Tootfinder

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…

Tactile Robotics: An Outlook
Shan Luo, Nathan F. Lepora, Wenzhen Yuan, Kaspar Althoefer, Gordon Cheng, Ravinder Dahiya
https://arxiv.org/abs/2508.11261 https://

Tactile Robotics: An Outlook
Robotics research has long sought to give robots the ability to perceive the physical world through touch in an analogous manner to many biological systems. Developing such tactile capabilities is important for numerous emerging applications that require robots to co-exist and interact closely with humans. Consequently, there has been growing interest in tactile sensing, leading to the development of various technologies, including piezoresistive and piezoelectric sensors, capacitive sensors, m…

Perception Before Reasoning: Two-Stage Reinforcement Learning for Visual Reasoning in Vision-Language Models
Yan Chen, Long Li, Teng Xi, Long Zeng, Jingdong Wang
https://arxiv.org/abs/2509.13031

Perception Before Reasoning: Two-Stage Reinforcement Learning for Visual Reasoning in Vision-Language Models
Reinforcement learning (RL) has proven highly effective in eliciting the reasoning capabilities of large language models (LLMs). Inspired by this success, recent studies have explored applying similar techniques to vision-language models (VLMs), aiming to enhance their reasoning performance. However, directly transplanting RL methods from LLMs to VLMs is suboptimal, as the tasks faced by VLMs are inherently more complex. Specifically, VLMs must first accurately perceive and understand visual in…

Surpassing Carnot efficiency with relativistic motion
Dimitris Moustos, Obinna Abah
https://arxiv.org/abs/2508.11554 https://arxiv.org/pdf/2508.11554

Surpassing Carnot efficiency with relativistic motion
Relativistic thermal devices offer a unique platform for understanding the interplay between motion, quantum fields, and thermodynamics, revealing phenomena inaccessible to stationary systems. We consider a two-qubit SWAP heat engine whose working medium consists of inertially moving Unruh-DeWitt qubit detectors, each coupled to a scalar quantum field in thermal equilibrium at a distinct temperature. Relativistic motion causes the qubits to perceive frequency-dependent effective temperatures th…

The colors we perceive follow consistent brain codes in all humans #neuroscience
Large-scale color biases in the retinotopic functional architecture are region specific an…

The colors we perceive follow consistent brain codes in all humans

How Easterners and Westerners perceive ADHD differently
Xing-Chan Lin (Department of Computer Science,Psychology, University of Cyprus,Open University of Cyprus)
https://arxiv.org/abs/2509.10549

How Easterners and Westerners perceive ADHD differently
Attention Deficit Hyperactivity Disorder has traditionally been conceptualized as a neurodevelopmental condition associated with deficits such as inattention, impulsivity, and poor time management. Such perspectives often emphasize pathology and functional limitations. More recent scholarship, however, has begun to reconceptualize ADHD by identifying potential adaptive characteristics, including heightened energy, periods of hyperfocus, and advanced cognitive flexibility. A growing body of qual…

ExploreVLM: Closed-Loop Robot Exploration Task Planning with Vision-Language Models
Zhichen Lou, Kechun Xu, Zhongxiang Zhou, Rong Xiong
https://arxiv.org/abs/2508.11918 https://…

ExploreVLM: Closed-Loop Robot Exploration Task Planning with Vision-Language Models
The advancement of embodied intelligence is accelerating the integration of robots into daily life as human assistants. This evolution requires robots to not only interpret high-level instructions and plan tasks but also perceive and adapt within dynamic environments. Vision-Language Models (VLMs) present a promising solution by combining visual understanding and language reasoning. However, existing VLM-based methods struggle with interactive exploration, accurate perception, and real-time pla…

Coarse Addition and the St. Petersburg Paradox: A Heuristic Perspective
Takashi Izumo
https://arxiv.org/abs/2507.12475 https://arxiv.…

Coarse Addition and the St. Petersburg Paradox: A Heuristic Perspective
The St. Petersburg paradox presents a longstanding challenge in decision theory. It describes a game whose expected value is infinite, yet for which no rational finite stake can be determined. Traditional solutions introduce auxiliary assumptions, such as diminishing marginal utility, temporal discounting, or extended number systems. These methods often involve mathematical refinements that may not correspond to how people actually perceive or process numerical information. This paper explores …

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Jiayue Melissa Shi, Dong Whi Yoo, Keran Wang, Violeta J. Rodriguez, Ravi Karkar, Koustuv Saha
https://arxiv.org/abs/2506.15047

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Family caregivers of individuals with Alzheimer's Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that place them at heightened risk for stress, anxiety, and depression. Although recent advances in generative AI -- particularly large language models (LLMs) -- offer new opportunities to support mental health, little is known about how caregivers perceive and engage with such technologies. To address this gap, we developed Carey, a GPT-4o-based chatbot …

Security Debt in Practice: Nuanced Insights from Practitioners
Chaima Boufaied, Taher Ghaleb, Zainab Masood
https://arxiv.org/abs/2507.11362 https://

Security Debt in Practice: Nuanced Insights from Practitioners
With the increasing reliance on software and automation nowadays, tight deadlines, limited resources, and prioritization of functionality over security can lead to insecure coding practices. When not handled properly, these constraints cause unaddressed security vulnerabilities to accumulate over time, forming Security Debts (SDs). Despite their critical importance, there is limited empirical evidence on how software practitioners perceive, manage, and communicate SDs in real-world settings. In…

The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models
Yike Shi (Diane), Qing Xiao (Diane), Qing (Diane), Hu, Hong Shen, Hua Shen
https://arxiv.org/abs/2509.10830

The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models
Large language models can influence users through conversation, creating new forms of dark patterns that differ from traditional UX dark patterns. We define LLM dark patterns as manipulative or deceptive behaviors enacted in dialogue. Drawing on prior work and AI incident reports, we outline a diverse set of categories with real-world examples. Using them, we conducted a scenario-based study where participants (N=34) compared manipulative and neutral LLM responses. Our results reveal that recog…

A Survey of Cognitive Distortion Detection and Classification in NLP
Archie Sage, Jeroen Keppens, Helen Yannakoudakis
https://arxiv.org/abs/2508.09878 https://

A Survey of Cognitive Distortion Detection and Classification in NLP
As interest grows in the application of natural language processing (NLP) techniques to mental health, a growing body of work explores the automatic detection and classification of cognitive distortions (CDs). CDs are habitual patterns of negatively biased or flawed thinking that distort how people perceive events, judge themselves, and react to the world around them. Identifying and addressing them is an important part of therapy. Despite its momentum, the field remains fragmented, with incons…

Some of the country’s largest law firms have declined to represent clients challenging the Trump administration,
more than a dozen attorneys and nonprofit leaders told ProPublica,
while others have sought to avoid any clients that Trump might perceive as his enemies.
That includes both clients willing to pay the firms’ steep rates,
and those who receive free representation.
Big Law firms are also refusing to take on legal work involving environmental protectio…

Trump’s War on Big Law Means It’s Harder to Challenge the Administration
Some of America’s largest law firms are refusing to take pro bono and paid legal work from groups that seek to hold the government to account on issues like environmental protection, LGBTQ+ rights and police accountability.

COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation
Pakizar Shamoi, Nuray Toganas, Muragul Muratbekova, Elnara Kadyrgali, Adilet Yerkin, Ayan Igali, Malika Ziyada, Ayana Adilova, Aron Karatayev, Yerdauit Torekhan
https://arxiv.org/abs/2507.11488

COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation
Colors are omnipresent in today's world and play a vital role in how humans perceive and interact with their surroundings. However, it is challenging for computers to imitate human color perception. This paper introduces the Human Perception-Based Fuzzy Color Model, COLIBRI (Color Linguistic-Based Representation and Interpretation), designed to bridge the gap between computational color representations and human visual perception. The proposed model uses fuzzy sets and logic to create a framewo…

😣 Effortful pastimes are perceived as more meaningful than those requiring minimal effort, study finds
https://medicalxpress.com/news/2025-08-effortful-pastimes-meaningful-requiring-minimal.html

Effortful pastimes are perceived as more meaningful than those requiring minimal effort, study finds
Past studies suggest that how people spend their time outside of work or academic endeavors can contribute to their overall well-being and life satisfaction. Yet how humans perceive different leisure activities that they engage in and the extent to which they feel that these activities contribute to their life's purpose has not yet been extensively investigated.

caring how others perceive u is natural, important and has generally made things better for me, not worse. and ur all still doing it in ur niche online dgaf groups in a less deliberate and self-aware way that contributes to hurt

Perceive-Sample-Compress: Towards Real-Time 3D Gaussian Splatting
Zijian Wang, Beizhen Zhao, Hao Wang
https://arxiv.org/abs/2508.04965 https://arxiv.org/pd…

Perceive-Sample-Compress: Towards Real-Time 3D Gaussian Splatting
Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated remarkable capabilities in real-time and photorealistic novel view synthesis. However, traditional 3DGS representations often struggle with large-scale scene management and efficient storage, particularly when dealing with complex environments or limited computational resources. To address these limitations, we introduce a novel perceive-sample-compress framework for 3D Gaussian Splatting. Specifically, we propose a scene percept…

Embodied Navigation Foundation Model
Jiazhao Zhang, Anqi Li, Yunpeng Qi, Minghan Li, Jiahang Liu, Shaoan Wang, Haoran Liu, Gengze Zhou, Yuze Wu, Xingxing Li, Yuxin Fan, Wenjun Li, Zhibo Chen, Fei Gao, Qi Wu, Zhizheng Zhang, He Wang
https://arxiv.org/abs/2509.12129

Embodied Navigation Foundation Model
Navigation is a fundamental capability in embodied AI, representing the intelligence required to perceive and interact within physical environments following language instructions. Despite significant progress in large Vision-Language Models (VLMs), which exhibit remarkable zero-shot performance on general vision-language tasks, their generalization ability in embodied navigation remains largely confined to narrow task settings and embodiment-specific architectures. In this work, we introduce a…

that A-list web sites aggressively pop a geolocation permission prompt for absolutely no good reason has done a huge disservice to how folks perceive privacy on the web

Safety of Embodied Navigation: A Survey
Zixia Wang, Jia Hu, Ronghui Mu
https://arxiv.org/abs/2508.05855 https://arxiv.org/pdf/2508.05855

Safety of Embodied Navigation: A Survey
As large language models (LLMs) continue to advance and gain influence, the development of embodied AI has accelerated, drawing significant attention, particularly in navigation scenarios. Embodied navigation requires an agent to perceive, interact with, and adapt to its environment while moving toward a specified target in unfamiliar settings. However, the integration of embodied navigation into critical applications raises substantial safety concerns. Given their deployment in dynamic, real-w…

From Perception to Protection: A Developer-Centered Study of Security and Privacy Threats in Extended Reality (XR)
Kunlin Cai, Jinghuai Zhang, Ying Li, Zhiyuan Wang, Xun Chen, Tianshi Li, Yuan Tian
https://arxiv.org/abs/2509.06368

From Perception to Protection: A Developer-Centered Study of Security and Privacy Threats in Extended Reality (XR)
The immersive nature of XR introduces a fundamentally different set of security and privacy (S&P) challenges due to the unprecedented user interactions and data collection that traditional paradigms struggle to mitigate. As the primary architects of XR applications, developers play a critical role in addressing novel threats. However, to effectively support developers, we must first understand how they perceive and respond to different threats. Despite the growing importance of this issue, ther…

Understanding support for AI regulation: A Bayesian network perspective
Andrea Cremaschi, Dae-Jin Lee, Manuele Leonelli
https://arxiv.org/abs/2507.05866 ht…

Understanding support for AI regulation: A Bayesian network perspective
As artificial intelligence (AI) becomes increasingly embedded in public and private life, understanding how citizens perceive its risks, benefits, and regulatory needs is essential. To inform ongoing regulatory efforts such as the European Union's proposed AI Act, this study models public attitudes using Bayesian networks learned from the nationally representative 2023 German survey Current Questions on AI. The survey includes variables on AI interest, exposure, perceived threats and opportunit…

Sonnet 015 - XV
When I consider every thing that grows
Holds in perfection but a little moment,
That this huge stage presenteth nought but shows
Whereon the stars in secret influence comment;
When I perceive that men as plants increase,
Cheered and checked even by the self-same sky,
Vaunt in their youthful sap, at height decrease,
And wear their brave state out of memory;
Then the conceit of this inconstant stay
Sets you most rich i…

A few days ago I got very excited when seeing a link for a minimal emacs setup.
Why? My Emacs setup is not minimal. And I always think it is not well enough organized.
I stopped looking at this minimal setup project very early. I have realized that I’d have to use and learn yet another set of settings that this would translate into Emacs options.
I perceive this extra indirection layer as an added complexity and distancing myself from understanding Emacs.

