Tootfinder

Task Decoding based on Eye Movements using Synthetic Data Augmentation
Shanmuka Sadhu, Arca Baran, Preeti Pandey, Ayush Kumar
https://arxiv.org/abs/2509.11547 https://

Task Decoding based on Eye Movements using Synthetic Data Augmentation
Machine learning has been extensively used in various applications related to eye-tracking research. Understanding eye movement is one of the most significant subsets of eye-tracking research that reveals the scanning pattern of an individual. Researchers have thoroughly analyzed eye movement data to understand various eye-tracking applications, such as attention mechanisms, navigational behavior, task understanding, etc. The outcome of traditional machine learning algorithms used for decoding …

The Ethics Engine: A Modular Pipeline for Accessible Psychometric Assessment of Large Language Models
Jake Van Clief, Constantine Kyritsopoulos
https://arxiv.org/abs/2510.11742 …

The Ethics Engine: A Modular Pipeline for Accessible Psychometric Assessment of Large Language Models
As Large Language Models increasingly mediate human communication and decision-making, understanding their value expression becomes critical for research across disciplines. This work presents the Ethics Engine, a modular Python pipeline that transforms psychometric assessment of LLMs from a technically complex endeavor into an accessible research tool. The pipeline demonstrates how thoughtful infrastructure design can expand participation in AI research, enabling investigators across cognitive…

Lost in Data: How Older Adults Perceive and Navigate Health Data Representations
Peterson Jean, Emma Murphy, Enda Bates
https://arxiv.org/abs/2509.11876 https://

Lost in Data: How Older Adults Perceive and Navigate Health Data Representations
As the ageing population grows, older adults increasingly rely on wearable devices to monitor chronic conditions. However, conventional health data representations (HDRs) often present accessibility challenges, particularly for critical health parameters like blood pressure and sleep data. This study explores how older adults interact with these representations, identifying key barriers such as semantic inconsistency and difficulties in understanding. While research has primarily focused on dat…

Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes
Briana Lee, Samantha Limon, Alyssia Chen, Kenny Ka'aiakamanu-Quibilan, Anthony Peruma
https://arxiv.org/abs/2509.10717

Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes
The technology industry offers exciting and diverse career opportunities, ranging from traditional software development to emerging fields such as artificial intelligence, cybersecurity, and data science. Career fairs play a crucial role in helping Computer Science (CS) students understand the various career pathways available to them in the industry. However, limited research exists on how CS students experience and benefit from these events. Through a survey of 86 students, we investigate the…

PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval
Wonbin Kweon, Runchu Tian, SeongKu Kang, Pengcheng Jiang, Zhiyong Lu, Jiawei Han, Hwanjo Yu
https://arxiv.org/abs/2510.09897

PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval
Scientific document retrieval is a critical task for enabling knowledge discovery and supporting research across diverse domains. However, existing dense retrieval methods often struggle to capture fine-grained scientific concepts in texts due to their reliance on holistic embeddings and limited domain understanding. Recent approaches leverage large language models (LLMs) to extract fine-grained semantic entities and enhance semantic matching, but they typically treat entities as independent fr…

Weekend Reads
* Fingerprinting DPI devices
https://arxiv.org/abs/2509.09081
* Exportation of China's GFW
https://

Fingerprinting Deep Packet Inspection Devices by Their Ambiguities
Users around the world face escalating network interference such as censorship, throttling, and interception, largely driven by the commoditization and growing availability of Deep Packet Inspection (DPI) devices. Once reserved for a few well-resourced nation-state actors, the ability to interfere with traffic at scale is now within reach of nearly any network operator. Despite this proliferation, our understanding of DPIs and their deployments on the Internet remains limited -- being network i…

Understanding NFTs from EIP Standards
Minfeng Qi, Qin Wang, Guangsheng Yu, Ruiqiang Li, Victor Zhou, Shiping Chen
https://arxiv.org/abs/2508.07190 https://…

Understanding NFTs from EIP Standards
We argue that the technical foundations of non-fungible tokens (NFTs) remain inadequately understood. Prior research has focused on market dynamics, user behavior, and isolated security incidents, yet systematic analysis of the standards underpinning NFT functionality is largely absent. We present the first study of NFTs through the lens of Ethereum Improvement Proposals (EIPs). We conduct a large-scale empirical analysis of 191 NFT-related EIPs and 10K+ Ethereum Magicians discussions (as of …

AutoEmpirical: LLM-Based Automated Research for Empirical Software Fault Analysis
Jiongchi Yu, Weipeng Jiang, Xiaoyu Zhang, Qiang Hu, Xiaofei Xie, Chao Shen
https://arxiv.org/abs/2510.04997

AutoEmpirical: LLM-Based Automated Research for Empirical Software Fault Analysis
Understanding software faults is essential for empirical research in software development and maintenance. However, traditional fault analysis, while valuable, typically involves multiple expert-driven steps such as collecting potential faults, filtering, and manual investigation. These processes are both labor-intensive and time-consuming, creating bottlenecks that hinder large-scale fault studies in complex yet critical software systems and slow the pace of iterative empirical research. In …

