Tootfinder

Coverage-Guided Testing for Deep Learning Models: A Comprehensive Survey
Hongjing Guo, Chuanqi Tao, Zhiqiu Huang, Weiqin Zou
https://arxiv.org/abs/2507.00496

Coverage-Guided Testing for Deep Learning Models: A Comprehensive Survey
As Deep Learning (DL) models are increasingly applied in safety-critical domains, ensuring their quality has emerged as a pressing challenge in modern software engineering. Among emerging validation paradigms, coverage-guided testing (CGT) has gained prominence as a systematic framework for identifying erroneous or unexpected model behaviors. Despite growing research attention, existing CGT studies remain methodologically fragmented, limiting the understanding of current advances and emerging t…

Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research
Jennifer Haase, Sebastian Pokutta
https://arxiv.org/abs/2506.01839

Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research
As large language models (LLMs) transition from static tools to fully agentic systems, their potential for transforming social science research has become increasingly evident. This paper introduces a structured framework for understanding the diverse applications of LLM-based agents, ranging from simple data processors to complex, multi-agent systems capable of simulating emergent social dynamics. By mapping this developmental continuum across six levels, the paper clarifies the technical and …

Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis
Rafi Al Attrach, Pedro Moreira, Rajna Fani, Renato Umeton, Leo Anthony Celi
https://arxiv.org/abs/2507.01053

Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis
As ever-larger clinical datasets become available, they have the potential to unlock unprecedented opportunities for medical research. Foremost among them is Medical Information Mart for Intensive Care (MIMIC-IV), the world's largest open-source EHR database. However, the inherent complexity of these datasets, particularly the need for sophisticated querying skills and the need to understand the underlying clinical settings, often presents a significant barrier to their effective use. M3 lowers…

Invisible Architectures of Thought: Toward a New Science of AI as Cognitive Infrastructure
Giuseppe Riva
https://arxiv.org/abs/2507.22893 https://arxiv.org…

Invisible Architectures of Thought: Toward a New Science of AI as Cognitive Infrastructure
Contemporary human-AI interaction research overlooks how AI systems fundamentally reshape human cognition pre-consciously, a critical blind spot for understanding distributed cognition. This paper introduces "Cognitive Infrastructure Studies" (CIS) as a new interdisciplinary domain to reconceptualize AI as "cognitive infrastructures": foundational, often invisible systems conditioning what is knowable and actionable in digital societies. These semantic infrastructures transport meaning, operate…

Extraction of Physical Parameters of RRab Variables using Neural Network based Interpolator
Nitesh Kumar (Department of Physics, Applied Science Cluster, University of Petroleum and Energy Studies), Harinder P. Singh (Department of Physics and Astrophysics, University of Delhi, Delhi, India), Oleg Malkov (Institute of Astronomy of the Russian Academy of Sciences), Santosh Joshi (Aryabhatta Research Institute of Observational Sciences), Kefeng Tan (National Astronomical Observatories, C…

Extraction of Physical Parameters of RRab Variables using Neural Network based Interpolator
Determining the physical parameters of pulsating variable stars such as RR Lyrae is essential for understanding their internal structure, pulsation mechanisms, and evolutionary state. In this study, we present a machine learning framework that uses feedforward artificial neural networks (ANNs) to infer stellar parameters-mass ($M$), luminosity (log($L/L_\odot$)), effective temperature (log($T_{\rm eff}$)), and metallicity ($Z$)-directly from Transiting Exoplanet Survey Satellite (TESS) light cu…

Behavioral alignment in social networks
Yu Xia, Alex McAvoy, Qi Su
https://arxiv.org/abs/2506.00046 https://arxiv.org/pdf/2506.00046

Behavioral alignment in social networks
The orderly behaviors observed in large-scale groups, such as fish schooling and the organized movement of crowds, are both ubiquitous and essential for the survival and stability of these systems. Such complex collective behaviors often emerge from simple local interactions and strategy adjustments among individuals. Understanding how these basic rules shape complex group dynamics has long been a significant scientific challenge. Historically, research has predominantly focused on imitation an…

The Incomplete Bridge: How AI Research (Mis)Engages with Psychology
Han Jiang, Pengda Wang, Xiaoyuan Yi, Xing Xie, Ziang Xiao
https://arxiv.org/abs/2507.22847 https://

