattopadhyay
https://arxiv.org/abs/2507.19609

Securing the Internet of Medical Things (IoMT): Real-World Attack Taxonomy and Practical Security Measures
The Internet of Medical Things (IoMT) has the potential to radically improve healthcare by enabling real-time monitoring, remote diagnostics, and AI-driven decision making. However, the connectivity, embedded intelligence, and inclusion of a wide variety of novel sensors expose medical devices to severe cybersecurity threats, compromising patient safety and data privacy. In addition, many devices also have direct capacity - individually or in conjunction with other IoMT devices - to perform act…

Auto-scaling Approaches for Cloud-native Applications: A Survey and Taxonomy
Minxian Xu, Linfeng Wen, Junhan Liao, Huaming Wu, Kejiang Ye, Chengzhong Xu
https://arxiv.org/abs/2507.17128

Auto-scaling Approaches for Cloud-native Applications: A Survey and Taxonomy
The interactions within cloud-native applications are complex, with a constantly changing number of services and loads, posing higher demands on auto-scaling approach. This mainly involves several challenges such as microservices dependency analysis, performance profiling, anomaly detection, workload characterization and task co-location. Therefore, some advanced algorithms have been investigated into auto-scaling cloud-native applications to optimize system and application performance. These a…

Unpacking Graduate Students' Learning Experience with Generative AI Teaching Assistant in A Quantitative Methodology Course
Zhanxin Hao, Haifeng Luo, Yongyi Chen, Yu Zhang
https://arxiv.org/abs/2506.02966

Unpacking Graduate Students' Learning Experience with Generative AI Teaching Assistant in A Quantitative Methodology Course
The study was conducted in an Advanced Quantitative Research Methods course involving 20 graduate students. During the course, student inquiries made to the AI were recorded and coded using Bloom's taxonomy and the CLEAR framework. A series of independent sample t-tests and poisson regression analyses were employed to analyse the characteristics of different questions asked by students with different backgrounds. Post course interviews were conducted with 10 students to gain deeper insights int…

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges
Yiming Shen, Jiashuo Zhang, Zhenzhe Shao, Wenxuan Luo, Yanlin Wang, Ting Chen, Zibin Zheng, Jiachi Chen
https://arxiv.org/abs/2508.02773

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges
The convergence of Web3 technologies and AI agents represents a rapidly evolving frontier poised to reshape decentralized ecosystems. This paper presents the first and most comprehensive analysis of the intersection between Web3 and AI agents, examining five critical dimensions: landscape, economics, governance, security, and trust mechanisms. Through an analysis of 133 existing projects, we first develop a taxonomy and systematically map the current market landscape (RQ1), identifying distinct…

A Survey on False Information Detection: From A Perspective of Propagation on Social Networks
Kun Xie, Sibo Wang
https://arxiv.org/abs/2506.18052 https://

A Survey on False Information Detection: From A Perspective of Propagation on Social Networks
The proliferation of false information in the digital age has become a pressing concern, necessitating the development of effective and robust detection methods. This paper offers a comprehensive review of existing false information detection techniques, approached from a novel perspective that emphasizes the propagation characteristics of misinformation. We introduce a new taxonomy that categorizes these methods into homogeneous and heterogeneous propagation-based approaches, providing a deepe…

Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications
Wenxuan Wang, Zizhan Ma, Meidan Ding, Shiyi Zheng, Shengyuan Liu, Jie Liu, Jiaming Ji, Wenting Chen, Xiang Li, Linlin Shen, Yixuan Yuan
https://arxiv.org/abs/2508.00669

Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications
The proliferation of Large Language Models (LLMs) in medicine has enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning, a cornerstone of clinical practice. This has catalyzed a shift from single-step answer generation to the development of LLMs explicitly designed for medical reasoning. This paper provides the first systematic review of this emerging field. We propose a taxonomy of reasoning enhancement techniq…