It is vital that we don't overestimate our understanding of human cognition.
Yes, there is reason to believe we have a better grasp of some things than when AI hype first appeared in the 1950s, but that better understanding is not clear, and almost certainly isn't on the basis of simple computation

Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
Wanyue Zhang, Yibin Huang, Yangbin Xu, JingJing Huang, Helu Zhi, Shuo Ren, Wang Xu, Jiajun Zhang
https://arxiv.org/abs/2509.02359

Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
Spatial understanding is essential for Multimodal Large Language Models (MLLMs) to support perception, reasoning, and planning in embodied environments. Despite recent progress, existing studies reveal that MLLMs still struggle with spatial understanding. However, existing research lacks a comprehensive and systematic evaluation of these limitations, often restricted to isolated scenarios, such as single-view or video. In this work, we present a systematic analysis of spatial understanding from…

How liveable are London's 15-minute neighbourhoods? Exploring liveability profiles and active travel patterns in London
Philyoung Jeong, Ivann Schlosser, Alexei Poliakov, Elsa Arcaute
https://arxiv.org/abs/2510.09296

How liveable are London's 15-minute neighbourhoods? Exploring liveability profiles and active travel patterns in London
Healthy and liveable neighbourhoods have increasingly been recognised as essential components of sustainable urban development. Yet, ambiguity surrounding their definition and constituent elements presents challenges in understanding and evaluating neighbourhood profiles, highlighting the need for a more detailed and systematic assessment. This research develops a composite Liveability Index for Greater London based on metrics related to the proximity, density, and diversity of POIs, along with…

Longitudinal magneto-thermal conductivity and magneto-Seebeck of itinerant antiferromagnetic BaMn$_2$Bi$_2$
Takuma Ogasawara (Beijing Academy of Quantum Information Sciences), Hailiang Xia (Institute of Physics Chinese Academy of Sciences), Khuong-Kim Huynh (Department of Chemistry, Aarhus University), Qifeng Yao (Beijing Academy of Quantum Information Sciences), Liguo Zhang (Beijing Academy of Quantum Information Sciences), Thomas L M Lane (Beijing Academy of Quantum Information Scien…

Longitudinal magneto-thermal conductivity and magneto-Seebeck of itinerant antiferromagnetic BaMn$_2$Bi$_2$
Thermal transport, generally mediated by the direct microscopic exchange of kinetic energy via lattice phonons, can also be modified by contributions from additional quasiparticles, such as electrons and magnons. However, a comprehensive understanding of the magnon influence has yet to be realized and remains an active research area. The most significant roadblock has been a lack of available materials in which these three quasiparticles can be clearly identified and quantitatively examined in …

Toward a Multi-Echelon Cyber Warfare Theory: A Meta-Game-Theoretic Paradigm for Defense and Dominance
Ya-Ting Yang, Quanyan Zhu
https://arxiv.org/abs/2509.08976 https://

Toward a Multi-Echelon Cyber Warfare Theory: A Meta-Game-Theoretic Paradigm for Defense and Dominance
Cyber warfare has become a central element of modern conflict, especially within multi-domain operations. As both a distinct and critical domain, cyber warfare requires integrating defensive and offensive technologies into coherent strategies. While prior research has emphasized isolated tactics or fragmented technologies, a holistic understanding is essential for effective resource deployment and risk mitigation. Game theory offers a unifying framework for this purpose. It not only models atta…

Towards Understanding the Shape of Representations in Protein Language Models
Kosio Beshkov, Anders Malthe-S{\o}renssen
https://arxiv.org/abs/2509.24895 https://

Towards Understanding the Shape of Representations in Protein Language Models
While protein language models (PLMs) are one of the most promising avenues of research for future de novo protein design, the way in which they transform sequences to hidden representations, as well as the information encoded in such representations is yet to be fully understood. Several works have attempted to propose interpretability tools for PLMs, but they have focused on understanding how individual sequences are transformed by such models. Therefore, the way in which PLMs transform the wh…

The 21 cm forest one-dimensional power spectrum as a probe of dark matter particles and primordial black holes
Meng-Lin Zhao, Yue Shao, Sai Wang, Xin Zhang
https://arxiv.org/abs/2509.05705

The 21 cm forest one-dimensional power spectrum as a probe of dark matter particles and primordial black holes
Understanding the nature of dark matter (DM) particles remains a pivotal challenge in modern cosmology. Current cosmological research on these phenomena primarily utilizes cosmic microwave background (CMB) observations and other late-time probes, which predominantly focus on large scales. We introduce a novel probe, the 21 cm forest signal, which can be used to investigate DM properties on small scales during the epoch of reionization, thereby addressing the gap left by other cosmological probe…

Simulation in Cybersecurity: Understanding Techniques, Applications, and Goals
Luca Serena, Gabriele D'Angelo, Stefano Ferretti, Moreno Marzolla
https://arxiv.org/abs/2508.06106

