arXiv AI

Intelligent Interaction Techniques (IIxT) - Proposal

The paper proposes Intelligent Interaction Techniques (IIxTs), arguing that current interaction techniques (IxTs) used in graphical user interfaces are largely static and were only modestly updated for smartphones. It suggests developing more intelligent IxTs that allow users to mix modalities—such as text, voice, and gestures—within a single interaction, thereby extending beyond the typical chat‑window approach seen in many AI interfaces. The proposal calls for research into new IxTs and the supporting infrastructure needed to realize these intelligent, multimodal interactions.

arXiv AI
3d ago

The Power of Meta Access in the Software of the Future --- Demonstrated on LLM Chat UIs and Chat AI

The paper proposes ‘meta access’—a structured, permissioned view of an interface’s state—to enhance large language model chat UIs. It outlines a ladder of access levels, from feature tutoring to full orchestration, and demonstrates the concept with the Meta Chat prototype, which uses summaries, settings, and message links to enable reversible changes and a browsable tutorial library. Meta access aims to transform chat from a single conversation window into a control layer for the entire workspace while addressing risks such as grounding, privacy, and prompt injection.

By Julian Kunkel, Ali Doosthosseini
arXiv AI
Sep 10

AgentLens: Adaptive Visual Modalities for Human-Agent Interaction in Mobile GUI Agents

AgentLens is a mobile GUI agent that adapts its visual communication during task execution, offering Full UI, Partial UI, and GenUI modalities. It uses a Virtual Display to allow background operation while selectively overlaying visual information. In a study with 21 participants, 85.7% preferred AgentLens, which also scored highest on usability and adoption intent.

By Jeonghyeon Kim, Byeongjun Joung, Junwon Lee, Joohyung Lee, Taehoon Min, Sunjae Lee
arXiv AI
Oct 5

Automating the Application of HCI Principles: Skills for On-Demand UI Construction, the Human-AI Space to Think, and the Future of HCI

The article discusses how large language models can now generate user interfaces from natural‑language descriptions, and proposes a framework that turns HCI design knowledge into machine‑readable ‘skills’ that guide on‑demand UI construction. By treating the dialogue between user and AI as a shared cognitive workspace, these skills encode established HCI principles (e.g., Nielsen’s heuristics, WCAG criteria) as editable, version‑controlled artifacts. The authors outline a research agenda that envisions HCI moving from heuristic checklists to a machine‑executable, open‑source craft.

By Nathan Conklin, Miranda Capra, Chris North
arXiv AI
Sep 15

Personalizing Personal Health Interfaces: Co-Design with Generative AI

The paper explores how generative AI can lower the barrier to personalizing health dashboards by enabling users to co-design interfaces in Figma Make. In a study with 14 participants, redesigns of Google and Apple Health focused on personal context, future planning, and interactive experiences, though conversational AI designs tended toward chat-window conventions. AI facilitated the materialization of loosely articulated ideas, yet model defaults and generation latency influenced iteration, and the process highlighted interpretability and accountability over privacy, trust, and emotional safety.

By Karthik S. Bhat, Vidhi Shah, Vedika Agnihotri, Dong Whi Yoo, Koustuv Saha
arXiv Computation and Language
Nov 2

Hybrid Computer Use: A Unified Programmatic, Semantic, and Visual Interface for General Agents

Hybrid Computer Use (HCU) is a unified interface that enables general agents to interact with computers through three complementary channels: programmatic code and command-line tools, semantic accessibility (AX) features, and visual graphical user interfaces (GUI). The design emphasizes efficient AX interaction, batch execution, and AX diffs to reduce round trips, while a supplementary skill guides agents in selecting and coordinating these channels. On the OSWorld 2.0 benchmark, HCU outperforms other systems in both efficiency and task success, demonstrating the benefits of flexible, multi‑channel computer‑use interfaces.

By Haoqing Wang, Xiaoyi Zhang, Sicheng Fan, Zhexin Zhang, Xinjie Zhang, Jiahao Li, Bin Li, Yan Lu
arXiv AI
Sep 2

Are We There Yet? Assessing Computer-Use Agents for Blind Users' Accessible Interaction with Desktop Applications

The study evaluates computer-use agents (CUAs) for blind users by conducting a three‑week diary study with eight participants using the OLLA prototype. Across 1,258 commands in 12 desktop applications, GPT‑5 achieved the highest success rate of 52.5%, while analysis uncovered failures in grounding, planning, constraint‑tracking, and termination. Interviews highlighted additional needs beyond automation for blind users.

By Satwik Ram Kodandaram, Monalika Padma Reddy, Xiaojun Bi, Jiawei Zhou, I. V. Ramakrishnan, Vikas Ashok
arXiv AI
Sep 17

Affora: A Design System for Agent-Friendly Interfaces

Affora is a design system aimed at making software interfaces more readable by computer-use agents while still allowing designers visual freedom and maintaining familiar human workflows. The authors conducted three controlled studies on component implementations, visual variation, and interaction-design principles, using the results to create guidance from individual components to full sites, along with reusable implementations and executable checks. Evaluation on independently authored interfaces showed performance gains where Affora addressed existing deficits, with limited effects elsewhere, and a workflow case suggested reduced interaction cost.

By Jin Gao
arXiv Computation and Language
Nov 2

SimXAI: A Simple Drop-in Layer Increases Interaction Quality and Understanding in Conversational XAI

SimXAI is a lightweight, LLM‑centric conversational layer that replaces traditional intent recognition, dialogue management, and template‑based response modules in GUS‑style ConvXAI agents. By using a single tool‑calling LLM to interface with existing XAI resources, it provides a modular interface that improves function‑selection accuracy, faithfulness, and handling of context‑dependent requests. In a user study, substituting SimXAI for the conversational layer of the TalkToModel agent led to more accurate task completion in fewer turns, demonstrating enhanced conversational quality while preserving faithfulness.

By Van Bach Nguyen, Qianli Wang, Nils Feldhus, J\"org Schl\"otterer, Christin Seifert