This https://arxiv.org/abs/2503.02614 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Personalized Generation In Large Model Era: A Survey
In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents the first comprehensive survey on PGen, investigating existing research in this rapidly growing field. We conceptualize PGen from a unified perspective, systematically formalizing its key components, core objectives, and abstract workflows. Based on this unified perspective, we propose a multi-level taxonomy, offering …

(Re)-Defining Planets -- the Fundamental Plane of Planets
Madhu Kashyap Jagadeesh, Arkil D. Parikh, Margarita Safonova, Bernard Foing
https://arxiv.org/abs/2506.16063

(Re)-Defining Planets -- the Fundamental Plane of Planets
6000+ exoplanets are now detected using various techniques, with estimates of billions of planets existing in our Galaxy alone. They are called super-Earths, hot Earths, mini-Neptunes, hot Neptunes, sub-Neptunes, Saturns, Jupiters, hot Jupiters, gas giants, ice giants, rocky, terran, subterran, superterran, and so on. This prompted the emergence of many recent works on taxonomy, or classification, of exoplanets. However, there is still no basic, fundamental definition of 'What is a planet?'. IA…

Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Zhanna Kaufman, Madeline Endres, Cindy Xiong Bearfield, Yuriy Brun
https://arxiv.org/abs/2508.00140

Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Systems relying on ML have become ubiquitous, but so has biased behavior within them. Research shows that bias significantly affects stakeholders' trust in systems and how they use them. Further, stakeholders of different backgrounds view and trust the same systems differently. Thus, how ML models' behavior is explained plays a key role in comprehension and trust. We survey explainability visualizations, creating a taxonomy of design characteristics. We conduct user studies to evaluate five sta…

How do Pre-Trained Models Support Software Engineering? An Empirical Study in Hugging Face
Alexandra Gonz\'alez, Xavier Franch, David Lo, Silverio Mart\'inez-Fern\'andez
https://arxiv.org/abs/2506.03013

How do Pre-Trained Models Support Software Engineering? An Empirical Study in Hugging Face
Open-Source Pre-Trained Models (PTMs) provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software Engineering (SE) needs. To address this gap, we derive a taxonomy encompassing 147 SE tasks and apply an SE-oriented classification to PTMs in a popular open-source ML repository, Hugging Face (HF). Our repository mining study began with a systematically gathered database of PTMs from the HF API, considering their model card de…

In-context learning for the classification of manipulation techniques in phishing emails
Antony Dalmiere (LAAS-TRUST, LAAS), Guillaume Auriol (LAAS-TRUST, INSA Toulouse), Vincent Nicomette (LAAS-TSF, LAAS), Pascal Marchand (LERASS)
https://arxiv.org/abs/2506.22515

In-context learning for the classification of manipulation techniques in phishing emails
Traditional phishing detection often overlooks psychological manipulation. This study investigates using Large Language Model (LLM) In-Context Learning (ICL) for fine-grained classification of phishing emails based on a taxonomy of 40 manipulation techniques. Using few-shot examples with GPT-4o-mini on real-world French phishing emails (SignalSpam), we evaluated performance against a human-annotated test set (100 emails). The approach effectively identifies prevalent techniques (e.g., Baiting, …

Replaced article(s) found for nlin.AO. https://arxiv.org/list/nlin.AO/new
[1/1]:
- Taxonomy of Prediction
Alexandra Jurgens, James P. Crutchfield

Descriptions of Cantor Sets: A Set-Theoretic Survey and Open Problems
Mohsen Soltanifar
https://arxiv.org/abs/2506.13103 https://arxi…

Descriptions of Cantor Sets: A Set-Theoretic Survey and Open Problems
This survey synthesizes the principal descriptive set-theoretic perspectives on deterministic Cantor sets on the real line and charts directions for future study. After recounting their historical genesis and compiling an up-to-date taxonomy, we review the Borel hierarchy and four hierarchically ordered representations-general, nested, iterated-function-system (IFS), and q-ary expansion-presented from the most general to the most specific set-theoretic description of deterministic Cantor sets. …

Cybersecurity AI: The Dangerous Gap Between Automation and Autonomy
V\'ictor Mayoral-Vilches
https://arxiv.org/abs/2506.23592 https://