Simulation in Cybersecurity: Understanding Techniques, Applications, and Goals
Modeling and simulation are widely used in cybersecurity research to assess cyber threats, evaluate defense mechanisms, and analyze vulnerabilities. However, the diversity of application areas, the variety of cyberattacks scenarios, and the differing objectives of these simulations makes it difficult to identify methodological trends. Existing reviews often focus on specific modeling techniques or application domains, making it challenging to analyze the field as a whole. To address these limit…

Joint decoding method for controllable contextual speech recognition based on Speech LLM
Yangui Fang, Jing Peng, Yu Xi, Xu Li, Haoyu Li, Chengwei Zhang, Guohui Zhong, Kai Yu
https://arxiv.org/abs/2508.08585

Joint decoding method for controllable contextual speech recognition based on Speech LLM
Contextual speech recognition refers to the ability to identify preferences for specific content based on contextual information. Recently, leveraging the contextual understanding capabilities of Speech LLM to achieve contextual biasing by injecting contextual information through prompts have emerged as a research hotspot.However, the direct information injection method via prompts relies on the internal attention mechanism of the model, making it impossible to explicitly control the extent of …

Weakly superconducting anisotropy in 4Hb-Nb0.95Ti0.05Se2 with 1T/1H heterostructure
Fanyu Meng, Hechang Lei
https://arxiv.org/abs/2509.08716 https://arxiv.…

Weakly superconducting anisotropy in 4Hb-Nb0.95Ti0.05Se2 with 1T/1H heterostructure
Heterostructures of layered transition metal dichalcogenide (TMD) exhibit rich physical properties by the combination of strong electronic correlation effects in 1T layer and superconductivity in 1H layer. But the limited number of bulk TMD materials with such heterostructures impedes the in-depth understanding of the physical mechanisms behind these properties, as well as research on tuning of these properties. Here, we report a systematic study on physical properties of the 4Hb-Nb0.95Ti0.05Se…

Analysis of Solow-Swan model with nonlocal fractional derivative operator
MO Aibinu, KJ Duffy, S Moyo
https://arxiv.org/abs/2508.06883 https://arxiv.org/pd…

Analysis of Solow-Swan model with nonlocal fractional derivative operator
The Solow-Swan equation is a foundational model in the evolution of modern economic growth theory. It offers key insights into the long-term behaviour of capital accumulation and output. Since its inception, the model has served as a cornerstone for understanding macroeconomic dynamics and has inspired a vast body of subsequent research. However, traditional formulations of the Solow-Swan model rely on integer-order derivatives, which may not fully capture the memory and hereditary properties o…

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.

Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation Framework
Nils Dycke, Iryna Gurevych
https://arxiv.org/abs/2508.21422 htt…

Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation Framework
Large Language Models (LLMs) have great potential to accelerate and support scholarly peer review and are increasingly used as fully automatic review generators (ARGs). However, potential biases and systematic errors may pose significant risks to scientific integrity; understanding the specific capabilities and limitations of state-of-the-art ARGs is essential. We focus on a core reviewing skill that underpins high-quality peer review: detecting faulty research logic. This involves evaluating t…

Long-Term Experiences From Working with Extended Reality in the Wild
Verena Biener, Florian Jack Winston, Dieter Schmalstieg, Alexander Plopski
https://arxiv.org/abs/2509.05067 …

Long-Term Experiences From Working with Extended Reality in the Wild
Extended Reality (XR) is increasingly used as a productivity tool and recent commercial XR devices have even been specifically designed as productivity tools, or, at least, are heavily advertised for such purposes, such as the Apple Vision Pro (AVP), which has now been available for more than one year. In spite of what marketing suggests, research still lacks an understanding of the long-term usage of such devices in ecologically valid everyday settings, as most studies are conducted in very co…

Aggregate-Combine-Readout GNNs Are More Expressive Than Logic C2
Stan P Hauke, Przemys{\l}aw Andrzej Wa{\l}\k{e}ga
https://arxiv.org/abs/2508.06091 https://

Aggregate-Combine-Readout GNNs Are More Expressive Than Logic C2
In recent years, there has been growing interest in understanding the expressive power of graph neural networks (GNNs) by relating them to logical languages. This research has been been initialised by an influential result of Barceló et al. (2020), who showed that the graded modal logic (or a guarded fragment of the logic C2), characterises the logical expressiveness of aggregate-combine GNNs. As a ``challenging open problem'' they left the question whether full C2 characterises the logical ex…

Unsupervised and semi-supervised clustering methods to identify and refine participant experience levels in educational research
Julien-Pooya Weihs, Adrien Weihs, Vegard Gjerde, Helge Drange
https://arxiv.org/abs/2508.03840

Unsupervised and semi-supervised clustering methods to identify and refine participant experience levels in educational research
The progression from novice to disciplinary expert is a longstanding area of inquiry in educational research. Studies investigating such progressions have often resorted to participants' self-assessments or other qualitative indicators as a starting point to define experience. But does a participant's estimated experience coincide with metrics derived from their conceptual understanding of a discipline? Using data extracted from over 150 concept maps, we first demonstrate that disciplinary expe…

