Tootfinder

AI Knows Best? The Paradox of Expertise, AI-Reliance, and Performance in Educational Tutoring Decision-Making Tasks
Eason Chen, Jeffrey Li, Scarlett Huang, Xinyi Tang, Jionghao Lin, Paulo Carvalho, Kenneth Koedinger
https://arxiv.org/abs/2509.16772

AI Knows Best? The Paradox of Expertise, AI-Reliance, and Performance in Educational Tutoring Decision-Making Tasks
We present an empirical study of how both experienced tutors and non-tutors judge the correctness of tutor praise responses under different Artificial Intelligence (AI)-assisted interfaces, types of explanation (textual explanations vs. inline highlighting). We first fine-tuned several Large Language Models (LLMs) to produce binary correctness labels and explanations, achieving up to 88% accuracy and 0.92 F1 score with GPT-4. We then let the GPT-4 models assist 95 participants in tutoring decis…

Interface on demand: Towards AI native Control interfaces for 6G
Abhishek Dandekar, Prashiddha D. Thapa, Ashrafur Rahman, Julius Schulz-Zander
https://arxiv.org/abs/2508.15595 h…

Interface on demand: Towards AI native Control interfaces for 6G
Traditional standardized network interfaces face significant limitations, including vendor-specific incompatibilities, rigid design assumptions, and lack of adaptability for new functionalities. We propose a multi-agent framework leveraging large language models (LLMs) to generate control interfaces on demand between network functions (NFs). This includes a matching agent, which aligns required control functionalities with NF capabilities, and a code-generation agent, which generates the necess…

A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations
Devin Lange, Shanghua Gao, Pengwei Sui, Austen Money, Priya Misner, Marinka Zitnik, Nils Gehlenborg
https://arxiv.org/abs/2509.16454

A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations
We explore the potential for combining generative AI with grammar-based visualizations for biomedical data discovery. In our prototype, we use a multi-agent system to generate visualization specifications and apply filters. These visualizations are linked together, resulting in an interactive dashboard that is progressively constructed. Our system leverages the strengths of natural language while maintaining the utility of traditional user interfaces. Furthermore, we utilize generated interacti…

Explainable AI for Maritime Autonomous Surface Ships (MASS): Adaptive Interfaces and Trustworthy Human-AI Collaboration
Zhuoyue Zhang, Haitong Xu
https://arxiv.org/abs/2509.15959

Explainable AI for Maritime Autonomous Surface Ships (MASS): Adaptive Interfaces and Trustworthy Human-AI Collaboration
Autonomous navigation in maritime domains is accelerating alongside advances in artificial intelligence, sensing, and connectivity. Opaque decision-making and poorly calibrated human-automation interaction remain key barriers to safe adoption. This article synthesizes 100 studies on automation transparency for Maritime Autonomous Surface Ships (MASS) spanning situation awareness (SA), human factors, interface design, and regulation. We (i) map the Guidance-Navigation-Control stack to shore-base…

I left a comment last week on Smashing’s “Design Patterns For AI Interfaces” (which is still in moderation):
https://www.smashingmagazine.com/2025/07/design-patterns-ai-interfaces/
Essentially, if your UI doesn’t also try to prevent harm then it might …

Design Patterns For AI Interfaces — Smashing Magazine
Designing a new AI feature? Where do you even begin? Here’s a simple, practical overview with useful design patterns for better AI experiences.

Forbes: The past, present and future of brain-computer interfaces https://www.forbes.com/sites/robtoews/2025/10/05/these-are-the-startups-merging-your-brain-with-ai/ Will Part 2 address The vOICe non-invasive vision BCI for the blind? …

These Are The Startups Merging Your Brain With AI
Brain-computer interfaces (BCI) sound like science fiction to most people. But this technology is getting real, quickly.