I've just saw a van painted to make an illusion of being broken in half. Like, there's a black crack painted in the middle, all horizontal lines are tilted and there's even a slight black triangle painted on top to enhance the impression. I found it really hard to perceive its true shape.

An Exploratory Study on AI-driven Visualisation Techniques on Decision Making in Extended Reality
Ze Dong, Binyang Han, Jingjing Zhang, Ruoyu Wen, Barrett Ens, Adrian Clark, Tham Piumsomboon
https://arxiv.org/abs/2507.10981

An Exploratory Study on AI-driven Visualisation Techniques on Decision Making in Extended Reality
The integration of extended reality (XR) with artificial intelligence (AI) introduces a new paradigm for user interaction, enabling AI to perceive user intent, stimulate the senses, and influence decision-making. We explored the impact of four AI-driven visualisation techniques -- `Inform,' `Nudge,' `Recommend,' and `Instruct' -- on user decision-making in XR using the Meta Quest Pro. To test these techniques, we used a pre-recorded 360-degree video of a supermarket, overlaying each technique t…

A Distributed Consensus Algorithm for Autonomous Vehicles Deciding Entering Orders on Intesections without Traffic Signals
Younjeong Lee, Young Yoon
https://arxiv.org/abs/2507.03486

A Distributed Consensus Algorithm for Autonomous Vehicles Deciding Entering Orders on Intesections without Traffic Signals
We propose a methodology for connected autonomous vehicles (CAVs) to determine their passing priority at unsignalized intersections where they coexist with human-driven vehicles (HVs). Assuming that CAVs can perceive the entry order of surrounding vehicles using computer vision technology and are capable of avoiding collisions, we introduce a voting-based distributed consensus algorithm inspired by Raft to resolve tie-breaking among simultaneously arriving CAVs. The algorithm is structured arou…

🤨 Study of adolescents shows that the urban environment influences their sense of justice and trust in institutions
https://phys.org/news/2025-06-adolescents-urban-environment-justice.html

Study of adolescents shows that the urban environment influences their sense of justice and trust in institutions
A study of young people in the city of São Paulo, Brazil, reveals that adolescents living in neighborhoods with high rates of violence and poor infrastructure tend to perceive the world as being more unfair for them than for others.

Bi-Level Game-Theoretic Planning of Cyber Deception for Cognitive Arbitrage
Ya-Ting Yang, Quanyan Zhu
https://arxiv.org/abs/2509.05498 https://arxiv.org/pd…

Bi-Level Game-Theoretic Planning of Cyber Deception for Cognitive Arbitrage
Cognitive vulnerabilities shape human decision-making and arise primarily from two sources: (1) cognitive capabilities, which include disparities in knowledge, education, expertise, or access to information, and (2) cognitive biases, such as rational inattention, confirmation bias, and base rate neglect, which influence how individuals perceive and process information. Exploiting these vulnerabilities allows an entity with superior cognitive awareness to gain a strategic advantage, a concept re…

Deep Learning Based Dynamic Environment Reconstruction for Vehicular ISAC Scenarios
Junzhe Song, Ruisi He, Mi Yang, Zhengyu Zhang, Bingcheng Liu, Jiahui Han, Haoxiang Zhang, Bo Ai
https://arxiv.org/abs/2508.05226

Deep Learning Based Dynamic Environment Reconstruction for Vehicular ISAC Scenarios
Integrated Sensing and Communication (ISAC) technology plays a critical role in future intelligent transportation systems, by enabling vehicles to perceive and reconstruct the surrounding environment through reuse of wireless signals, thereby reducing or even eliminating the need for additional sensors such as LiDAR or radar. However, existing ISAC based reconstruction methods often lack the ability to track dynamic scenes with sufficient accuracy and temporal consistency, limiting the real wor…

Reinforcement Learning in Vision: A Survey
Weijia Wu, Chen Gao, Joya Chen, Kevin Qinghong Lin, Qingwei Meng, Yiming Zhang, Yuke Qiu, Hong Zhou, Mike Zheng Shou
https://arxiv.org/abs/2508.08189

Reinforcement Learning in Vision: A Survey
Recent advances at the intersection of reinforcement learning (RL) and visual intelligence have enabled agents that not only perceive complex visual scenes but also reason, generate, and act within them. This survey offers a critical and up-to-date synthesis of the field. We first formalize visual RL problems and trace the evolution of policy-optimization strategies from RLHF to verifiable reward paradigms, and from Proximal Policy Optimization to Group Relative Policy Optimization. We then org…

@… Q: Do you need full bells and whistles (like locking, ACLs, device files, sockets), or just plain files as a user would perceive them?

Exploring Gain-Doped-Waveguide-Synapse for Neuromorphic Applications: A Pulsed Pump-Signal Approach
Robert Otupiri, Ripalta Stabile
https://arxiv.org/abs/2507.05931

Exploring Gain-Doped-Waveguide-Synapse for Neuromorphic Applications: A Pulsed Pump-Signal Approach
Neuromorphic computing promises to transform AI systems by enabling them to perceive, respond to, and adapt swiftly and accurately to dynamic data and user interactions. However, traditional silicon-based and hybrid electronic technologies for artificial neurons constrain neuromorphic processors in terms of flexibility, scalability, and energy efficiency. In this study, we pioneer the use of Doped-Gain-Layer-on-Waveguide-Synapses for bio-inspired neurons, utilizing a pulsed pump-signal mechanis…

Can Contextualized Physics Problems Enhance Student Motivation? Uncovering a Student-Teacher Perception Gap through a Large-Scale Survey
Yajun Wei, Xinting Peng, Yi Zhong, Feipeng Pi, Yanfang Zhai, Lei Bao
https://arxiv.org/abs/2507.05586

Can Contextualized Physics Problems Enhance Student Motivation? Uncovering a Student-Teacher Perception Gap through a Large-Scale Survey
Embedding physics problems unreal-world settings, here termed contextualized physics problems (CPP), is widely believed to foster students' interest, motivation, and learning. However, firm evidence for this claim remains scarce. To explore this issue, we surveyed 868 secondary students and 154 teachers to examine their attitudes toward CPP and investigate whether students and teachers perceive these problems as promoting student interest and motivation in learning physics. The findings reveal …

Merging Bodies, Dividing Conflict: Body-Swapping in Mixed Reality Increases Closeness Yet Weakens the Joint Simon Effect
Yuan He, Brendan Rooney, Rachel McDonnell
https://arxiv.org/abs/2509.09815