A Rapid Review Regarding the Concept of Legal Requirements in Requirements Engineering
Jukka Ruohonen
https://arxiv.org/abs/2509.06012 https://arxiv.org/pd…

A Rapid Review Regarding the Concept of Legal Requirements in Requirements Engineering
Out of a personal puzzlement, recent peer review comments, and demonstrable confusion in the existing literature, the paper presents a rapid review of the concept of legal requirements (LRs) in requirements engineering (RE) research. According to reviewing results, a normative understanding of LRs has often been present, although proper definitions and conceptual operationalizations are lacking. Some papers also see LRs as functional and others as non-functional requirements. Legal requirements…

Towards Experience-Centered AI: A Framework for Integrating Lived Experience in Design and Development
Sanjana Gautam, Mohit Chandra, Ankolika De, Tatiana Chakravorti, Girik Malik, Munmun De Choudhury
https://arxiv.org/abs/2508.06849

Towards Experience-Centered AI: A Framework for Integrating Lived Experience in Design and Development
Lived experiences fundamentally shape how individuals interact with AI systems, influencing perceptions of safety, trust, and usability. While prior research has focused on developing techniques to emulate human preferences, and proposed taxonomies to categorize risks (such as psychological harms and algorithmic biases), these efforts have provided limited systematic understanding of lived human experiences or actionable strategies for embedding them meaningfully into the AI development lifecyc…

The Telephone Game: Evaluating Semantic Drift in Unified Models
Sabbir Mollah, Rohit Gupta, Sirnam Swetha, Qingyang Liu, Ahnaf Munir, Mubarak Shah
https://arxiv.org/abs/2509.04438

The Telephone Game: Evaluating Semantic Drift in Unified Models
Employing a single, unified model (UM) for both visual understanding (image-to-text: I2T) and and visual generation (text-to-image: T2I) has opened a new direction in Visual Language Model (VLM) research. While UMs can also support broader unimodal tasks (e.g., text-to-text, image-to-image), we focus on the core cross-modal pair T2I and I2T, as consistency between understanding and generation is critical for downstream use. Existing evaluations consider these capabilities in isolation: FID and …

Dimension Reduction for Characterizing Sexual Dimorphism in Biomechanics of the Temporomandibular Joint
Sung Hee Park, Xin Zhang, Elizabeth Slate, Shuchun Sun, Hai Yao
https://arxiv.org/abs/2510.00217 …

Dimension Reduction for Characterizing Sexual Dimorphism in Biomechanics of the Temporomandibular Joint
Sexual dimorphism is a critical factor in many biological and medical research fields. In biomechanics and bioengineering, understanding sex differences is crucial for studying musculoskeletal conditions such as temporomandibular disorder (TMD). This paper focuses on the association between the craniofacial skeletal morphology and temporomandibular joint (TMJ) related masticatory muscle attachments to discern sex differences. Data were collected from 10 male and 11 female cadaver heads to inves…

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…

Alignment Without Understanding: A Message- and Conversation-Centered Approach to Understanding AI Sycophancy
Lihua Du, Xing Lyu, Lezi Xie, Bo Feng
https://arxiv.org/abs/2509.21665

Alignment Without Understanding: A Message- and Conversation-Centered Approach to Understanding AI Sycophancy
AI sycophancy is increasingly recognized as a harmful alignment, but research remains fragmented and underdeveloped at the conceptual level. This article redefines AI sycophancy as the tendency of large language models (LLMs) and other interactive AI systems to excessively and/or uncritically validate, amplify, or align with a user's assertions-whether these concern factual information, cognitive evaluations, or affective states. Within this framework, we distinguish three types of sycophancy: …

Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
Yuanchen Wu, Yubai Yuan
https://arxiv.org/abs/2509.18484 https://arxiv.org/pdf/2…

Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
Estimating causal effects on networks is important for both scientific research and practical applications. Unlike traditional settings that assume the Stable Unit Treatment Value Assumption (SUTVA), interference allows an intervention/treatment on one unit to affect the outcomes of others. Understanding both direct and spillover effects is critical in fields such as epidemiology, political science, and economics. Causal inference on networks faces two main challenges. First, causal effects are…

Crowdsourcing Star-Formation Research and the Power of Participatory Science
Grace Wolf-Chase (Planetary Science Institute), Charles Kerton (Iowa State University), Kathryn Devine (The College of Idaho), Nicholas Larose (Iowa State University), Maya Coleman (The College of Idaho)
https://arxiv.org/abs/2508.14188

Crowdsourcing Star-Formation Research and the Power of Participatory Science
We review participatory science programs that have contributed to the understanding of star formation. The Milky Way Project (MWP), one of the earliest participatory science projects launched on the Zooniverse platform, produced the largest catalog of ``bubbles'' associated with feedback from hot young stars to date, and enabled the identification of a new class of compact star-forming regions (SFRs) known as ``yellowballs'' (YBs). The analysis of YBs through their infrared colors and catalog c…

Analyzing Memory Effects in Large Language Models through the lens of Cognitive Psychology
Zhaoyang Cao, Lael Schooler, Reza Zafarani
https://arxiv.org/abs/2509.17138 https://…