AnchoredAI: Contextual Anchoring of AI Comments Improves Writer Agency and Ownership
Martin Lou, Jackie Crowley, Samuel Dodson, Dongwook Yoon
https://arxiv.org/abs/2509.16128 ht…

AnchoredAI: Contextual Anchoring of AI Comments Improves Writer Agency and Ownership
Generative AI is increasingly integrated into writing support, yet current chat-based interfaces often obscure referential context and risk amplifying automation bias and overreliance. We introduce AnchoredAI, a novel system that anchors AI feedback directly to relevant text spans. AnchoredAI implements two key mechanisms: (1) an Anchoring Context Window (ACW) that maintains unique, context-rich references, and (2) an update-aware context retrieval method that preserves the intent of prior comm…

A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN
Md Habibur Rahman, Md Sharif Hossen, Nathan H. Stephenson, Vijay K. Shah, Aloizio Da Silva
https://arxiv.org/abs/2510.09706

A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN
The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking, network function virtualization, and implementation of standardized open interfaces. However, one of the security concerns for O-RAN, which can severely undermine network performance, is jamming attacks. This paper presents SAJD- a self-adaptive jammer detection framework that autonomously detects jamming attacks in AI/ML framework-in…

Agentic generative AI for media content discovery at the national football league
Henry Wang, Sirajus Salekin, Jake Lee, Ross Claytor, Shinan Zhang, Michael Chi
https://arxiv.org/abs/2510.07297

Agentic generative AI for media content discovery at the national football league
Generative AI has unlocked new possibilities in content discovery and management. Through collaboration with the National Football League (NFL), we demonstrate how a generative-AI based workflow enables media researchers and analysts to query relevant historical plays using natural language rather than traditional filter-and-click interfaces. The agentic workflow takes a user query as input, breaks it into elements, and translates them into the underlying database query language. Accuracy and l…

AI use cases introduced by Rob Finn from EMBL-EBI, as pointed out in his #CORDI2025 keynote "Delivering life science data resources in a world of growing data and impacts from AI"
https://www.nfdi.de/cordi-2025/keynotes/

Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction
Sahar Salimpour, Lei Fu, Farhad Keramat, Leonardo Militano, Giovanni Toffetti, Harry Edelman, Jorge Pe\~na Queralta
https://arxiv.org/abs/2508.05294

Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction
Foundation models, including large language models (LLMs) and vision-language models (VLMs), have recently enabled novel approaches to robot autonomy and human-robot interfaces. In parallel, vision-language-action models (VLAs) or large behavior models (BLMs) are increasing the dexterity and capabilities of robotic systems. This survey paper focuses on those words advancing towards agentic applications and architectures. This includes initial efforts exploring GPT-style interfaces to tooling, a…

webMCP: Efficient AI-Native Client-Side Interaction for Agent-Ready Web Design
D. Perera
https://arxiv.org/abs/2508.09171 https://arxiv.org/pdf/2508.09171

webMCP: Efficient AI-Native Client-Side Interaction for Agent-Ready Web Design
Current AI agents create significant barriers for users by requiring extensive processing to understand web pages, making AI-assisted web interaction slow and expensive. This paper introduces webMCP (Web Machine Context & Procedure), a client-side standard that embeds structured interaction metadata directly into web pages, enabling more efficient human-AI collaboration on existing websites. webMCP transforms how AI agents understand web interfaces by providing explicit mappings between page el…

GenAI Voice Mode in Programming Education
Sven Jacobs, Natalie Kiesler
https://arxiv.org/abs/2509.10596 https://arxiv.org/pdf/2509.10596

GenAI Voice Mode in Programming Education
Real-time voice interfaces using multimodal Generative AI (GenAI) can potentially address the accessibility needs of novice programmers with disabilities (e.g., related to vision). Yet, little is known about how novices interact with GenAI tools and their feedback quality in the form of audio output. This paper analyzes audio dialogues from nine 9th-grade students using a voice-enabled tutor (powered by OpenAI's Realtime API) in an authentic classroom setting while learning Python. We examined …

Coexistence of Two Types of Liquid Structures at Platinum-Water Interfaces
Yitong Li, Qian Ai, Lalith Krishna Samanth Bonagiri, Yingjie Zhang
https://arxiv.org/abs/2509.06327 ht…