The Incomplete Bridge: How AI Research (Mis)Engages with Psychology
Social sciences have accumulated a rich body of theories and methodologies for investigating the human mind and behaviors, while offering valuable insights into the design and understanding of Artificial Intelligence (AI) systems. Focusing on psychology as a prominent case, this study explores the interdisciplinary synergy between AI and the field by analyzing 1,006 LLM-related papers published in premier AI venues between 2023 and 2025, along with the 2,544 psychology publications they cite. T…

Causal Decompositions of 1D Quantum Cellular Automata
Augustin Vanrietvelde, Octave Mestoudjian, Pablo Arrighi
https://arxiv.org/abs/2506.22219 https://

Causal Decompositions of 1D Quantum Cellular Automata
Understanding quantum theory's causal structure stands out as a major matter, since it radically departs from classical notions of causality. We present advances in the research program of causal decompositions, which investigates the existence of an equivalence between the causal and the compositional structures of unitary channels. Our results concern one-dimensional Quantum Cellular Automata (1D QCAs), i.e. unitary channels over a line of $N$ quantum systems (with or without periodic boundar…

Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda
Christian Meske, Tobias Hermanns, Esther von der Weiden, Kai-Uwe Loser, Thorsten Berger
https://arxiv.org/abs/2507.21928

Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda
Software development is undergoing a fundamental transformation as vibe coding becomes widespread, with large portions of contemporary codebases now being AI-generated. The disconnect between rapid adoption and limited conceptual understanding highlights the need for an inquiry into this emerging paradigm. Drawing on an intent perspective and historical analysis, we define vibe coding as a software development paradigm where humans and generative AI engage in collaborative flow to co-create sof…

Double Compact Binary Merger Rate Density in Open Star Clusters: Black Holes, Neutron Stars, and White Dwarfs
Savannah Cary, Michiko Fujii, Long Wang, Ataru Tanikawa
https://arxiv.org/abs/2506.22673

Double Compact Binary Merger Rate Density in Open Star Clusters: Black Holes, Neutron Stars, and White Dwarfs
Studying compact-object binary mergers in star clusters is crucial for understanding stellar evolution and dynamical interactions in galaxies. Open clusters in particular are more abundant over cosmic time than globular clusters, however, previous research on low-mass clusters with $\lesssim 10^3~\textrm{M}_{\odot}$ has focused on binary black holes (BBHs) or black hole-neutron star (BH-NS) binaries. Binary mergers of other compact objects, such as white dwarfs (WDs), are also crucial as progen…

AI, AGI, and learning efficiency
An addendum to this: I'm someone who would accurately be called "anti-AI" in the modern age, yet I'm also an "AI researcher" in some ways (have only dabbled in neutral nets).
I don't like:
- AI systems that are the product of labor abuses towards the data workers who curate their training corpora.
- AI systems that use inordinate amounts of water and energy during an intensifying climate catastrophe.
- AI systems that are fundamentally untrustworthy and which reinforce and amplify human biases, *especially* when those systems are exposed in a way that invites harms.
- AI systems which are designed to "save" my attention or brain bandwidth but such my doing so cripple my understating of the things I might use them for when I fact that understanding was the thing I was supposed to be using my time to gain, and where the later lack of such understanding will be costly to me.
- AI systems that are designed by and whose hype fattens the purse of people who materially support genocide and the construction of concentration campus (a.k.a. fascists).
In other words, I do not like and except in very extenuating circumstances I will not use ChatGPT, Claude, Copilot, Gemini, etc.
On the other hand, I do like:
- AI research as an endeavor to discover new technologies.
- Generative AI as a research topic using a spectrum of different methods.
- Speculating about non-human intelligences, including artificial ones, and including how to behave ethically towards them.
- Large language models as a specific technique, and autoencoders and other neural networks, assuming they're used responsibly in terms of both resource costs & presentation to end users.
I write this because I think some people (especially folks without CS backgrounds) may feel that opposing AI for all the harms it's causing runs the risk of opposing technological innovation more broadly, and/or may feel there's a risk that they will be "left behind" as everyone else embraces the hype and these technologies inevitability become ubiquitous and essential (I know I feel this way sometimes). Just know that is entirely possible and logically consistent to both oppose many forms of modern AI while also embracing and even being optimistic about AI research, and that while LLMs are currently all the rage, they're not the endpoint of what AI will look like in the future, and their downsides are not inherent in AI development.

AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind
Meryem Yilmaz Soylu, Jeonghyun Lee, Jui-Tse Hung, Christopher Zhang Cui, David A. Joyner
https://arxiv.org/abs/2507.21654

AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind
As Artificial Intelligence (AI) tools become increasingly embedded in higher education, understanding how students interact with these systems is essential to supporting effective learning. This study examines how students' AI literacy and prior exposure to AI technologies shape their perceptions of Socratic Mind, an interactive AI-powered formative assessment tool. Drawing on Self-Determination Theory and user experience research, we analyze relationships among AI literacy, perceived usability…

Decompiling Smart Contracts with a Large Language Model
Isaac David, Liyi Zhou, Dawn Song, Arthur Gervais, Kaihua Qin
https://arxiv.org/abs/2506.19624 http…

Decompiling Smart Contracts with a Large Language Model
The widespread lack of broad source code verification on blockchain explorers such as Etherscan, where despite 78,047,845 smart contracts deployed on Ethereum (as of May 26, 2025), a mere 767,520 (< 1%) are open source, presents a severe impediment to blockchain security. This opacity necessitates the automated semantic analysis of on-chain smart contract bytecode, a fundamental research challenge with direct implications for identifying vulnerabilities and understanding malicious behavior. Pre…

Towards Industrial Convergence : Understanding the evolution of scientific norms and practices in the field of AI
Antoine Houssard
https://arxiv.org/abs/2505.17945

Towards Industrial Convergence : Understanding the evolution of scientific norms and practices in the field of AI
In the field of artificial intelligence (AI) research, there seems to be a rapprochement between academics and industrial forces. The aim of this study is to assess whether and to what extent industrial domination in the field as well as the ever more frequent switch between academia and industry resulted in the adoption of industrial norms and practices by academics. Using bibliometric information and data on scientific code, we aimed to understand academic and industrial researchers' practice…

Understanding Bias in Perceiving Dimensionality Reduction Projections
Seoyoung Doh, Hyeon Jeon, Sungbok Shin, Ghulam Jilani Quadri, Nam Wook Kim, Jinwook Seo
https://arxiv.org/abs/2507.20805

Understanding Bias in Perceiving Dimensionality Reduction Projections
Selecting the dimensionality reduction technique that faithfully represents the structure is essential for reliable visual communication and analytics. In reality, however, practitioners favor projections for other attractions, such as aesthetics and visual saliency, over the projection's structural faithfulness, a bias we define as visual interestingness. In this research, we conduct a user study that (1) verifies the existence of such bias and (2) explains why the bias exists. Our study sugge…

Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision
Yuping He, Yifei Huang, Guo Chen, Lidong Lu, Baoqi Pei, Jilan Xu, Tong Lu, Yoichi Sato
https://arxiv.org/abs/2506.06253

Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision
Perceiving the world from both egocentric (first-person) and exocentric (third-person) perspectives is fundamental to human cognition, enabling rich and complementary understanding of dynamic environments. In recent years, allowing the machines to leverage the synergistic potential of these dual perspectives has emerged as a compelling research direction in video understanding. In this survey, we provide a comprehensive review of video understanding from both exocentric and egocentric viewpoint…

Ensemble Modeling of the Solar Wind Flow with Boundary Conditions Governed by Synchronic Photospheric Magnetograms. I. Multi-point Validation in the Inner Heliosphere
Dinesha V. Hegde (Department of Space Science, The University of Alabama in Huntsville, Huntsville, USA, Center for Space Plasma and Aeronomic Research, The University of Alabama in Huntsville, Huntsville, USA), Tae K. Kim (Center for Space Plasma and Aeronomic Research, The University of Alabama in Huntsville, Huntsville…

Ensemble Modeling of the Solar Wind Flow with Boundary Conditions Governed by Synchronic Photospheric Magnetograms. I. Multi-point Validation in the Inner Heliosphere
The solar wind (SW) is a vital component of space weather, providing a background for solar transients such as coronal mass ejections, stream interaction regions, and energetic particles propagating toward Earth. Accurate prediction of space weather events requires a precise description and thorough understanding of physical processes occurring in the ambient SW plasma. Ensemble simulations of the three-dimensional SW flow are performed using an empirically-driven magnetohydrodynamic heliospher…

Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example
Yuhan Helena Liu, Guangyu Robert Yang, Christopher J. Cueva
https://arxiv.org/abs/2506.06904

Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example
Understanding how the brain learns may be informed by studying biologically plausible learning rules. These rules, often approximating gradient descent learning to respect biological constraints such as locality, must meet two critical criteria to be considered an appropriate brain model: (1) good neuroscience task performance and (2) alignment with neural recordings. While extensive research has assessed the first criterion, the second remains underexamined. Employing methods such as Procruste…

On the Modern Structure of the Gauss-Landau Theorem
Manuel M. Aguilera
https://arxiv.org/abs/2506.15101 https://arxiv.org/pdf/2506.15…

On the Modern Structure of the Gauss-Landau Theorem
We formalize the Gauss-Landau theorem, providing a unified prime factorization approach to computing the GCD and LCM of finite nonzero integer sets. Although commonly used as a heuristic or technique in elementary number theory education, these theorems have not been explicitly formalized or named in the literature. This formalization aims to enhance understanding and facilitate adoption in mathematical instruction and research.