Analyzing Memory Effects in Large Language Models through the lens of Cognitive Psychology
Memory, a fundamental component of human cognition, exhibits adaptive yet fallible characteristics as illustrated by Schacter's memory "sins".These cognitive phenomena have been studied extensively in psychology and neuroscience, but the extent to which artificial systems, specifically Large Language Models (LLMs), emulate these cognitive phenomena remains underexplored. This study uses human memory research as a lens for understanding LLMs and systematically investigates human memory effects i…

Excitation Gaps of Ground and Excited State Energy of the Fermi-Hubbard Model Using Variational Quantum Eigensolver
Mrinal Dev, Bikash K. Behera, Vivek Vyas, Prasanta K. Panigrahi
https://arxiv.org/abs/2508.12307

Excitation Gaps of Ground and Excited State Energy of the Fermi-Hubbard Model Using Variational Quantum Eigensolver
The Hubbard model is a challenging quantum many-body problem and serves as a benchmark for quantum computing research. Accurate computation of its ground and excited state energies is essential for understanding correlated electron systems. In this study, the ground, first, and second excited state energies of 4$\times$1 and 2$\times$2 Hubbard lattices are obtained using a newly designed ansatz circuit. The ansatz is constructed by combining concepts from the Hamiltonian Variational Ansatz (HVA…

Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
Asim Ersoy, Enes Altinisik, Husrev Taha Sencar, Kareem Darwish
https://arxiv.org/abs/2509.20957 https://

Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
Tool calling is a critical capability that allows Large Language Models (LLMs) to interact with external systems, significantly expanding their utility. However, research and resources for tool calling are predominantly English-centric, leaving a gap in our understanding of how to enable this functionality for other languages, such as Arabic. This paper investigates three key research questions: (1) the necessity of in-language (Arabic) tool-calling data versus relying on cross-lingual transfer…

Lonely Individuals Show Distinct Patterns of Social Media Engagement
Yajing Wang, Talayeh Aledavood, Juhi Kulshrestha
https://arxiv.org/abs/2510.06733 https://

Lonely Individuals Show Distinct Patterns of Social Media Engagement
Loneliness has reached epidemic proportions globally, posing serious risks to mental and physical health. As social media platforms increasingly mediate social interaction, understanding their relationship with loneliness has become urgent. While survey-based research has examined social media use and loneliness, findings remain mixed, and little is known about when and how often people engage with social media, or about whether different types of platforms are differently associated with lonel…

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…

Crystalline-to-Crystalline Phase Transition between Germanium Selenide Polymorphs with High Resistance Contrast
Joonho Kim, Kihyun Lee, Joong-Eon Jung, Han Joo Lee, Seongil Im, Kwanpyo Kim
https://arxiv.org/abs/2508.17997

Crystalline-to-Crystalline Phase Transition between Germanium Selenide Polymorphs with High Resistance Contrast
Understanding phase transitions between crystalline phases of a material is crucial for both fundamental research and potential applications such as phase-change memory. In this study, we investigate the phase transition between GeSe crystalline polymorphs induced by either global annealing at moderate temperatures or localized laser-induced heating. The highly conductive gamma-GeSe transforms into semiconducting, single-crystalline alpha-GeSe while preserving a well-aligned crystal orientation…

Temporal $k$-Core Query, Revisited
Yinyu Liu, Kaiqiang Yu, Shengxin Liu, Cheng Long, Zhaoquan Gu
https://arxiv.org/abs/2508.15238 https://arxiv.org/pdf/250…

Temporal $k$-Core Query, Revisited
Querying cohesive subgraphs in temporal graphs is essential for understanding the dynamic structure of real-world networks, such as evolving communities in social platforms, shifting hyperlink structures on the Web, and transient communication patterns in call networks. Recently, research has focused on the temporal $k$-core query, which aims to identify all $k$-cores across all possible time sub-intervals within a given query interval. The state-of-the-art algorithm OTCD mitigates redundant co…

Digital Voices of Survival: From Social Media Disclosures to Support Provisions for Domestic Violence Victims
Kanlun Wang, Zhe Fu, Wangjiaxuan Xin, Lina Zhou, Shashi Kiran Chandrappa
https://arxiv.org/abs/2509.12288

Digital Voices of Survival: From Social Media Disclosures to Support Provisions for Domestic Violence Victims
Domestic Violence (DV) is a pervasive public health problem characterized by patterns of coercive and abusive behavior within intimate relationships. With the rise of social media as a key outlet for DV victims to disclose their experiences, online self-disclosure has emerged as a critical yet underexplored avenue for support-seeking. In addition, existing research lacks a comprehensive and nuanced understanding of DV self-disclosure, support provisions, and their connections. To address these …

Linguistic Hooks: Investigating The Role of Language Triggers in Phishing Emails Targeting African Refugees and Students
Mythili Menon, Nisha Vinayaga-Sureshkanth, Alec Schon, Kaitlyn Hemberger, Murtuza Jadliwala
https://arxiv.org/abs/2509.04700