Merging Bodies, Dividing Conflict: Body-Swapping in Mixed Reality Increases Closeness Yet Weakens the Joint Simon Effect
Mixed Reality (MR) presents novel opportunities to investigate how individuals perceive themselves and others during shared, augmented experiences within a common physical environment. Previous research has demonstrated that users can embody avatars in MR, temporarily extending their sense of self. However, there has been limited exploration of body-swapping, a condition in which two individuals simultaneously inhabit each other's avatars, and its potential effects on social interaction in imme…

Disclosing Generative AI Use in Digital Humanities Research
Rongqian Ma, Xuhan Zhang, Adrian Wisnicki
https://arxiv.org/abs/2507.03216 https://

Disclosing Generative AI Use in Digital Humanities Research
This survey study investigates how digital humanists perceive and approach generative AI disclosure in research. The results indicate that while digital humanities scholars acknowledge the importance of disclosing GenAI use, the actual rate of disclosure in research practice remains low. Respondents differ in their views on which activities most require disclosure and on the most appropriate methods for doing so. Most also believe that safeguards for AI disclosure should be established through …

FlexNGIA 2.0: Redesigning the Internet with Agentic AI - Protocols, Services, and Traffic Engineering Designed, Deployed, and Managed by AI
Mohamed Faten Zhani, Younes Korbi, Yamen Mkadem
https://arxiv.org/abs/2509.02124

FlexNGIA 2.0: Redesigning the Internet with Agentic AI - Protocols, Services, and Traffic Engineering Designed, Deployed, and Managed by AI
The escalating demands of immersive communications, alongside advances in network softwarization and AI-driven cognition and generative reasoning, create a pivotal opportunity to rethink and reshape the future Internet. In this context, we introduce in this paper, FlexNGIA 2.0, an Agentic AI-driven Internet architecture that leverages LLM-based AI agents to autonomously orchestrate, configure, and evolve the network. These agents can, at runtime, perceive, reason, coordinate among themselves to…

A Multidimensional AI-powered Framework for Analyzing Tourist Perception in Historic Urban Quarters: A Case Study in Shanghai
Kaizhen Tan, Yufan Wu, Yuxuan Liu, Haoran Zeng
https://arxiv.org/abs/2509.03830

A Multidimensional AI-powered Framework for Analyzing Tourist Perception in Historic Urban Quarters: A Case Study in Shanghai
Historic urban quarters play a vital role in preserving cultural heritage while serving as vibrant spaces for tourism and everyday life. Understanding how tourists perceive these environments is essential for sustainable, human-centered urban planning. This study proposes a multidimensional AI-powered framework for analyzing tourist perception in historic urban quarters using multimodal data from social media. Applied to twelve historic quarters in central Shanghai, the framework integrates foc…

Touch Speaks, Sound Feels: A Multimodal Approach to Affective and Social Touch from Robots to Humans
Qiaoqiao Ren, Tony Belpaeme
https://arxiv.org/abs/2508.07839 https://…

Touch Speaks, Sound Feels: A Multimodal Approach to Affective and Social Touch from Robots to Humans
Affective tactile interaction constitutes a fundamental component of human communication. In natural human-human encounters, touch is seldom experienced in isolation; rather, it is inherently multisensory. Individuals not only perceive the physical sensation of touch but also register the accompanying auditory cues generated through contact. The integration of haptic and auditory information forms a rich and nuanced channel for emotional expression. While extensive research has examined how rob…

Conical singularity in spacetimes with NUT is observer-dependent
Ivan Kol\'a\v{r}, Pavel Krtou\v{s}, Maciej Ossowski
https://arxiv.org/abs/2507.21238 https://

Conical singularity in spacetimes with NUT is observer-dependent
We discuss the issue of defining and measuring conical deficits (conicity) in spacetimes with the torsion singularity such as the Misner string in Taub--NUT spacetime. We propose a geometric definition that generalizes the standard notion of conicity to stationary axially symmetric spacetimes with torsion singularity, where the conical deficit becomes observer-dependent -- it depends on the choice of a timelike Killing vector. This implies the existence of observers who perceive no conical sing…

Sync-TVA: A Graph-Attention Framework for Multimodal Emotion Recognition with Cross-Modal Fusion
Zeyu Deng, Yanhui Lu, Jiashu Liao, Shuang Wu, Chongfeng Wei
https://arxiv.org/abs/2507.21395

Sync-TVA: A Graph-Attention Framework for Multimodal Emotion Recognition with Cross-Modal Fusion
Multimodal emotion recognition (MER) is crucial for enabling emotionally intelligent systems that perceive and respond to human emotions. However, existing methods suffer from limited cross-modal interaction and imbalanced contributions across modalities. To address these issues, we propose Sync-TVA, an end-to-end graph-attention framework featuring modality-specific dynamic enhancement and structured cross-modal fusion. Our design incorporates a dynamic enhancement module for each modality and…

End-to-End Humanoid Robot Safe and Comfortable Locomotion Policy
Zifan Wang, Xun Yang, Jianzhuang Zhao, Jiaming Zhou, Teli Ma, Ziyao Gao, Arash Ajoudani, Junwei Liang
https://arxiv.org/abs/2508.07611

End-to-End Humanoid Robot Safe and Comfortable Locomotion Policy
The deployment of humanoid robots in unstructured, human-centric environments requires navigation capabilities that extend beyond simple locomotion to include robust perception, provable safety, and socially aware behavior. Current reinforcement learning approaches are often limited by blind controllers that lack environmental awareness or by vision-based systems that fail to perceive complex 3D obstacles. In this work, we present an end-to-end locomotion policy that directly maps raw, spatio-t…

Uncanny or Not? Perceptions of AI-Generated Faces in Autism
Gabriella Waters
https://arxiv.org/abs/2507.08230 https://arxiv.org/pdf/2…

Uncanny or Not? Perceptions of AI-Generated Faces in Autism
As artificial intelligence (AI) systems become increasingly sophisticated at generating synthetic human faces, understanding how these images are perceived across diverse populations is important. This study investigates how autistic individuals/individuals with autism perceive AI-generated faces, focusing on the uncanny valley effect. Using a qualitative approach, we analyzed discussions from the r/autism community on Reddit to explore how autistic participants/participants with autism describ…

Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse
Paulina DeVito, Akhil Vallala, Sean Mcmahon, Yaroslav Hinda, Benjamin Thaw, Hanqi Zhuang, Hari Kalva
https://arxiv.org/abs/2506.16412

Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse
Generative AI (GAI) technologies are quickly reshaping the educational landscape. As adoption accelerates, understanding how students and educators perceive these tools is essential. This study presents one of the most comprehensive analyses to date of stakeholder discourse dynamics on GAI in education using social media data. Our dataset includes 1,199 Reddit posts and 13,959 corresponding top-level comments. We apply sentiment analysis, topic modeling, and author classification. To support th…

Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy
Jordan Peper, Yan Miao, Sayan Mitra, Ivan Ruchkin
https://arxiv.org/abs/2508.14181 https://

Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy
Precise and comprehensive situational awareness is a critical capability of modern autonomous systems. Deep neural networks that perceive task-critical details from rich sensory signals have become ubiquitous; however, their black-box behavior and sensitivity to environmental uncertainty and distribution shifts make them challenging to verify formally. Abstraction-based verification techniques for vision-based autonomy produce safety guarantees contingent on rigid assumptions, such as bounded e…

PG-Agent: An Agent Powered by Page Graph
Weizhi Chen, Ziwei Wang, Leyang Yang, Sheng Zhou, Xiaoxuan Tang, Jiajun Bu, Yong Li, Wei Jiang
https://arxiv.org/abs/2509.03536 https://…

PG-Agent: An Agent Powered by Page Graph
Graphical User Interface (GUI) agents possess significant commercial and social value, and GUI agents powered by advanced multimodal large language models (MLLMs) have demonstrated remarkable potential. Currently, existing GUI agents usually utilize sequential episodes of multi-step operations across pages as the prior GUI knowledge, which fails to capture the complex transition relationship between pages, making it challenging for the agents to deeply perceive the GUI environment and generaliz…

Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scaling
Bruce Fang, Danyi Gao
https://arxiv.org/abs/2507.00550 https:/…

Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scaling
This paper addresses the challenges of rapid resource variation and highly uncertain task loads in cloud computing environments. It proposes an optimization method for elastic cloud resource scaling based on a multi-agent system. The method deploys multiple autonomous agents to perceive resource states in parallel and make local decisions. While maintaining the distributed nature of the system, it introduces a collaborative value function to achieve global coordination. This improves the respon…

Do LVLMs Know What They Know? A Systematic Study of Knowledge Boundary Perception in LVLMs
Zhikai Ding, Shiyu Ni, Keping Bi
https://arxiv.org/abs/2508.19111 https://

Do LVLMs Know What They Know? A Systematic Study of Knowledge Boundary Perception in LVLMs
Large vision-language models (LVLMs) demonstrate strong visual question answering (VQA) capabilities but are shown to hallucinate. A reliable model should perceive its knowledge boundaries-knowing what it knows and what it does not. This paper investigates LVLMs' perception of their knowledge boundaries by evaluating three types of confidence signals: probabilistic confidence, answer consistency-based confidence, and verbalized confidence. Experiments on three LVLMs across three VQA datasets sh…

"Maybe We Need Some More Examples:" Individual and Team Drivers of Developer GenAI Tool Use
Courtney Miller, Rudrajit Choudhuri, Mara Ulloa, Sankeerti Haniyur, Robert DeLine, Margaret-Anne Storey, Emerson Murphy-Hill, Christian Bird, Jenna L. Butler
https://arxiv.org/abs/2507.21280

"Maybe We Need Some More Examples:" Individual and Team Drivers of Developer GenAI Tool Use
Despite the widespread availability of generative AI tools in software engineering, developer adoption remains uneven. This unevenness is problematic because it hampers productivity efforts, frustrates management's expectations, and creates uncertainty around the future roles of developers. Through paired interviews with 54 developers across 27 teams -- one frequent and one infrequent user per team -- we demonstrate that differences in usage result primarily from how developers perceive the too…

Perceptual Distortions and Autonomous Representation Learning in a Minimal Robotic System
David Warutumo, Ciira wa Maina
https://arxiv.org/abs/2507.07845 h…

Perceptual Distortions and Autonomous Representation Learning in a Minimal Robotic System
Autonomous agents, particularly in the field of robotics, rely on sensory information to perceive and navigate their environment. However, these sensory inputs are often imperfect, leading to distortions in the agent's internal representation of the world. This paper investigates the nature of these perceptual distortions and how they influence autonomous representation learning using a minimal robotic system. We utilize a simulated two-wheeled robot equipped with distance sensors and a compass…

Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contrastive Dual-Path Learning Approach
Panpan Ji, Junni Song, Hang Xiao, Hanyu Liu, Chao Li
https://arxiv.org/abs/2507.02826

Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contrastive Dual-Path Learning Approach
Sensor-based Human Activity Recognition (HAR) is a core technology that enables intelligent systems to perceive and interact with their environment. However, multimodal HAR systems still encounter key challenges, such as difficulties in cross-modal feature alignment and imbalanced modality contributions. To address these issues, we propose a novel framework called the Dynamic Contrastive Dual-Path Network (DCDP-HAR). The framework comprises three key components. First, a dual-path feature extra…

🤩 Surgical microscope uses 48 tiny cameras to offer precise 3D imaging
#imaging

Surgical microscope uses 48 tiny cameras to offer precise 3D imaging
For over a century, surgeons performing delicate procedures have relied on stereoscopic microscopes to gain a sense of depth. These tools mimic human vision by presenting slightly different images to each eye, allowing the brain to perceive three-dimensional structures—a crucial aid when working with fragile blood vessels or intricate brain tissue. Despite modern upgrades like digital displays and video capture, today's operating microscopes still depend on the same core principle: two views,…

Integrating Vision Foundation Models with Reinforcement Learning for Enhanced Object Interaction
Ahmad Farooq, Kamran Iqbal
https://arxiv.org/abs/2508.05838 https://

Integrating Vision Foundation Models with Reinforcement Learning for Enhanced Object Interaction
This paper presents a novel approach that integrates vision foundation models with reinforcement learning to enhance object interaction capabilities in simulated environments. By combining the Segment Anything Model (SAM) and YOLOv5 with a Proximal Policy Optimization (PPO) agent operating in the AI2-THOR simulation environment, we enable the agent to perceive and interact with objects more effectively. Our comprehensive experiments, conducted across four diverse indoor kitchen settings, demons…

PC-JND: Subjective Study and Dataset on Just Noticeable Difference for Point Clouds in 6DoF Virtual Reality
Chunling Fan, Yun Zhang, Dietmar Saupe, Raouf Hamzaoui, Weisi Lin
https://arxiv.org/abs/2507.21557