Cybersecurity AI: The Dangerous Gap Between Automation and Autonomy
The cybersecurity industry combines "automated" and "autonomous" AI, creating dangerous misconceptions about system capabilities. Recent milestones like XBOW topping HackerOne's leaderboard showcase impressive progress, yet these systems remain fundamentally semi-autonomous--requiring human oversight. Drawing from robotics principles, where the distinction between automation and autonomy is well-established, I take inspiration from prior work and establish a 6-level taxonomy (Level 0-5) disting…

The Impact of AI on Educational Assessment: A Framework for Constructive Alignment
Patrick Stokkink
https://arxiv.org/abs/2506.23815 https://

The Impact of AI on Educational Assessment: A Framework for Constructive Alignment
The influence of Artificial Intelligence (AI), and specifically Large Language Models (LLM), on education is continuously increasing. These models are frequently used by students, giving rise to the question whether current forms of assessment are still a valid way to evaluate student performance and comprehension. The theoretical framework developed in this paper is grounded in Constructive Alignment (CA) theory and Bloom's taxonomy for defining learning objectives. We argue that AI influences…

FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
Likun Tan, Kuan-Wei Huang, Kevin Wu
https://arxiv.org/abs/2507.20930 https://

FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
Hallucinations in large language models pose a critical challenge for applications requiring factual reliability, particularly in high-stakes domains such as finance. This work presents an effective approach for detecting and editing factually incorrect content in model-generated responses based on the provided context. Given a user-defined domain-specific error taxonomy, we construct a synthetic dataset by inserting tagged errors into financial question-answering corpora and then fine-tune fou…

A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications
Boyang Yang, Zijian Cai, Fengling Liu, Bach Le, Lingming Zhang, Tegawend\'e F. Bissyand\'e, Yang Liu, Haoye Tian
https://arxiv.org/abs/2506.23749

A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications
Large language models (LLMs) are reshaping automated program repair (APR). We categorize the recent 63 LLM-based APR systems published from January 2022 to June 2025 into four paradigms, and show how retrieval- or analysis-augmented contexts strengthen any of them. This taxonomy clarifies key trade-offs: fine-tuning delivers strong task alignment at high training cost; prompting enables rapid deployment but is limited by prompt design and context windows; procedural pipelines offer reproducible…

GlobDB: A comprehensive species-dereplicated microbial genome resource
Daan R. Speth (Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria), Nick Pullen (Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria), Samuel T. N. Aroney (Centre for Microbiome Research School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, Australia), Benjamin L. …

GlobDB: A comprehensive species-dereplicated microbial genome resource
Over the past years, substantial numbers of microbial species' genomes have been deposited outside of conventional INSDC databases. The GlobDB aggregates 14 independent genomic catalogues to provide a comprehensive database of species-dereplicated microbial genomes, with consistent taxonomy, annotations, and additional analysis resources. The GlobDB is available at https://globdb.org/.

"Mansion House: Chancellor Rachel Reeves axes plans for UK Green Taxonomy"
#UK #UnitedKingdom #Finance #Money

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
Senyao Li, Haozhao Wang, Wenchao Xu, Rui Zhang, Song Guo, Jingling Yuan, Xian Zhong, Tianwei Zhang, Ruixuan Li
https://arxiv.org/abs/2507.16731

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
As large language models (LLMs) evolve, deploying them solely in the cloud or compressing them for edge devices has become inadequate due to concerns about latency, privacy, cost, and personalization. This survey explores a collaborative paradigm in which cloud-based LLMs and edge-deployed small language models (SLMs) cooperate across both inference and training. We present a unified taxonomy of edge-cloud collaboration strategies. For inference, we categorize approaches into task assignment, t…

Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
Ina K. Schieferdecker
https://arxiv.org/abs/2506.14640

Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test automation and model-based testing. However, designing, developing, maintaining and evolving test automation is a considerable effort. Meanwhile, AI's breakthroughs in many engineering fields are opening…