Linguistic Hooks: Investigating The Role of Language Triggers in Phishing Emails Targeting African Refugees and Students
Phishing and sophisticated email-based social engineering attacks disproportionately affect vulnerable populations, such as refugees and immigrant students. However, these groups remain understudied in cybersecurity research. This gap in understanding, coupled with their exclusion from broader security and privacy policies, increases their susceptibility to phishing and widens the digital security divide between marginalized and non-marginalized populations. To address this gap, we first conduc…

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…

Towards interpretable emotion recognition: Identifying key features with machine learning
Yacouba Kaloga, Ina Kodrasi
https://arxiv.org/abs/2508.04230 https://

Towards interpretable emotion recognition: Identifying key features with machine learning
Unsupervised methods, such as wav2vec2 and HuBERT, have achieved state-of-the-art performance in audio tasks, leading to a shift away from research on interpretable features. However, the lack of interpretability in these methods limits their applicability in critical domains like medicine, where understanding feature relevance is crucial. To better understand the features of unsupervised models, it remains critical to identify the interpretable features relevant to a given task. In this work, …

OmniDPO: A Preference Optimization Framework to Address Omni-Modal Hallucination
Junzhe Chen, Tianshu Zhang, Shiyu Huang, Yuwei Niu, Chao Sun, Rongzhou Zhang, Guanyu Zhou, Lijie Wen, Xuming Hu
https://arxiv.org/abs/2509.00723

OmniDPO: A Preference Optimization Framework to Address Omni-Modal Hallucination
Recently, Omni-modal large language models (OLLMs) have sparked a new wave of research, achieving impressive results in tasks such as audio-video understanding and real-time environment perception. However, hallucination issues still persist. Similar to the bimodal setting, the priors from the text modality tend to dominate, leading OLLMs to rely more heavily on textual cues while neglecting visual and audio information. In addition, fully multimodal scenarios introduce new challenges. Most exi…

Solar cosmic ray generation, Newtonian gravity, missing mass, dark energy, laboratory-based nuclear astrophysics, and all that
Richard Talman
https://arxiv.org/abs/2508.19296 ht…

Solar cosmic ray generation, Newtonian gravity, missing mass, dark energy, laboratory-based nuclear astrophysics, and all that
Begun as part of a promotion of accelerator-based nuclear astrophysics, research toward this goal has shifted to cosmic ray production within the solar system. This has been motivated by the high quality of data collected recently by programs such as the International Space Station (ISS), by the fundamental importance of the topic, and by the unsatisfactory state of our understanding of the actual source of cosmic rays. A ``minor'' change in the Newtonian gravitational formulation converts sola…

A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects
Johan Lin{\aa}ker, Cailean Osborne, Jennifer Ding, Ben Burtenshaw
https://arxiv.org/abs/2509.25397

A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects
The proliferation of open large language models (LLMs) is fostering a vibrant ecosystem of research and innovation in artificial intelligence (AI). However, the methods of collaboration used to develop open LLMs both before and after their public release have not yet been comprehensively studied, limiting our understanding of how open LLM projects are initiated, organized, and governed as well as what opportunities there are to foster this ecosystem even further. We address this gap through an …

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes
Zilong Lin, Zichuan Li, Xiaojing Liao, XiaoFeng Wang
https://arxiv.org/abs/2508.12622

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes
The advancement of AI technologies, particularly Large Language Models (LLMs), has transformed computing while introducing new security and privacy risks. Prior research shows that cybercriminals are increasingly leveraging uncensored LLMs (ULLMs) as backends for malicious services. Understanding these ULLMs has been hindered by the challenge of identifying them among the vast number of open-source LLMs hosted on platforms like Hugging Face. In this paper, we present the first systematic study …

Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
Lukas Wegmeth, Tobias Vente, Alan Said, Joeran Beel
https://arxiv.org/abs/2509.13001

Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
As global warming soars, the need to assess and reduce the environmental impact of recommender systems is becoming increasingly urgent. Despite this, the recommender systems community hardly understands, addresses, and evaluates the environmental impact of their work. In this study, we examine the environmental impact of recommender systems research by reproducing typical experimental pipelines. Based on our results, we provide guidelines for researchers and practitioners on how to minimize the…

A Desktop-Centric Design Space for Direct Object Examination and Visualization in Mixed-Reality Environments
Sam Johnson-Lacoss, Santiago V. Lombeyda, S. George Djorgovski
https://arxiv.org/abs/2508.05088

A Desktop-Centric Design Space for Direct Object Examination and Visualization in Mixed-Reality Environments
Mixed reality (MR) environments are bound to become ubiquitous as MR technology becomes lighter, higher resolution, more affordable, and overall becomes a seamless extension of our current work and living spaces. For research scientists and clinicians focused on understanding 3D phenomena or patient pathologies within the context of the larger human anatomy, that means a necessary evolution of their workstations currently only utilizing 2D interfaces for everyday communication, logistics and da…

The Impact of 2D Segmentation Backbones on Point Cloud Predictions Using 4D Radar
William L. Muckelroy III, Mohammed Alsakabi, John M. Dolan, Ozan K. Tonguz
https://arxiv.org/abs/2509.19644