PC-JND: Subjective Study and Dataset on Just Noticeable Difference for Point Clouds in 6DoF Virtual Reality
The Just Noticeable Difference (JND) accounts for the minimum distortion at which humans can perceive a difference between a pristine stimulus and its distorted version. The JND concept has been widely applied in visual signal processing tasks, including coding, transmission, rendering, and quality assessment, to optimize human-centric media experiences. A point cloud is a mainstream volumetric data representation consisting of both geometry information and attributes (e.g. color). Point clouds…

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- Humans Perceive Wrong Narratives from AI Reasoning Texts
Mosh Levy, Zohar Elyoseph, Yoav Goldberg

Probing Experts' Perspectives on AI-Assisted Public Speaking Training
Nesrine Fourati, Alisa Barkar, Marion Drag\'ee, Liv Danthon-Lefebvre, Mathieu Chollet
https://arxiv.org/abs/2507.07930

Probing Experts' Perspectives on AI-Assisted Public Speaking Training
Background: Public speaking is a vital professional skill, yet it remains a source of significant anxiety for many individuals. Traditional training relies heavily on expert coaching, but recent advances in AI has led to novel types of commercial automated public speaking feedback tools. However, most research has focused on prototypes rather than commercial applications, and little is known about how public speaking experts perceive these tools. Objectives: This study aims to evaluate expert…

End-to-End Audio-Visual Learning for Cochlear Implant Sound Coding in Noisy Environments
Meng-Ping Lin, Enoch Hsin-Ho Huang, Shao-Yi Chien, Yu Tsao
https://arxiv.org/abs/2508.13576

End-to-End Audio-Visual Learning for Cochlear Implant Sound Coding in Noisy Environments
The cochlear implant (CI) is a remarkable biomedical device that successfully enables individuals with severe-to-profound hearing loss to perceive sound by converting speech into electrical stimulation signals. Despite advancements in the performance of recent CI systems, speech comprehension in noisy or reverberant conditions remains a challenge. Recent and ongoing developments in deep learning reveal promising opportunities for enhancing CI sound coding capabilities, not only through replicat…

Integrated Radio Sensing Capabilities for 6G Networks: AI/ML Perspective
Victor Shatov, Steffen Schieler, Charlotte Muth, Jos\'e Miguel Mateos-Ramos, Ivo Bizon, Florian Euchner, Sebastian Semper, Stephan ten Brink, Gerhard Fettweis, Christian H\"ager, Henk Wymeersch, Laurent Schmalen, Reiner Thom\"a, Norman Franchi
https://

Integrated Radio Sensing Capabilities for 6G Networks: AI/ML Perspective
The sixth-generation wireless communications (6G) is often labeled as "connected intelligence". Radio sensing, aligned with machine learning (ML) and artificial intelligence (AI), promises, among other benefits, breakthroughs in the system's ability to perceive the environment and effectively utilize this awareness. This article offers a tutorial-style survey of AI and ML approaches to enhance the sensing capabilities of next-generation wireless networks. To this end, while staying in the frame…

Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks
Muhammad Ahmed Mohsin, Junaid Ahmad, Muhammad Hamza Nawaz, Muhammad Ali Jamshed
https://arxiv.org/abs/2508.19495

Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks
Ambient intelligence (AmI) is a computing paradigm in which physical environments are embedded with sensing, computation, and communication so they can perceive people and context, decide appropriate actions, and respond autonomously. Realizing AmI at global scale requires sixth generation (6G) wireless networks with capabilities for real time perception, reasoning, and action aligned with human behavior and mobility patterns. We argue that Generative Artificial Intelligence (GenAI) is the crea…

Baseline behaviour in human vision
Thomas Fabian
https://arxiv.org/abs/2507.14573 https://arxiv.org/pdf/2507.14573

Baseline behaviour in human vision
Humans perceive their visual environment by directing their eyes towards relevant objects. The deployment of visual attention depends substantially on the stimulus's properties, higher cognitive processes, and biases and constraints of the visual system. Numerous models describe people's eye movements depending on the performed task or the viewed content. However, there is no universal, context-invariant model of human gaze behaviour. Here we show that statistical regularities can be utilised t…

Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots
Oluwadamilola Sotomi, Devika Kodi, Kiruthiga Chandra Shekar, Aliasghar Arab
https://arxiv.org/abs/2509.06768

Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots
Autonomous robots operating in dynamic environments should identify and report anomalies. Embodying proactive mitigation improves safety and operational continuity. This paper presents a multimodal anomaly detection and mitigation system that integrates vision-language models and large language models to identify and report hazardous situations and conflicts in real-time. The proposed system enables robots to perceive, interpret, report, and if possible respond to urban and environmental anomal…

"It was Tragic": Exploring the Impact of a Robot's Shutdown
Agam Oberlender, Hadas Erel
https://arxiv.org/abs/2509.06934 https://arxiv.org/pd…

"It was Tragic": Exploring the Impact of a Robot's Shutdown
It is well established that people perceive robots as social entities, even when they are not designed for social interaction. We evaluated whether the social interpretation of robotic gestures should also be considered when turning off a robot. In the experiment, participants engaged in a brief preliminary neutral interaction while a robotic arm showed interest in their actions. At the end of the task, participants were asked to turn off the robotic arm under two conditions: (1) a Non-designed…

Understanding the Challenges and Promises of Developing Generative AI Apps: An Empirical Study
Buthayna AlMulla, Maram Assi, Safwat Hassan
https://arxiv.org/abs/2506.16453

Understanding the Challenges and Promises of Developing Generative AI Apps: An Empirical Study
The release of ChatGPT in 2022 triggered a rapid surge in generative artificial intelligence mobile apps (i.e., Gen-AI apps). Despite widespread adoption, little is known about how end users perceive and evaluate these Gen-AI functionalities in practice. In this work, we conduct a user-centered analysis of 676,066 reviews from 173 Gen-AI apps on the Google Play Store. We introduce a four-phase methodology, SARA (Selection, Acquisition, Refinement, and Analysis), that enables the systematic extr…

Situational Awareness as the Imperative Capability for Disaster Resilience in the Era of Complex Hazards and Artificial Intelligence
Hongrak Pak, Ali Mostafavi
https://arxiv.org/abs/2508.16669