Securing AI Agents with Information-Flow Control
Manuel Costa, Boris K\"opf, Aashish Kolluri, Andrew Paverd, Mark Russinovich, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Santiago Zanella-B\'eguelin
https://arxiv.org/abs/2505.23643

Securing AI Agents with Information-Flow Control
As AI agents become increasingly autonomous and capable, ensuring their security against vulnerabilities such as prompt injection becomes critical. This paper explores the use of information-flow control (IFC) to provide security guarantees for AI agents. We present a formal model to reason about the security and expressiveness of agent planners. Using this model, we characterize the class of properties enforceable by dynamic taint-tracking and construct a taxonomy of tasks to evaluate security…

SpeechIQ: Speech Intelligence Quotient Across Cognitive Levels in Voice Understanding Large Language Models
Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi
https://arxiv.org/abs/2507.19361

SpeechIQ: Speech Intelligence Quotient Across Cognitive Levels in Voice Understanding Large Language Models
We introduce Speech-based Intelligence Quotient (SIQ) as a new form of human cognition-inspired evaluation pipeline for voice understanding large language models, LLM Voice, designed to assess their voice understanding ability. Moving beyond popular voice understanding metrics such as word error rate (WER), SIQ examines LLM Voice across three cognitive levels motivated by Bloom's Taxonomy: (1) Remembering (i.e., WER for verbatim accuracy); (2) Understanding (i.e., similarity of LLM's interpreta…

This https://arxiv.org/abs/2412.07951 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents
Recent gains in popularity of AI conversational agents have led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences of individuals. Additionally, psychological risks have often been presented as a sub-category within broader AI-related risks in past taxonomy works, leading to under-represe…

A Task Taxonomy for Conformance Checking
Jana-Rebecca Rehse, Michael Grohs, Finn Klessascheck, Lisa-Marie Klein, Tatiana von Landesberger, Luise Pufahl
https://arxiv.org/abs/2507.11976

A Task Taxonomy for Conformance Checking
Conformance checking is a sub-discipline of process mining, which compares observed process traces with a process model to analyze whether the process execution conforms with or deviates from the process design. Organizations can leverage this analysis, for example to check whether their processes comply with internal or external regulations or to identify potential improvements. Gaining these insights requires suitable visualizations, which make complex results accessible and actionable. So fa…

"What's Up, Doc?": Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets
Akshay Paruchuri, Maryam Aziz, Rohit Vartak, Ayman Ali, Best Uchehara, Xin Liu, Ishan Chatterjee, Monica Agrawal
https://arxiv.org/abs/2506.21532

"What's Up, Doc?": Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets
People are increasingly seeking healthcare information from large language models (LLMs) via interactive chatbots, yet the nature and inherent risks of these conversations remain largely unexplored. In this paper, we filter large-scale conversational AI datasets to achieve HealthChat-11K, a curated dataset of 11K real-world conversations composed of 25K user messages. We use HealthChat-11K and a clinician-driven taxonomy for how users interact with LLMs when seeking healthcare information in or…

Mastery Learning Improves Performance on Complex Tasks on PCP Literacy Test
Chandana Srinivas, Elif E. Firat, Robert S. Laramee, Alark Joshi
https://arxiv.org/abs/2506.10164

Mastery Learning Improves Performance on Complex Tasks on PCP Literacy Test
Developing literacy with unfamiliar data visualization techniques such as Parallel Coordinate Plots (PCPs) can be a significant challenge for students. We adopted the Revised Bloom's taxonomy to instruct students on Parallel Coordinate Plots (PCPs) using Mastery Learning in the classroom. To evaluate Mastery Learning's impact, we conducted an intervention in a Data Visualization course to teach students about PCPs using the Revised Bloom's taxonomy with and without Mastery Learning. Based on ou…

ASTAGEN: Empirical Evaluation of Automated SATD Taxonomy Generation with LLMs
Sota Nakashima, Yuta Ishimoto, Masanari Kondo, Tao Xiao, Yasutaka Kamei
https://arxiv.org/abs/2506.09601