The Impact of 2D Segmentation Backbones on Point Cloud Predictions Using 4D Radar
LiDAR's dense, sharp point cloud (PC) representations of the surrounding environment enable accurate perception and significantly improve road safety by offering greater scene awareness and understanding. However, LiDAR's high cost continues to restrict the broad adoption of high-level Autonomous Driving (AD) systems in commercially available vehicles. Prior research has shown progress towards circumventing the need for LiDAR by training a neural network, using LiDAR point clouds as ground trut…

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…

Landmarks, Monuments, and Beacons: Understanding Generative Calls to Action
Victoire Herv\'e, Henrik Warpefelt, Christoph Salge
https://arxiv.org/abs/2509.19030 https://

Landmarks, Monuments, and Beacons: Understanding Generative Calls to Action
Algorithmic evaluation of procedurally generated content struggles to find metrics that align with human experience, particularly for composite artefacts. Automatic decomposition as a possible solution requires concepts that meet a range of properties. To this end, drawing on Games Studies and Game AI research, we introduce the nested concepts of \textit{Landmarks}, \textit{Monuments}, and \textit{Beacons}. These concepts are based on the artefact's perceivability, evocativeness, and Call to Ac…

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…

A Study of Privacy-preserving Language Modeling Approaches
Pritilata Saha, Abhirup Sinha
https://arxiv.org/abs/2508.15421 https://arxiv.org/pdf/2508.15421

A Study of Privacy-preserving Language Modeling Approaches
Recent developments in language modeling have increased their use in various applications and domains. Language models, often trained on sensitive data, can memorize and disclose this information during privacy attacks, raising concerns about protecting individuals' privacy rights. Preserving privacy in language models has become a crucial area of research, as privacy is one of the fundamental human rights. Despite its significance, understanding of how much privacy risk these language models p…

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…

Formal Verification of Physical Layer Security Protocols for Next-Generation Communication Networks
Kangfeng Ye, Roberto Metere, Jim Woodcock, Poonam Yadav
https://arxiv.org/abs/2508.19430

Formal Verification of Physical Layer Security Protocols for Next-Generation Communication Networks
Formal verification is crucial for ensuring the robustness of security protocols against adversarial attacks. The Needham-Schroeder protocol, a foundational authentication mechanism, has been extensively studied, including its integration with Physical Layer Security (PLS) techniques such as watermarking and jamming. Recent research has used ProVerif to verify these mechanisms in terms of secrecy. However, the ProVerif-based approach limits the ability to improve understanding of security beyon…

Ketto and the Science of Giving: A Data-Driven Investigation of Crowdfunding for India
Karuna Chandra, Akshay Menon, Lydia Manikonda, Ponnurangam Kumaraguru
https://arxiv.org/abs/2509.12616

Ketto and the Science of Giving: A Data-Driven Investigation of Crowdfunding for India
The main goal of this paper is to investigate an up and coming crowdfunding platform used to raise funds for social causes in India called Ketto. Despite the growing usage of this platform, there is insufficient understanding in terms of why users choose this platform when there are other popular platforms such as GoFundMe. Using a dataset comprising of 119,493 Ketto campaigns, our research conducts an in-depth investigation into different aspects of how the campaigns on Ketto work with a speci…

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…

REMA: A Unified Reasoning Manifold Framework for Interpreting Large Language Model
Bo Li, Guanzhi Deng, Ronghao Chen, Junrong Yue, Shuo Zhang, Qinghua Zhao, Linqi Song, Lijie Wen
https://arxiv.org/abs/2509.22518

REMA: A Unified Reasoning Manifold Framework for Interpreting Large Language Model
Understanding how Large Language Models (LLMs) perform complex reasoning and their failure mechanisms is a challenge in interpretability research. To provide a measurable geometric analysis perspective, we define the concept of the Reasoning Manifold, a latent low-dimensional geometric structure formed by the internal representations corresponding to all correctly reasoned generations. This structure can be conceptualized as the embodiment of the effective thinking paths that the model has lear…

Evaluating the Effectiveness and Scalability of LLM-Based Data Augmentation for Retrieval
Pranjal A. Chitale, Bishal Santra, Yashoteja Prabhu, Amit Sharma
https://arxiv.org/abs/2509.16442

Evaluating the Effectiveness and Scalability of LLM-Based Data Augmentation for Retrieval
Compact dual-encoder models are widely used for retrieval owing to their efficiency and scalability. However, such models often underperform compared to their Large Language Model (LLM)-based retrieval counterparts, likely due to their limited world knowledge. While LLM-based data augmentation has been proposed as a strategy to bridge this performance gap, there is insufficient understanding of its effectiveness and scalability to real-world retrieval problems. Existing research does not system…

When Large Language Models Meet UAVs: How Far Are We?
Yihua Chen, Xingle Que, Jiashuo Zhang, Ting Chen, Guangshun Li, Jiachi Chen
https://arxiv.org/abs/2509.12795 https://