Situational Awareness as the Imperative Capability for Disaster Resilience in the Era of Complex Hazards and Artificial Intelligence
Disasters frequently exceed established hazard models, revealing blind spots where unforeseen impacts and vulnerabilities hamper effective response. This perspective paper contends that situational awareness (SA)-the ability to perceive, interpret, and project dynamic crisis conditions-is an often overlooked yet vital capability for disaster resilience. While risk mitigation measures can reduce known threats, not all hazards can be neutralized; truly adaptive resilience hinges on whether organi…

Architecture Considerations for ISAC in 6G
Sebastian Robitzsch, Laksh Bhatia, Konstantinos G. Filis, Neda Petreska, Michael Bahr, Pablo Picazo Martinez, Xi Li
https://arxiv.org/abs/2508.13736

Architecture Considerations for ISAC in 6G
ISAC is emerging as a foundational capability in 6G, enabling mobile networks to not only offer communication services but also to sense and perceive their environment at scale. This paper explores architectural considerations to enable sensing in 6G, extending on recent developments by (pre-)standardisation bodies such as 3GPP and ETSI. Selected ISAC use cases are presented from the European MultiX project including associated potential functional system requirements. The paper proposes a 6G s…

Shape2Animal: Creative Animal Generation from Natural Silhouettes
Quoc-Duy Tran, Anh-Tuan Vo, Dinh-Khoi Vo, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
https://arxiv.org/abs/2506.20616

Shape2Animal: Creative Animal Generation from Natural Silhouettes
Humans possess a unique ability to perceive meaningful patterns in ambiguous stimuli, a cognitive phenomenon known as pareidolia. This paper introduces Shape2Animal framework to mimics this imaginative capacity by reinterpreting natural object silhouettes, such as clouds, stones, or flames, as plausible animal forms. Our automated framework first performs open-vocabulary segmentation to extract object silhouette and interprets semantically appropriate animal concepts using vision-language model…

Humans Perceive Wrong Narratives from AI Reasoning Texts
Mosh Levy, Zohar Elyoseph, Yoav Goldberg
https://arxiv.org/abs/2508.16599 https://arxiv.org/pdf/25…

Humans Perceive Wrong Narratives from AI Reasoning Texts
A new generation of AI models generates step-by-step reasoning text before producing an answer. This text appears to offer a human-readable window into their computation process, and is increasingly relied upon for transparency and interpretability. However, it is unclear whether human understanding of this text matches the model's actual computational process. In this paper, we investigate a necessary condition for correspondence: the ability of humans to identify which steps in a reasoning te…

Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems
Yilin Li, Song Han, Sibo Wang, Ming Wang, Renzi Meng
https://arxiv.org/abs/2508.20508 https://

Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems
This paper proposes an intelligent service optimization method based on a multi-agent collaborative evolution mechanism to address governance challenges in large-scale microservice architectures. These challenges include complex service dependencies, dynamic topology structures, and fluctuating workloads. The method models each service as an agent and introduces graph representation learning to construct a service dependency graph. This enables agents to perceive and embed structural changes wi…

Passive Body-Area Electrostatic Field (Human Body Capacitance) for Ubiquitous Computing
Sizhen Bian, Mengxi Liu, Paul Lukowicz
https://arxiv.org/abs/2507.13520

Passive Body-Area Electrostatic Field (Human Body Capacitance) for Ubiquitous Computing
Passive body-area electrostatic field sensing, also referred to as human body capacitance (HBC), is an energy-efficient and non-intrusive sensing modality that exploits the human body's inherent electrostatic properties to perceive human behaviors. This paper presents a focused overview of passive HBC sensing, including its underlying principles, historical evolution, hardware architectures, and applications across research domains. Key challenges, such as susceptibility to environmental variat…

Would you let a humanoid play storytelling with your child? A usability study on LLM-powered narrative Humanoid-Robot Interaction
Maria Lombardi, Carmela Calabrese, Davide Ghiglino, Caterina Foglino, Davide De Tommaso, Giulia Da Lisca, Lorenzo Natale, Agnieszka Wykowska
https://arxiv.org/abs/2508.02505…

Would you let a humanoid play storytelling with your child? A usability study on LLM-powered narrative Humanoid-Robot Interaction
A key challenge in human-robot interaction research lies in developing robotic systems that can effectively perceive and interpret social cues, facilitating natural and adaptive interactions. In this work, we present a novel framework for enhancing the attention of the iCub humanoid robot by integrating advanced perceptual abilities to recognise social cues, understand surroundings through generative models, such as ChatGPT, and respond with contextually appropriate social behaviour. Specifical…

Towards Open World Detection: A Survey
Andrei-Stefan Bulzan, Cosmin Cernazanu-Glavan
https://arxiv.org/abs/2508.16527 https://arxiv.org/pdf/2508.16527

Towards Open World Detection: A Survey
For decades, Computer Vision has aimed at enabling machines to perceive the external world. Initial limitations led to the development of highly specialized niches. As success in each task accrued and research progressed, increasingly complex perception tasks emerged. This survey charts the convergence of these tasks and, in doing so, introduces Open World Detection (OWD), an umbrella term we propose to unify class-agnostic and generally applicable detection models in the vision domain. We star…

The SPACE of AI: Real-World Lessons on AI's Impact on Developers
Brian Houck, Travis Lowdermilk, Cody Beyer, Steven Clarke, Ben Hanrahan
https://arxiv.org/abs/2508.00178 htt…

The SPACE of AI: Real-World Lessons on AI's Impact on Developers
As artificial intelligence (AI) tools become increasingly embedded in software development workflows, questions persist about their true impact on developer productivity and experience. This paper presents findings from a mixed-methods study examining how developers perceive AI's influence across the dimensions of the SPACE framework: Satisfaction, Performance, Activity, Collaboration and Efficiency. Drawing on survey responses from over 500 developers and qualitative insights from interviews a…

QRetinex-Net: Quaternion-Valued Retinex Decomposition for Low-Level Computer Vision Applications
Sos Agaian, Vladimir Frants
https://arxiv.org/abs/2507.16683

QRetinex-Net: Quaternion-Valued Retinex Decomposition for Low-Level Computer Vision Applications
Images taken in low light often show color shift, low contrast, noise, and other artifacts that hurt computer-vision accuracy. Retinex theory addresses this by viewing an image S as the pixel-wise product of reflectance R and illumination I, mirroring the way people perceive stable object colors under changing light. The decomposition is ill-posed, and classic Retinex models have four key flaws: (i) they treat the red, green, and blue channels independently; (ii) they lack a neuroscientific mod…