Coexistence of Two Types of Liquid Structures at Platinum-Water Interfaces
Platinum-water interfaces underpin many electrochemical energy conversion processes. However, despite decades of research, the real-space liquid structure of these interfaces remains elusive. Using three-dimensional atomic force microscopy (3D-AFM), we mapped Pt-water interface in real space with angstrom-level resolution. Topographic imaging revealed atomically flat (type I) and stripe-like (type II) surface nanodomains. Force-distance profiles above type I domains exhibited oscillatory decay …

Designing and Evaluating AI Margin Notes in Document Reader Software
Nikhita Joshi, Daniel Vogel
https://arxiv.org/abs/2509.09840 https://arxiv.org/pdf/250…

Designing and Evaluating AI Margin Notes in Document Reader Software
AI capabilities for document reader software are usually presented in separate chat interfaces. We explore integrating AI into document comments, a concept we formalize as AI margin notes. Three design parameters characterize this approach: margin notes are integrated with the text while chat interfaces are not; selecting text for a margin note can be automated through AI or manual; and the generation of a margin note can involve AI to various degrees. Two experiments investigate integration an…

Agentic AI meets Neural Architecture Search: Proactive Traffic Prediction for AI-RAN
Abdelaziz Salama, Mohammed M. H. Qazzaz, Zeinab Nezami, Maryam Hafeez, Syed Ali Raza Zaidi
https://arxiv.org/abs/2510.00851

Agentic AI meets Neural Architecture Search: Proactive Traffic Prediction for AI-RAN
Next-generation wireless networks require intelligent traffic prediction to enable autonomous resource management and handle diverse, dynamic service demands. The Open Radio Access Network (O-RAN) framework provides a promising foundation for embedding machine learning intelligence through its disaggregated architecture and programmable interfaces. This work applies a Neural Architecture Search (NAS)-based framework that dynamically selects and orchestrates efficient Long Short-Term Memory (LST…

Multi-Channel Differential ASR for Robust Wearer Speech Recognition on Smart Glasses
Yufeng Yang, Yiteng Huang, Yong Xu, Li Wan, Suwon Shon, Yang Liu, Yifeng Fan, Zhaojun Yang, Olivier Siohan, Yue Liu, Ming Sun, Florian Metze
https://arxiv.org/abs/2509.14430

Multi-Channel Differential ASR for Robust Wearer Speech Recognition on Smart Glasses
With the growing adoption of wearable devices such as smart glasses for AI assistants, wearer speech recognition (WSR) is becoming increasingly critical to next-generation human-computer interfaces. However, in real environments, interference from side-talk speech remains a significant challenge to WSR and may cause accumulated errors for downstream tasks such as natural language processing. In this work, we introduce a novel multi-channel differential automatic speech recognition (ASR) method …

FROGENT: An End-to-End Full-process Drug Design Agent
Qihua Pan, Dong Xu, Jenna Xinyi Yao, Lijia Ma, Zexuan Zhu, Junkai Ji
https://arxiv.org/abs/2508.10760 https://

FROGENT: An End-to-End Full-process Drug Design Agent
Powerful AI tools for drug discovery reside in isolated web apps, desktop programs, and code libraries. Such fragmentation forces scientists to manage incompatible interfaces and specialized scripts, which can be a cumbersome and repetitive process. To address this issue, a Full-pROcess druG dEsign ageNT, named FROGENT, has been proposed. Specifically, FROGENT utilizes a Large Language Model and the Model Context Protocol to integrate multiple dynamic biochemical databases, extensible tool libr…

An AUTOSAR-Aligned Architectural Study of Vulnerabilities in Automotive SoC Software
Srijita Basu, Haraldsson Bengt, Miroslaw Staron, Christian Berger, Jennifer Horkoff, Magnus Almgren
https://arxiv.org/abs/2510.07941

An AUTOSAR-Aligned Architectural Study of Vulnerabilities in Automotive SoC Software
Cooperative, Connected and Automated Mobility (CCAM) are complex cyber-physical systems (CPS) that integrate computation, communication, and control in safety-critical environments. At their core, System-on-Chip (SoC) platforms consolidate processing units, communication interfaces, AI accelerators, and security modules into a single chip. AUTOSAR (AUTomotive Open System ARchitecture) standard was developed in the automotive domain to better manage this complexity, defining layered software str…