When Large Language Models Meet UAVs: How Far Are We?
The integration of unmanned aerial vehicles (UAVs) and large language models (LLMs) has emerged as a research direction of growing interest, with the potential to address challenges in autonomous decision-making, human-UAV interaction, and real-time adaptability. However, existing studies have remained largely in preliminary exploration with a limited understanding of real-world practice, risking a misalignment between academic research and practical needs and hindering the translation of resul…

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…

Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction
Matus Krajcovic, Peter Demcak, Eduard Kuric
https://arxiv.org/abs/2508.02376

Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction
Embodied conversational agents (ECAs) are increasingly more realistic and capable of dynamic conversations. In online surveys, anthropomorphic agents could help address issues like careless responding and satisficing, which originate from the lack of personal engagement and perceived accountability. However, there is a lack of understanding of how ECAs in user experience research may affect participant engagement, satisfaction, and the quality of responses. As a proof of concept, we propose an …

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…

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…

Beyond Community Notes: A Framework for Understanding and Building Crowdsourced Context Systems
Travis Lloyd, Tung Nguyen, Karen Levy, Mor Naaman
https://arxiv.org/abs/2509.15434

Beyond Community Notes: A Framework for Understanding and Building Crowdsourced Context Systems
Social media platforms are increasingly developing features that display crowdsourced context alongside posts, modeled after X's Community Notes. These systems, which we term Crowdsourced Context Systems (CCS), have the potential to reshape our information ecosystem as major platforms embrace them as alternatives to top-down fact-checking. To deeply understand the features and implications of such systems, we perform a systematic literature review of existing CCS research and analyze several re…

Perspectives, Needs and Challenges for Sustainable Software Engineering Teams: A FinServ Case Study
Satwik Ghanta, Peggy Gregory, Gul Calikli
https://arxiv.org/abs/2509.12466 ht…

Perspectives, Needs and Challenges for Sustainable Software Engineering Teams: A FinServ Case Study
Sustainable Software Engineering (SSE) is slowly becoming an industry need for reasons including reputation enhancement, improved profits and more efficient practices. However, SSE has many definitions, and this is a challenge for organisations trying to build a common and broadly agreed understanding of the term. Although much research effort has gone into identifying general SSE practices, there is a gap in understanding the sustainability needs of specific organisational contexts, such as fi…

Root Cause Analysis of Hydrogen Bond Separation in Spatio-Temporal Molecular Dynamics using Causal Models
Rahmat K. Adesunkanmi, Ashfaq Khokhar, Goce Trajcevski, Sohail Murad
https://arxiv.org/abs/2508.12500

Root Cause Analysis of Hydrogen Bond Separation in Spatio-Temporal Molecular Dynamics using Causal Models
Molecular dynamics simulations (MDS) face challenges, including resource-heavy computations and the need to manually scan outputs to detect "interesting events," such as the formation and persistence of hydrogen bonds between atoms of different molecules. A critical research gap lies in identifying the underlying causes of hydrogen bond formation and separation -understanding which interactions or prior events contribute to their emergence over time. With this challenge in mind, we propose leve…

"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…

Understanding Accessibility Needs of Blind Authors on CMS-Based Websites
Guillermo Vera-Amaro, Jos\'e Rafael Rojano-C\'aceres
https://arxiv.org/abs/2508.15045 https://…

Understanding Accessibility Needs of Blind Authors on CMS-Based Websites
This paper addresses the limited attention given to blind users as content creators in Content Management Systems (CMS), a gap that remains under-explored in web accessibility research. For blind authors, effective interaction with CMS platforms requires more than technical compliance; it demands interfaces designed with semantic clarity, predictable navigation, and meaningful feedback for screen reader users. This study investigates the accessibility barriers blind users face when performing k…

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…

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…

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 …

Funding the Frontier: Visualizing the Broad Impact of Science and Science Funding
Yifang Wang, Yifan Qian, Xiaoyu Qi, Yian Yin, Shengqi Dang, Ziqing Qian, Benjamin F. Jones, Nan Cao, Dashun Wang
https://arxiv.org/abs/2509.16323

Funding the Frontier: Visualizing the Broad Impact of Science and Science Funding
Understanding the broad impact of science and science funding is critical to ensuring that science investments and policies align with societal needs. Existing research links science funding to the output of scientific publications but largely leaves out the downstream uses of science and the myriad ways in which investing in science may impact human society. As funders seek to allocate scarce funding resources across a complex research landscape, there is an urgent need for informative and tra…

Impact Assessment Card: Communicating Risks and Benefits of AI Uses
Edyta Bogucka, Marios Constantinides, Sanja \v{S}\'cepanovi\'c, Daniele Quercia
https://arxiv.org/abs/2508.18919

Impact Assessment Card: Communicating Risks and Benefits of AI Uses
Communicating the risks and benefits of AI is important for regulation and public understanding. Yet current methods such as technical reports often exclude people without technical expertise. Drawing on HCI research, we developed an Impact Assessment Card to present this information more clearly. We held three focus groups with a total of 12 participants who helped identify design requirements and create early versions of the card. We then tested a refined version in an online study with 235 p…