Understanding the Error Sensitivity of Privacy-Aware Computing
Mat\'ias Mazzanti (University of Buenos Aires), Esteban Mocskos (University of Buenos Aires), Augusto Vega (IBM T. J. Watson Research Center), Pradip Bose (IBM T. J. Watson Research Center)
https://arxiv.org/abs/2506.07957

Understanding the Error Sensitivity of Privacy-Aware Computing
Homomorphic Encryption (HE) enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation. In particular, domains such as healthcare, finance, and government, where data privacy and security are of utmost importance, can benefit from HE by enabling third-party computation and services on sensitive data. In other words, HE constitutes the "Holy Grail" of cryptography: data remains encrypted all the time, being protected while in …

Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural Networks
Yuki Tomoda, Ichiro Tsuda, Yutaka Yamaguti
https://arxiv.org/abs/2507.12858

Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural Networks
Functional differentiation in the brain emerges as distinct regions specialize and is key to understanding brain function as a complex system. Previous research has modeled this process using artificial neural networks with specific constraints. Here, we propose a novel approach that induces functional differentiation in recurrent neural networks by minimizing mutual information between neural subgroups via mutual information neural estimation. We apply our method to a 2-bit working memory task…

Position: We Need An Algorithmic Understanding of Generative AI
Oliver Eberle, Thomas McGee, Hamza Giaffar, Taylor Webb, Ida Momennejad
https://arxiv.org/abs/2507.07544

Position: We Need An Algorithmic Understanding of Generative AI
What algorithms do LLMs actually learn and use to solve problems? Studies addressing this question are sparse, as research priorities are focused on improving performance through scale, leaving a theoretical and empirical gap in understanding emergent algorithms. This position paper proposes AlgEval: a framework for systematic research into the algorithms that LLMs learn and use. AlgEval aims to uncover algorithmic primitives, reflected in latent representations, attention, and inference-time c…

PRISON: Unmasking the Criminal Potential of Large Language Models
Xinyi Wu, Geng Hong, Pei Chen, Yueyue Chen, Xudong Pan, Min Yang
https://arxiv.org/abs/2506.16150

PRISON: Unmasking the Criminal Potential of Large Language Models
As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify. Existing research overlooked the systematic understanding and assessment of their criminal capability in realistic interactions. We propose a unified framework PRISON, to quantify LLMs' criminal potential across five dimensions: False Statements, Frame-Up, Psychological Manipulation, Emotional Disguise, and Moral Disengagement. Using structured crime scenarios adapted from classic film…

Towards Understanding Decision Problems As a Goal of Visualization Design
Lena Cibulski, Stefan Bruckner
https://arxiv.org/abs/2507.18428 https://arxiv.org…

Towards Understanding Decision Problems As a Goal of Visualization Design
Decision-making is a central yet under-defined goal in visualization research. While existing task models address decision processes, they often neglect the conditions framing a decision. To better support decision-making tasks, we propose a characterization scheme that describes decision problems through key properties of the data, users, and task context. This scheme helps visualization researchers specify decision-support claims more precisely and informs the design of appropriate visual enc…

Flexible Operator Fusion for Fast Sparse Transformer with Diverse Masking on GPU
Wenhao Dai, Haodong Deng, Mengfei Rong, Xinyu Yang, Hongyu Liu, Fangxin Liu, Hailong Yang, Weifeng Liu, Qingxiao Sun
https://arxiv.org/abs/2506.06095

Flexible Operator Fusion for Fast Sparse Transformer with Diverse Masking on GPU
Large language models are popular around the world due to their powerful understanding capabilities. As the core component of LLMs, accelerating Transformer through parallelization has gradually become a hot research topic. Mask layers introduce sparsity into Transformer to reduce calculations. However, previous works rarely focus on the performance optimization of sparse Transformer. Moreover, rule-based mechanisms ignore the fusion opportunities of mixed-type operators and fail to adapt to va…

Re-Evaluating Code LLM Benchmarks Under Semantic Mutation
Zhiyuan Pan, Xing Hu, Xin Xia, Xiaohu Yang
https://arxiv.org/abs/2506.17369 https://