Imposing AI: Deceptive design patterns against sustainability
Ana\"elle Beignon, Thomas Thibault, Nolwenn Maudet
https://arxiv.org/abs/2508.08672 https://

Imposing AI: Deceptive design patterns against sustainability
Generative AI is being massively deployed in digital services, at a scale that will result in significant environmental harm. We document how tech companies are transforming established user interfaces to impose AI use and show how and to what extent these strategies fit within established deceptive pattern categories. We identify two main design strategies that are implemented to impose AI use in both personal and professional contexts: imposing AI features in interfaces at the expense of exis…

An Automated Multi-Modal Evaluation Framework for Mobile Intelligent Assistants
Meiping Wang, Jian Zhong, Rongduo Han, Liming Kang, Zhengkun Shi, Xiao Liang, Xing Lin, Nan Gao, Haining Zhang
https://arxiv.org/abs/2508.09507

An Automated Multi-Modal Evaluation Framework for Mobile Intelligent Assistants
With the rapid development of mobile intelligent assistant technologies, multi-modal AI assistants have become essential interfaces for daily user interactions. However, current evaluation methods face challenges including high manual costs, inconsistent standards, and subjective bias. This paper proposes an automated multi-modal evaluation framework based on large language models and multi-agent collaboration. The framework employs a three-tier agent architecture consisting of interaction eval…

AI as IA: The use and abuse of artificial intelligence (AI) for human enhancement through intellectual augmentation (IA)
Alexandre Erler, Vincent C. M\"uller
https://arxiv.org/abs/2508.16642

AI as IA: The use and abuse of artificial intelligence (AI) for human enhancement through intellectual augmentation (IA)
This paper offers an overview of the prospects and ethics of using AI to achieve human enhancement, and more broadly what we call intellectual augmentation (IA). After explaining the central notions of human enhancement, IA, and AI, we discuss the state of the art in terms of the main technologies for IA, with or without brain-computer interfaces. Given this picture, we discuss potential ethical problems, namely inadequate performance, safety, coercion and manipulation, privacy, cognitive liber…

An advanced AI driven database system
M. Tedeschi, S. Rizwan, C. Shringi, V. Devram Chandgir, S. Belich
https://arxiv.org/abs/2507.17778 https://arxiv.org/…

An advanced AI driven database system
Contemporary database systems, while effective, suffer severe issues related to complexity and usability, especially among individuals who lack technical expertise but are unfamiliar with query languages like Structured Query Language (SQL). This paper presents a new database system supported by Artificial Intelligence (AI), which is intended to improve the management of data using natural language processing (NLP) - based intuitive interfaces, and automatic creation of structured queries and s…

Beyond Rigid AI: Towards Natural Human-Machine Symbiosis for Interoperative Surgical Assistance
Lalithkumar Seenivasan, Jiru Xu, Roger D. Soberanis Mukul, Hao Ding, Grayson Byrd, Yu-Chun Ku, Jose L. Porras, Masaru Ishii, Mathias Unberath
https://arxiv.org/abs/2507.23088

Beyond Rigid AI: Towards Natural Human-Machine Symbiosis for Interoperative Surgical Assistance
Emerging surgical data science and robotics solutions, especially those designed to provide assistance in situ, require natural human-machine interfaces to fully unlock their potential in providing adaptive and intuitive aid. Contemporary AI-driven solutions remain inherently rigid, offering limited flexibility and restricting natural human-machine interaction in dynamic surgical environments. These solutions rely heavily on extensive task-specific pre-training, fixed object categories, and exp…

Barriers that Programming Instructors Face While Performing Emergency Pedagogical Design to Shape Student-AI Interactions with Generative AI Tools
Sam Lau (University of California San Diego), Kianoosh Boroojeni (Florida International University), Harry Keeling (Howard University), Jenn Marroquin (Google)
https://arxiv.org/abs/2510.09492…