Customer Service Representative's Perception of the AI Assistant in an Organization's Call Center
Kai Qin, Kexin Du, Yimeng Chen, Yueyan Liu, Jie Cai, Zhiqiang Nie, Nan Gao, Guohui Wei, Shengzhu Wang, Chun Yu
https://arxiv.org/abs/2507.00513

Customer Service Representative's Perception of the AI Assistant in an Organization's Call Center
The integration of various AI tools creates a complex socio-technical environment where employee-customer interactions form the core of work practices. This study investigates how customer service representatives (CSRs) at the power grid service customer service call center perceive AI assistance in their interactions with customers. Through a field visit and semi-structured interviews with 13 CSRs, we found that AI can alleviate some traditional burdens during the call (e.g., typing and memori…

HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning
Zhi Su, Bike Zhang, Nima Rahmanian, Yuman Gao, Qiayuan Liao, Caitlin Regan, Koushil Sreenath, S. Shankar Sastry
https://arxiv.org/abs/2508.21043

HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning
Humanoid robots have recently achieved impressive progress in locomotion and whole-body control, yet they remain constrained in tasks that demand rapid interaction with dynamic environments through manipulation. Table tennis exemplifies such a challenge: with ball speeds exceeding 5 m/s, players must perceive, predict, and act within sub-second reaction times, requiring both agility and precision. To address this, we present a hierarchical framework for humanoid table tennis that integrates a m…

Bias, Accuracy, and Trust: Gender-Diverse Perspectives on Large Language Models
Aimen Gaba, Emily Wall, Tejas Ramkumar Babu, Yuriy Brun, Kyle Hall, Cindy Xiong Bearfield
https://arxiv.org/abs/2506.21898

Bias, Accuracy, and Trust: Gender-Diverse Perspectives on Large Language Models
Large language models (LLMs) are becoming increasingly ubiquitous in our daily lives, but numerous concerns about bias in LLMs exist. This study examines how gender-diverse populations perceive bias, accuracy, and trustworthiness in LLMs, specifically ChatGPT. Through 25 in-depth interviews with non-binary/transgender, male, and female participants, we investigate how gendered and neutral prompts influence model responses and how users evaluate these responses. Our findings reveal that gendered…

How Age Influences the Interpretation of Emotional Body Language in Humanoid Robots -- long paper version
Ilaria Consoli, Claudio Mattutino, Cristina Gena, Berardina de Carolis, Giuseppe Palestra
https://arxiv.org/abs/2507.19335

How Age Influences the Interpretation of Emotional Body Language in Humanoid Robots -- long paper version
This paper presents an empirical study investigating how individuals across different age groups, children, young and older adults, interpret emotional body language expressed by the humanoid robot NAO. The aim is to offer insights into how users perceive and respond to emotional cues from robotic agents, through an empirical evaluation of the robot's effectiveness in conveying emotions to different groups of users. By analyzing data collected from elderly participants and comparing these findi…

V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling
Junwei You, Pei Li, Zhuoyu Jiang, Zilin Huang, Rui Gan, Haotian Shi, Bin Ran
https://arxiv.org/abs/2506.21041

V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling
Ensuring robust planning and decision-making under rare, diverse, and visually degraded long-tail scenarios remains a fundamental challenge for autonomous driving in urban environments. This issue becomes more critical in cooperative settings, where vehicles and infrastructure jointly perceive and reason across complex environments. To address this challenge, we propose V2X-REALM, a vision-language model (VLM)-based framework with adaptive multimodal learning for robust cooperative autonomous d…

Characterizing Visualization Perception with Psychological Phenomena: Uncovering the Role of Subitizing in Data Visualization
Arran Zeyu Wang, Ghulam Jilani Quadri, Mengyuan Zhu, Chin Tseng, Danielle Albers Szafir
https://arxiv.org/abs/2508.17460

Characterizing Visualization Perception with Psychological Phenomena: Uncovering the Role of Subitizing in Data Visualization
Understanding how people perceive visualizations is crucial for designing effective visual data representations; however, many heuristic design guidelines are derived from specific tasks or visualization types, without considering the constraints or conditions under which those guidelines hold. In this work, we aimed to assess existing design heuristics for categorical visualization using well-established psychological knowledge. Specifically, we examine the impact of the subitizing phenomenon …

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…

Deceptive Game Design? Investigating the Impact of Visual Card Style on Player Perception
Leonie Kallabis, Timo Bertram, Florian Rupp
https://arxiv.org/abs/2506.18648

Deceptive Game Design? Investigating the Impact of Visual Card Style on Player Perception
The visual style of game elements considerably contributes to the overall experience. Aesthetics influence player appeal, while the abilities of game pieces define their in-game functionality. In this paper, we investigate how the visual style of collectible cards influences the players' perception of the card's actual strength in the game. Using the popular trading card game Magic: The Gathering, we conduct a single-blind survey study that examines how players perceive the strength of AI-gener…

Animal Interaction with Autonomous Mobility Systems: Designing for Multi-Species Coexistence
Tram Thi Minh Tran, Xinyan Yu, Marius Hoggenmueller, Callum Paker, Paul Schmitt, Julie Stephany Berrio Perez, Stewart Worrall, Martin Tomitsch
https://arxiv.org/abs/2507.16258

Animal Interaction with Autonomous Mobility Systems: Designing for Multi-Species Coexistence
Autonomous mobility systems increasingly operate in environments shared with animals, from urban pets to wildlife. However, their design has largely focused on human interaction, with limited understanding of how non-human species perceive, respond to, or are affected by these systems. Motivated by research in Animal-Computer Interaction (ACI) and more-than-human design, this study investigates animal interactions with autonomous mobility through a multi-method approach combining a scoping revi…

Mind & Motion: Opportunities and Applications of Integrating Biomechanics and Cognitive Models in HCI
Arthur Fleig, Florian Fischer, Markus Klar, Patrick Ebel, Miroslav Bachinski, Per Ola Kristensson, Roderick Murray-Smith, Antti Oulasvirta
https://arxiv.org/abs/2508.13788

Mind & Motion: Opportunities and Applications of Integrating Biomechanics and Cognitive Models in HCI
Computational models of how users perceive and act within a virtual or physical environment offer enormous potential for the understanding and design of user interactions. Cognition models have been used to understand the role of attention and individual preferences and beliefs on human decision making during interaction, while biomechanical simulations have been successfully applied to analyse and predict physical effort, fatigue, and discomfort. The next frontier in HCI lies in connecting the…