Re-Evaluating Code LLM Benchmarks Under Semantic Mutation
In the era of large language models (LLMs), code benchmarks have become an important research area in software engineering and are widely used by practitioners. These benchmarks evaluate the performance of LLMs on specific code-related tasks, such as code understanding and generation. A critical step in constructing code benchmarks is the design of prompts. However, as existing code benchmarks typically rely on a single prompt template per task, they are prone to the issue of prompt sensitivity…

Bayesian Deep Gaussian Processes for Correlated Functional Data: A Case Study in Cosmological Matter Power Spectra
Stephen A. Walsh, Annie S. Booth, David Higdon, Jared Clark, Kelly R. Moran, Katrin Heitmann
https://arxiv.org/abs/2507.18683

Bayesian Deep Gaussian Processes for Correlated Functional Data: A Case Study in Cosmological Matter Power Spectra
Understanding the structure of our universe and the distribution of matter is an area of active research. As cosmological surveys grow in complexity, the development of emulators to efficiently and effectively predict matter power spectra is essential. We are particularly motivated by the Mira-Titan Universe simulation suite that, for a specified cosmological parameterization (termed a "cosmology"), provides multiple response curves of various fidelities, including correlated functional realiza…

Perspectives on How Sociology Can Advance Theorizing about Human-Chatbot Interaction and Developing Chatbots for Social Good
Celeste Campos-Castillo, Xuan Kang, Linnea I. Laestadius
https://arxiv.org/abs/2507.05030

Perspectives on How Sociology Can Advance Theorizing about Human-Chatbot Interaction and Developing Chatbots for Social Good
Recently, research into chatbots (also known as conversational agents, AI agents, voice assistants), which are computer applications using artificial intelligence to mimic human-like conversation, has grown sharply. Despite this growth, sociology lags other disciplines (including computer science, medicine, psychology, and communication) in publishing about chatbots. We suggest sociology can advance understanding of human-chatbot interaction and offer four sociological theories to enhance extan…

Inhomogeneous stellar mixing in the final hours before the Cassiopeia A supernova
Toshiki Sato, Kai Matsunaga, Hiroyuki Uchida, Satoru Katsuda, Koh Takahashi, Hideyuki Umeda, Tomoya Takiwaki, Ryo Sawada, Takashi Yoshida, Ko Nakamura, Yui Kuboike, Paul P. Plucinsky, John P. Hughes
https://arxiv.org/abs/2507.07563

Inhomogeneous stellar mixing in the final hours before the Cassiopeia A supernova
Understanding stars and their evolution is a key goal of astronomical research and has long been a focus of human interest. In recent years, theorists have paid much attention to the final interior processes within massive stars, as they can be essential for revealing neutrino-driven supernova mechanisms and other potential transients of massive star collapse. However, it is challenging to observe directly the last hours of a massive star before explosion, since it is the supernova event that t…

Phase Evolution and Substrate-Dependent Nucleation of Quartz GeO$_2$ Films Grown by MOCVD on r- and c-Plane Sapphires
Botong Li, Imteaz Rahaman, Hunter Ellis, Bobby G. Duersch, Kathy Anderson, Kai Fu
https://arxiv.org/abs/2506.09380

Phase Evolution and Substrate-Dependent Nucleation of Quartz GeO$_2$ Films Grown by MOCVD on r- and c-Plane Sapphires
Ultrawide-bandgap (UWBG) semiconductors, such as GeO$_2$, are gaining significant attention for their potential in high-performance applications, particularly in piezoelectric devices. Despite extensive research, a comprehensive understanding of the growth dynamics and phase evolution of GeO$_2$ films via metal-organic chemical vapor deposition (MOCVD) remains insufficient. In this study, we investigate the growth behavior and morphological evolution of GeO$_2$ thin films on r-plane and c-plane…

Unveiling prethermalization and thermal processes through the simplest one-dimensional topological model
Guowen Yang, Jiale Wang, Yichuan Chen, Limin Song, Shiqi Xia, Daohong Song, Zhigang Chen, Nikolaos K. Efremidis
https://arxiv.org/abs/2507.04101

Unveiling prethermalization and thermal processes through the simplest one-dimensional topological model
Drawing on classical thermodynamic principles-such as the equipartition of energy and entropy maximization-extensive research has shown that the evolution of optical power in multimode optical systems tends toward a Rayleigh-Jeans distribution at thermal equilibrium. Understanding of the processes associated with the thermalization dynamics are of fundamental importance in analyzing and controlling such complex systems. In this work, we utilize a one-dimensional Su-Schrieffer-Heeger lattice as …