ASTAGEN: Empirical Evaluation of Automated SATD Taxonomy Generation with LLMs
Technical debt refers to suboptimal code that degrades software quality. When developers intentionally introduce such debt, it is called self-admitted technical debt (SATD). Since SATD hinders maintenance, identifying its categories is key to uncovering quality issues. Traditionally, constructing such taxonomies requires manually inspecting SATD comments and surrounding code, which is time-consuming, labor-intensive, and often inconsistent due to annotator subjectivity. This study presents ASTA…

A Literature Review on Simulation in Conversational Recommender Systems
Haoran Zhang, Xin Zhao, Jinze Chen, Junpeng Guo
https://arxiv.org/abs/2506.20291 ht…

A Literature Review on Simulation in Conversational Recommender Systems
Conversational Recommender Systems (CRSs) have garnered attention as a novel approach to delivering personalized recommendations through multi-turn dialogues. This review developed a taxonomy framework to systematically categorize relevant publications into four groups: dataset construction, algorithm design, system evaluation, and empirical studies, providing a comprehensive analysis of simulation methods in CRSs research. Our analysis reveals that simulation methods play a key role in tacklin…

Hybrid Annotation for Propaganda Detection: Integrating LLM Pre-Annotations with Human Intelligence
Ariana Sahitaj, Premtim Sahitaj, Veronika Solopova, Jiaao Li, Sebastian M\"oller, Vera Schmitt
https://arxiv.org/abs/2507.18343

Hybrid Annotation for Propaganda Detection: Integrating LLM Pre-Annotations with Human Intelligence
Propaganda detection on social media remains challenging due to task complexity and limited high-quality labeled data. This paper introduces a novel framework that combines human expertise with Large Language Model (LLM) assistance to improve both annotation consistency and scalability. We propose a hierarchical taxonomy that organizes 14 fine-grained propaganda techniques into three broader categories, conduct a human annotation study on the HQP dataset that reveals low inter-annotator agreeme…

What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
Quim Motger, Marc Oriol, Max Tiessler, Xavier Franch, Jordi Marco
https://arxiv.org/abs/2505.23452

What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
Opinion mining plays a vital role in analysing user feedback and extracting insights from textual data. While most research focuses on sentiment polarity (e.g., positive, negative, neutral), fine-grained emotion classification in app reviews remains underexplored. This paper addresses this gap by identifying and addressing the challenges and limitations in fine-grained emotion analysis in the context of app reviews. Our study adapts Plutchik's emotion taxonomy to app reviews by developing a str…

How to Elicit Explainability Requirements? A Comparison of Interviews, Focus Groups, and Surveys
Martin Obaidi, Jakob Droste, Hannah Deters, Marc Herrmann, Raymond Ochsner, Jil Kl\"under, Kurt Schneider
https://arxiv.org/abs/2505.23684

How to Elicit Explainability Requirements? A Comparison of Interviews, Focus Groups, and Surveys
As software systems grow increasingly complex, explainability has become a crucial non-functional requirement for transparency, user trust, and regulatory compliance. Eliciting explainability requirements is challenging, as different methods capture varying levels of detail and structure. This study examines the efficiency and effectiveness of three commonly used elicitation methods - focus groups, interviews, and online surveys - while also assessing the role of taxonomy usage in structuring a…

A Survey of Context Engineering for Large Language Models
Lingrui Mei, Jiayu Yao, Yuyao Ge, Yiwei Wang, Baolong Bi, Yujun Cai, Jiazhi Liu, Mingyu Li, Zhong-Zhi Li, Duzhen Zhang, Chenlin Zhou, Jiayi Mao, Tianze Xia, Jiafeng Guo, Shenghua Liu
https://arxiv.org/abs/2507.13334

A Survey of Context Engineering for Large Language Models
The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a formal discipline that transcends simple prompt design to encompass the systematic optimization of information payloads for LLMs. We present a comprehensive taxonomy decomposing Context Engineering into its foundational components and the sophisticated implementations that integrate them into intelligent systems. We fi…