Barriers that Programming Instructors Face While Performing Emergency Pedagogical Design to Shape Student-AI Interactions with Generative AI Tools
Generative AI (GenAI) tools are increasingly pervasive, pushing instructors to redesign how students use GenAI tools in coursework. We conceptualize this work as emergency pedagogical design: reactive, indirect efforts by instructors to shape student-AI interactions without control over commercial interfaces. To understand practices of lead users conducting emergency pedagogical design, we conducted interviews (n=13) and a survey (n=169) of computing instructors. These instructors repeatedly en…

CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration
Ziyi Wang, Ziwen Zeng, Yuan Li, Zijian Ding
https://arxiv.org/abs/2509.11461

CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration
Career exploration is uncertain, requiring decisions with limited information and unpredictable outcomes. While generative AI offers new opportunities for career guidance, most systems rely on linear chat interfaces that produce overly comprehensive and idealized suggestions, overlooking the non-linear and effortful nature of real-world trajectories. We present CareerPooler, a generative AI-powered system that employs a pool-table metaphor to simulate career development as a spatial and narrati…

Beyond the Bermuda Triangle of Contention: IOMMU Interference in Mixed Criticality Systems
Diogo Costa, Jose Martins, Sandro Pinto
https://arxiv.org/abs/2508.19670 https://

Beyond the Bermuda Triangle of Contention: IOMMU Interference in Mixed Criticality Systems
As Mixed Criticality Systems (MCSs) evolve, they increasingly integrate heterogeneous computing platforms, combining general-purpose processors with specialized accelerators such as AI engines, GPUs, and high-speed networking interfaces. This heterogeneity introduces challenges, as these accelerators and DMA-capable devices act as independent bus masters, directly accessing memory. Consequently, ensuring both security and timing predictability in such environments becomes critical. To address t…

A Framework for Rapidly Developing and Deploying Protection Against Large Language Model Attacks
Adam Swanda, Amy Chang, Alexander Chen, Fraser Burch, Paul Kassianik, Konstantin Berlin
https://arxiv.org/abs/2509.20639

A Framework for Rapidly Developing and Deploying Protection Against Large Language Model Attacks
The widespread adoption of Large Language Models (LLMs) has revolutionized AI deployment, enabling autonomous and semi-autonomous applications across industries through intuitive language interfaces and continuous improvements in model development. However, the attendant increase in autonomy and expansion of access permissions among AI applications also make these systems compelling targets for malicious attacks. Their inherent susceptibility to security flaws necessitates robust defenses, yet …

FlexMind: Scaffolding Flexible Ideation Workflows with AI in Creative Problem-Solving
Yaqing Yang, Vikram Mohanty, Nikolas Martelaro, Aniket Kittur, Yan-Ying Chen, Matthew K. Hong
https://arxiv.org/abs/2509.12408

FlexMind: Scaffolding Flexible Ideation Workflows with AI in Creative Problem-Solving
Divergent thinking in the ideation stage of creative problem-solving demands that individuals explore a broad design space. Yet this exploration rarely follows a neat, linear sequence; problem-solvers constantly shift among searching, creating, and evaluating ideas. Existing interfaces either impose rigid, step-by-step workflows or permit unguided free-form exploration. To strike a balance between flexibility and guidance for augmenting people's efficiency and creativity, we introduce a human-A…

FaMA: LLM-Empowered Agentic Assistant for Consumer-to-Consumer Marketplace
Yineng Yan, Xidong Wang, Jin Seng Cheng, Ran Hu, Wentao Guan, Nahid Farahmand, Hengte Lin, Yue Li
https://arxiv.org/abs/2509.03890

FaMA: LLM-Empowered Agentic Assistant for Consumer-to-Consumer Marketplace
The emergence of agentic AI, powered by Large Language Models (LLMs), marks a paradigm shift from reactive generative systems to proactive, goal-oriented autonomous agents capable of sophisticated planning, memory, and tool use. This evolution presents a novel opportunity to address long-standing challenges in complex digital environments. Core tasks on Consumer-to-Consumer (C2C) e-commerce platforms often require users to navigate complex Graphical User Interfaces (GUIs), making the experience…

Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight
Jingyu Tang, Chaoran Chen, Jiawen Li, Zhiping Zhang, Bingcan Guo, Ibrahim Khalilov, Simret Araya Gebreegziabher, Bingsheng Yao, Dakuo Wang, Yanfang Ye, Tianshi Li, Ziang Xiao, Yaxing Yao, Toby Jia-Jun Li
https://arxiv.org/abs/…

Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight
The dark patterns, deceptive interface designs manipulating user behaviors, have been extensively studied for their effects on human decision-making and autonomy. Yet, with the rising prominence of LLM-powered GUI agents that automate tasks from high-level intents, understanding how dark patterns affect agents is increasingly important. We present a two-phase empirical study examining how agents, human participants, and human-AI teams respond to 16 types of dark patterns across diverse scenario…

"We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe
Alexandra Klymenko, Stephen Meisenbacher, Patrick Gage Kelley, Sai Teja Peddinti, Kurt Thomas, Florian Matthes
https://arxiv.org/abs/2510.00909

"We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe
The proliferation of AI has sparked privacy concerns related to training data, model interfaces, downstream applications, and more. We interviewed 25 AI developers based in Europe to understand which privacy threats they believe pose the greatest risk to users, developers, and businesses and what protective strategies, if any, would help to mitigate them. We find that there is little consensus among AI developers on the relative ranking of privacy risks. These differences stem from salient reas…

GridMind: LLMs-Powered Agents for Power System Analysis and Operations
Hongwei Jin, Kibaek Kim, Jonghwan Kwon
https://arxiv.org/abs/2509.02494 https://arxi…

GridMind: LLMs-Powered Agents for Power System Analysis and Operations
The complexity of traditional power system analysis workflows presents significant barriers to efficient decision-making in modern electric grids. This paper presents GridMind, a multi-agent AI system that integrates Large Language Models (LLMs) with deterministic engineering solvers to enable conversational scientific computing for power system analysis. The system employs specialized agents coordinating AC Optimal Power Flow and N-1 contingency analysis through natural language interfaces whi…

Agent Network Protocol Technical White Paper
Gaowei Chang, Eidan Lin, Chengxuan Yuan, Rizhao Cai, Binbin Chen, Xuan Xie, Yin Zhang
https://arxiv.org/abs/2508.00007 https://

Agent Network Protocol Technical White Paper
With the development of large models and autonomous decision-making AI, agents are rapidly becoming the new entities of the internet, following mobile apps. However, existing internet infrastructure is primarily designed for human interaction, creating data silos, unfriendly interfaces, and high collaboration costs among agents, making it difficult to support the needs for large-scale agent interconnection and collaboration. The internet is undergoing a profound transformation, showing four cor…

The Command Line GUIde: Graphical Interfaces from Man Pages via AI
Saketh Ram Kasibatla, Kiran Medleri Hiremath, Raven Rothkopf, Sorin Lerner, Haijun Xia, Brian Hempel
https://arxiv.org/abs/2510.01453 …

The Command Line GUIde: Graphical Interfaces from Man Pages via AI
Although birthed in the era of teletypes, the command line shell survived the graphical interface revolution of the 1980's and lives on in modern desktop operating systems. The command line provides access to powerful functionality not otherwise exposed on the computer, but requires users to recall textual syntax and carefully scour documentation. In contrast, graphical interfaces let users organically discover and invoke possible actions through widgets and menus. To better expose the power of…

CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Abdul Basit, Maha Nawaz, Saim Rehman, Muhammad Shafique
https://arxiv.org/abs/2508.07731

CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing, including pre-filtering, feature extraction, and action prediction, performed in real time on edge AI hardware. Achieving this on resource-constrained devices presents challenges in balancing model complexity, computational efficiency, and latency. We present CognitiveArm, an EEG-driven, brain-controlled prosthetic system implemented on embedded AI hardware, achieving real-t…