Critical Nodes Identification in Complex Networks: A Survey
Duxin Chen, Jiawen Chen, Xiaoyu Zhang, Qinghan Jia, Xiaolu Liu, Ye Sun, Linyuan Lv, Wenwu Yu
https://arxiv.org/abs/2507.06164

Critical Nodes Identification in Complex Networks: A Survey
Complex networks have become essential tools for understanding diverse phenomena in social systems, traffic systems, biomolecular systems, and financial systems. Identifying critical nodes is a central theme in contemporary research, serving as a vital bridge between theoretical foundations and practical applications. Nevertheless, the intrinsic complexity and structural heterogeneity characterizing real-world networks, with particular emphasis on dynamic and higher-order networks, present subs…

The Study on Modified Theories of General Relativity: A Differential Geometric Approach
N. S. Kavya
https://arxiv.org/abs/2507.04031 https://

The Study on Modified Theories of General Relativity: A Differential Geometric Approach
The introduction of General Relativity (GR) in 1915 revolutionized our understanding of gravity, but over time, its limitations in explaining phenomena like dark energy, dark matter, and quantum gravity have motivated alternative theories. Early modifications, such as Weyl's 1919 proposal, focused on adding higher-order terms to the Einstein-Hilbert action. GR's non-renormalizability further strengthened the case for extending it. A central theme of modern gravity research is modifying the geom…

International Catacomb Society Online Panel with Recent Shohet Grant Recipients | August 5, 2025
https://ift.tt/DUGJdPy
CFP: Failure: Understanding Art as Process, 1150-1750 (Florence, 15-17 Oct 20) Call for…
via Input 4 RELCFP

Insights from Railway Professionals: Rethinking Railway assumptions regarding safety and autonomy
Josh Hunter, John McDermid, Simon Burton
https://arxiv.org/abs/2507.17756 https…

Insights from Railway Professionals: Rethinking Railway assumptions regarding safety and autonomy
This study investigates how railway professionals perceive safety as a concept within rail, with the intention to help inform future technological developments within the industry. Through a series of interviews with drivers, route planners,and administrative personnel, the research explores the currentstate of safety practices, the potential for automation and the understanding of the railway as a system of systems. Key findings highlight a cautious attitude towards automation, a preference fo…

Numerical Investigation of Wave Scattering in Granular Media: Grain-Scale Inversion and the Role of Boundary Effects
Ning Liu, Wen-Tao Hu
https://arxiv.org/abs/2507.07455

Numerical Investigation of Wave Scattering in Granular Media: Grain-Scale Inversion and the Role of Boundary Effects
Seismic coda waves, once regarded as noise, are now recognized as key indicators of wave scattering in seismograms. Aki (1969) first proposed that these waves result from small-scale heterogeneities in the Earth's interior, spurring research into their interpretation and applications. Coda waves are now known to carry valuable information about subsurface heterogeneity, making the study of scattering essential for understanding complex geological structures. Wave scattering in unconsolidated …

An Empirical Study of GenAI Adoption in Open-Source Game Development: Tools, Tasks, and Developer Challenges
Xiang Echo Chen, Wenhan Zhu, Guoshuai Albert Shi, Michael W. Godfrey
https://arxiv.org/abs/2507.18029

An Empirical Study of GenAI Adoption in Open-Source Game Development: Tools, Tasks, and Developer Challenges
The growing capabilities of generative AI (GenAI) have begun to reshape how games are designed and developed, offering new tools for content creation, gameplay simulation, and design ideation. While prior research has explored traditional uses of AI in games, such as controlling agents or generating procedural content. There is limited empirical understanding of how GenAI is adopted by developers in real-world contexts, especially within the open-source community. This study aims to explore how…

This https://arxiv.org/abs/2505.19988 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Automatic Metadata Extraction for Text-to-SQL
Large Language Models (LLMs) have recently become sophisticated enough to automate many tasks ranging from pattern finding to writing assistance to code generation. In this paper, we examine text-to-SQL generation. We have observed from decades of experience that the most difficult part of query development lies in understanding the database contents. These experiences inform the direction of our research. Text-to-SQL benchmarks such as SPIDER and Bird contain extensive metadata that is gener…

Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control
Anton Klenitskiy, Konstantin Polev, Daria Denisova, Alexey Vasilev, Dmitry Simakov, Gleb Gusev
https://arxiv.org/abs/2507.12202

Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control
Many current state-of-the-art models for sequential recommendations are based on transformer architectures. Interpretation and explanation of such black box models is an important research question, as a better understanding of their internals can help understand, influence, and control their behavior, which is very important in a variety of real-world applications. Recently sparse autoencoders (SAE) have been shown to be a promising unsupervised approach for extracting interpretable features f…

Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs
Yahan Yu, Yuyang Dong, Masafumi Oyamada
https://arxiv.org/abs/2507.06999

Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs
Reasoning is a key capability for large language models (LLMs), particularly when applied to complex tasks such as mathematical problem solving. However, multimodal reasoning research still requires further exploration of modality alignment and training costs. Many of these approaches rely on additional data annotation and relevant rule-based rewards to enhance the understanding and reasoning ability, which significantly increases training costs and limits scalability. To address these challeng…

Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?
Giovanni M. Di Liberto, Emily Y. J. Ip
https://arxiv.org/abs/2506.05494 https://…

Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?
Understanding the neural basis of speech communication is essential for uncovering how sounds are translated into meaning, how that changes with development, ageing, and speech-related deficits, as well as contributing to brain-computer interfaces research. While traditional neurophysiological studies have relied on simplified, controlled paradigms, recent advances have shifted the field toward more ecologically-valid approaches. Here, we examine the impact of continuous speech research and dis…

"Mapping What I Feel": Understanding Affective Geovisualization Design Through the Lens of People-Place Relationships
Xingyu Lan, Yutong Yang, Yifan Wang
https://arxiv.org/abs/2507.11841

"Mapping What I Feel": Understanding Affective Geovisualization Design Through the Lens of People-Place Relationships
Affective visualization design is an emerging research direction focused on communicating and influencing emotion through visualization. However, as revealed by previous research, this area is highly interdisciplinary and involves theories and practices from diverse fields and disciplines, thus awaiting analysis from more fine-grained angles. To address this need, this work focuses on a pioneering and relatively mature sub-area, affective geovisualization design, to further the research in this…

Privacy-Aware, Public-Aligned: Embedding Risk Detection and Public Values into Scalable Clinical Text De-Identification for Trusted Research Environments
Arlene Casey, Stuart Dunbar, Franz Gruber, Samuel McInerney, Mat\'u\v{s} Falis, Pamela Linksted, Katie Wilde, Kathy Harrison, Alison Hamilton, Christian Cole
https://arxiv.…

Privacy-Aware, Public-Aligned: Embedding Risk Detection and Public Values into Scalable Clinical Text De-Identification for Trusted Research Environments
Clinical free-text data offers immense potential to improve population health research such as richer phenotyping, symptom tracking, and contextual understanding of patient care. However, these data present significant privacy risks due to the presence of directly or indirectly identifying information embedded in unstructured narratives. While numerous de-identification tools have been developed, few have been tested on real-world, heterogeneous datasets at scale or assessed for governance read…

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…

An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis
Mingda Zhang, Na Zhao, Jianglong Qing, Qing xu, Kaiwen Pan, Ting luo
https://arxiv.org/abs/2507.07893

An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis
The rapid development of artificial intelligence has positioned large language models as fundamental components of intelligent legal systems. However, these models face significant limitations in legal dispute analysis, including insufficient legal knowledge representation, limited concept understanding, and reasoning deficiencies. This research proposes an enhanced framework integrating prompt engineering with multidimensional knowledge graphs. The framework introduces a three-stage hierarchic…

Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation
Kazi Mahathir Rahman, Naveed Imtiaz Nafis, Md. Farhan Sadik, Mohammad Al Rafi, Mehedi Hasan Shahed
https://arxiv.org/abs/2507.06530

Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation
Helping deaf and hard-of-hearing people communicate more easily is the main goal of Automatic Sign Language Translation. Although most past research has focused on turning sign language into text, doing the reverse, turning spoken English into sign language animations, has been largely overlooked. That's because it involves multiple steps, such as understanding speech, translating it into sign-friendly grammar, and generating natural human motion. In this work, we introduce a complete pipeline …

10th Enemy Encounters Webinar “Rumours and 19th-20th Century Religious Resistance, State Repression and Maoist Campaigns in China”
https://ift.tt/Gdnl5hN
CFP: Failure: Understanding Art as Process, 1150-1750 (Florence, 15-17 Oct 20) Call for…
via Input 4 RELCFP