SoK: Evaluating Jailbreak Guardrails for Large Language Models
Xunguang Wang, Zhenlan Ji, Wenxuan Wang, Zongjie Li, Daoyuan Wu, Shuai Wang
https://arxiv.org/abs/2506.10597

SoK: Evaluating Jailbreak Guardrails for Large Language Models
Large Language Models (LLMs) have achieved remarkable progress, but their deployment has exposed critical vulnerabilities, particularly to jailbreak attacks that circumvent safety mechanisms. Guardrails--external defense mechanisms that monitor and control LLM interaction--have emerged as a promising solution. However, the current landscape of LLM guardrails is fragmented, lacking a unified taxonomy and comprehensive evaluation framework. In this Systematization of Knowledge (SoK) paper, we pre…

Architecting Human-AI Cocreation for Technical Services -- Interaction Modes and Contingency Factors
Jochen Wulf, Jurg Meierhofer, Frank Hannich
https://arxiv.org/abs/2507.14034

Architecting Human-AI Cocreation for Technical Services -- Interaction Modes and Contingency Factors
Agentic AI systems, powered by Large Language Models (LLMs), offer transformative potential for value co-creation in technical services. However, persistent challenges like hallucinations and operational brittleness limit their autonomous use, creating a critical need for robust frameworks to guide human-AI collaboration. Drawing on established Human-AI teaming research and analogies from fields like autonomous driving, this paper develops a structured taxonomy of human-agent interaction. Based…

Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems
Qian Xiong, Yuekai Huang, Ziyou Jiang, Zhiyuan Chang, Yujia Zheng, Tianhao Li, Mingyang Li
https://arxiv.org/abs/2507.15296

Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems
The emergence of the tool agent paradigm has broadened the capability boundaries of the Large Language Model (LLM), enabling it to complete more complex tasks. However, the effectiveness of this paradigm is limited due to the issue of parameter failure during its execution. To explore this phenomenon and propose corresponding suggestions, we first construct a parameter failure taxonomy in this paper. We derive five failure categories from the invocation chain of a mainstream tool agent. Then, w…

Automatic Qiskit Code Refactoring Using Large Language Models
Jos\'e Manuel Su\'arez, Luis Mariano Bibb\'o, Joaquin Bogado, Alejandro Fernandez
https://arxiv.org/abs/2506.14535

Automatic Qiskit Code Refactoring Using Large Language Models
As quantum software frameworks evolve, developers face increasing challenges in maintaining compatibility with rapidly changing APIs. In this work, we present a novel methodology for refactoring Qiskit code using large language models (LLMs). We begin by extracting a taxonomy of migration scenarios from the different sources of official Qiskit documentation (such as release notes), capturing common patterns such as migration of functionality to different modules and deprecated usage. This taxon…

AI-Based Software Vulnerability Detection: A Systematic Literature Review
Samiha Shimmi, Hamed Okhravi, Mona Rahimi
https://arxiv.org/abs/2506.10280 https:…

AI-Based Software Vulnerability Detection: A Systematic Literature Review
Software vulnerabilities in source code pose serious cybersecurity risks, prompting a shift from traditional detection methods (e.g., static analysis, rule-based matching) to AI-driven approaches. This study presents a systematic review of software vulnerability detection (SVD) research from 2018 to 2023, offering a comprehensive taxonomy of techniques, feature representations, and embedding methods. Our analysis reveals that 91% of studies use AI-based methods, with graph-based models being th…

Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead
Priyavanshi Pathania, Nikhil Bamby, Rohit Mehra, Samarth Sikand, Vibhu Saujanya Sharma, Vikrant Kaulgud, Sanjay Podder, Adam P. Burden
https://arxiv.org/abs/2506.09683 …

Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead
The proliferation of software and AI comes with a hidden risk: its growing energy and carbon footprint. As concerns regarding environmental sustainability come to the forefront, understanding and optimizing how software impacts the environment becomes paramount. In this paper, we present a state-of-the-art review of methods and tools that enable the measurement of software and AI-related energy and/or carbon emissions. We introduce a taxonomy to categorize the existing work as Monitoring, Estim…