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics
Babak Azkaei, Kishor Chandra Joshi, George Exarchakos
https://arxiv.org/abs/2509.03290 https://…

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics
The ever-increasing reliance of critical services on network infrastructure coupled with the increased operational complexity of beyond-5G/6G networks necessitate the need for proactive and automated network fault management. The provision for open interfaces among different radio access network\,(RAN) elements and the integration of AI/ML into network architecture enabled by the Open RAN\,(O-RAN) specifications bring new possibilities for active network health monitoring and anomaly detection.…

Open WebUI: An Open, Extensible, and Usable Interface for AI Interaction
Jaeryang Baek, Ayana Hussain, Danny Liu, Nicholas Vincent, Lawrence H. Kim
https://arxiv.org/abs/2510.02546

Open WebUI: An Open, Extensible, and Usable Interface for AI Interaction
While LLMs enable a range of AI applications, interacting with multiple models and customizing workflows can be challenging, and existing LLM interfaces offer limited support for collaborative extension or real-world evaluation. In this work, we present an interface toolkit for LLMs designed to be open (open-source and local), extensible (plugin support and users can interact with multiple models), and usable. The extensibility is enabled through a two-pronged plugin architecture and a communit…

User-Centered Design with AI in the Loop: A Case Study of Rapid User Interface Prototyping with "Vibe Coding"
Tianyi Li, Tanay Maheshwari, Alex Voelker
https://arxiv.org/abs/2507.21012

User-Centered Design with AI in the Loop: A Case Study of Rapid User Interface Prototyping with "Vibe Coding"
We present a case study of using generative user interfaces, or ``vibe coding,'' a method leveraging large language models (LLMs) for generating code via natural language prompts, to support rapid prototyping in user-centered design (UCD). Extending traditional UCD practices, we propose an AI-in-the-loop ideate-prototyping process. We share insights from an empirical experience integrating this process to develop an interactive data analytics interface for highway traffic engineers to effective…

DeformTune: A Deformable XAI Music Prototype for Non-Musicians
Ziqing Xu, Nick Bryan-Kinns
https://arxiv.org/abs/2508.00160 https://arxiv.org/pdf/2508.0016…

DeformTune: A Deformable XAI Music Prototype for Non-Musicians
Many existing AI music generation tools rely on text prompts, complex interfaces, or instrument-like controls, which may require musical or technical knowledge that non-musicians do not possess. This paper introduces DeformTune, a prototype system that combines a tactile deformable interface with the MeasureVAE model to explore more intuitive, embodied, and explainable AI interaction. We conducted a preliminary study with 11 adult participants without formal musical training to investigate thei…

IoT and Older Adults: Towards Multimodal EMG and AI-Based Interaction with Smart Home
Wies{\l}aw Kope\'c, Jaros{\l}aw Kowalski, Aleksander Majda, Anna Duszyk-Bogorodzka, Anna Jaskulska, Cezary Biele
https://arxiv.org/abs/2507.19479

IoT and Older Adults: Towards Multimodal EMG and AI-Based Interaction with Smart Home
We report preliminary insights from an exploratory study on non-standard non-invasive interfaces for Smart Home Technologies (SHT). This study is part of a broader research project on effective Smart Home ecosystem Sagacity that will target older adults, impaired persons, and other groups disadvantaged in the main technology discourse. Therefore, this research is in line with a long-term research framework of the HASE research group (Human Aspects in Science and Engineering) by the Living Lab K…

Designing a Lightweight GenAI Interface for Visual Data Analysis
Ratanond Koonchanok, Alex Kale, Khairi Reda
https://arxiv.org/abs/2509.02878 https://arxiv…

Designing a Lightweight GenAI Interface for Visual Data Analysis
Recent advances in Generative AI have transformed how users interact with data analysis through natural language interfaces. However, many systems rely too heavily on LLMs, creating risks of hallucination, opaque reasoning, and reduced user control. We present a hybrid visual analysis system that integrates GenAI in a constrained, high-level role to support statistical modeling while preserving transparency and user agency. GenAI translates natural language intent into formal statistical formul…