"How can we learn and use AI at the same time?:: Participatory Design of GenAI with High School Students
Isabella Pu, Prerna Ravi, Linh Dieu Dinh, Chelsea Joe, Caitlin Ogoe, Zixuan Li, Cynthia Breazeal, Anastasia K. Ostrowski
https://arxiv.org/abs/2506.15525

The Everyday Security of Living with Conflict
Jessica McClearn, Reem Talhouk, Rikke Bjerg Jensen
https://arxiv.org/abs/2506.09580 https://

The Everyday Security of Living with Conflict
When `cyber' is used as a prefix, attention is typically drawn to the technological and spectacular aspects of war and conflict -- and, by extension, security. We offer a different approach to engaging with and understanding security in such contexts, by foregrounding the everyday -- mundane -- experiences of security within communities living with and fleeing from war. We do so through three vignettes from our field research in Colombia, Lebanon and Sweden, respectively, and by highlighting th…

A Survey of AIOps in the Era of Large Language Models
Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Aiwei Liu, Yong Yang, Zhonghai Wu, Xuming Hu, Philip S. Yu, Ying Li
https://arxiv.org/abs/2507.12472

A Survey of AIOps in the Era of Large Language Models
As large language models (LLMs) grow increasingly sophisticated and pervasive, their application to various Artificial Intelligence for IT Operations (AIOps) tasks has garnered significant attention. However, a comprehensive understanding of the impact, potential, and limitations of LLMs in AIOps remains in its infancy. To address this gap, we conducted a detailed survey of LLM4AIOps, focusing on how LLMs can optimize processes and improve outcomes in this domain. We analyzed 183 research paper…

The Gauss-Markov Adjunction: Categorical Semantics of Residuals in Supervised Learning
Moto Kamiura
https://arxiv.org/abs/2507.02442 https://

The Gauss-Markov Adjunction: Categorical Semantics of Residuals in Supervised Learning
Enhancing the intelligibility and interpretability of machine learning is a crucial task in responding to the demand for Explicability as an AI principle, and in promoting the better social implementation of AI. The aim of our research is to contribute to this improvement by reformulating machine learning models through the lens of category theory, thereby developing a semantic framework for structuring and understanding AI systems. Our categorical modeling in this paper clarifies and formalize…

iReDev: A Knowledge-Driven Multi-Agent Framework for Intelligent Requirements Development
Dongming Jin, Weisong Sun, Jiangping Huang, Peng Liang, Jifeng Xuan, Yang Liu, Zhi Jin
https://arxiv.org/abs/2507.13081

iReDev: A Knowledge-Driven Multi-Agent Framework for Intelligent Requirements Development
Requirements development is a critical phase as it is responsible for providing a clear understanding of what stakeholders need. It involves collaboration among stakeholders to extract explicit requirements and address potential conflicts, which is time-consuming and labor-intensive. Recently, multi-agent systems for software development have attracted much attention. However, existing research provides limited support for requirements development and overlooks the injection of human knowledge …

A Mapping Study About Training in Industry Context in Software Engineering
Breno Alves de Andrade, Rodrigo Siqueira, Lidiane Gomes, Antonio Oliveira, Danilo Monteiro Ribeiro
https://arxiv.org/abs/2506.12590

A Mapping Study About Training in Industry Context in Software Engineering
Context: Corporate training plays a strategic role in the continuous development of professionals in the software engineering industry. However, there is a lack of systematized understanding of how training initiatives are designed, implemented, and evaluated within this domain. Objective: This study aims to map the current state of research on corporate training in software engineering in industry settings, using Eduardo Salas' training framework as an analytical lens. Method: A systematic…

Understanding Visually Impaired Tramway Passengers Interaction with Public Transport Systems
Dominik Mimra, Dominik Kaar, Enrico Del Re, Novel Certad, Joshua Cherian Varughese, David Seibt, Cristina Olaverri-Monreal
https://arxiv.org/abs/2506.03687

Understanding Visually Impaired Tramway Passengers Interaction with Public Transport Systems
Designing inclusive public transport services is crucial to developing modern, barrier-free smart city infrastructure. This research contributes to the design of inclusive public transport by considering accessibility challenges emerging from socio-technical systems, thus demanding the integration of technological and social solutions. Using Actor-Network Theory (ANT) as a theoretical framework and a mixed-method approach, including shadowing and a focus group, this study examines the socio-tec…

Augmenting the Generality and Performance of Large Language Models for Software Engineering
Fabian C. Pe\~na
https://arxiv.org/abs/2506.11548 https://

Augmenting the Generality and Performance of Large Language Models for Software Engineering
Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources o…