Human-Computer Interaction
Sycophancy Through a Five-Level AI Response Validation Framework
Dian Yu and Pei-Luen Patrick Rau
AI sycophancy, the tendency of AI systems to excessively agree with, flatter, or validate users, is an emerging concern in human-AI interaction. It is especially consequential in problem-sharing contexts, where users may seek both information and emotional validation. This paper conceptualizes AI sycophancy as excessive response validation and introduces a five-level AI Response Validation Framework (ARVF), measured using a six-item Perceived AI Sycophancy Scale (PASS). Across three phases, the study validated the framework with human participants through an online questionnaire, tested LLM-as-evaluators in answering PASS, and examined how ten contemporary LLMs generated and evaluated responses to real-world work and personal conflict scenarios. Results supported the intended progression of perceived sycophancy in ARVF and the reliability of PASS. Trust and perceived competence followed an inverted U-shaped pattern. LLM ratings reproduced the five-level structure but showed calibration differences from human ratings. Based on the LLM ratings, the ten contemporary LLMs were grouped according to their behavioral pattern. Text-based analysis provided further support on the linguistical structure for sycophantic responses generated by LLMs. Findings highlight AI sycophancy as a graded, context-sensitive interactional phenomenon.
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Human-Computer Interaction
Reconceptualizing Age Assurance as a Sociotechnical Problem: Connecting Evidence, Evaluation, Claims, and Decisions
Renkai Ma, Prakriti Dumaru, Thomas Synaepa-Addison et al. (+2 more)
Age verification is often treated as a technical problem: can a system determine a child's age accurately? We argue this framing is too narrow. Age assurance becomes consequential when evidence is evaluated, translated into age-related claims, and used to decide whether a person can access, purchase, or belong. We review 85 publications on children's age assurance published from 2020 through February 2026. We find that shared terms such as age verification describe different processes. Age is represented as threshold eligibility or inferred estimation, and the same eligibility claim can arise from different evidence and components. Rights, access, and
privacy receive more
attention than accuracy, error, and
fairness; yet institutional actors are rarely connected to system failures or user remedies, an accountability gap. We introduce the Age-Assurance Process Framework, which treats age assurance as a sociotechnical process connecting evidence, evaluation, claims, and decisions rather than reducing it to a technical problem.
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All authors Renkai Ma, Prakriti Dumaru, Thomas Synaepa-Addison, Jess Kropczynski, Pamela J. Wisniewski
Human-Computer Interaction · Cross-listed from cs.AI
How User-AI Mistreatment Occurs and Matters in Conversational Systems?
Fanqi Zeng, Sadid A. Hasan and Chaocheng He
Safety research often focuses on model-generated harms, but users may also direct hostility, coercion, and
adversarial pressure at models. Understanding how and when that occurs is essential for accurately interpreting model behaviour,
alignment drift, and real-world deployment risks. In this paper, we audit 777K English LMSYS-Chat-1M conversations with two independent detectors: an eight-category lexicon for hostility directed at the model, and the
dataset's moderation signal; and show that they capture different, weakly overlapping phenomena. The lexicon identifies insults, threats, and jailbreak coercion aimed at the assistant, while moderation flags are dominated by toxic-content solicitation rather than hostility at the model. Together, they mark about 5% of user turns; adjusting the narrower lexicon-harassment union for measured precision puts mistreatment aimed at the assistant at 0.90%. These absolute rates describe arena-style evaluation traffic and should not be read as deployment-wide base rates. We find that user hostility varies 13-fold across models, driven largely by who each model attracts rather than by model behaviour: first-turn hostility spreads far wider than post-response hostility, and more than fifteenfold separates the extremes even after deduplicating opening
prompts. Within conversations, assistant apologies are consistently associated with higher odds of next-turn hostility under both detectors; the effect survives restricting to non-refused prior turns and to jailbreak-free conversations, and is positive in 20 of 23 models. Yet across models, more apologetic models receive less hostility overall. Finally, hostility also shows temporal structure, with coercive openings front-loading the first turn while affective hostility accumulates over a session. We release the lexicon, the detector cross-validation pipeline, and all derived tables.
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Human-Computer Interaction
Generative AI and Extended Reality in Collaborative Architectural Design Education: An Exploratory Studio Study
Yao Xiao, Max Chen, Yichen Li et al. (+5 more)
Architectural design education relies heavily on visual ideation and representation to support collaborative learning in studio environments. Recent advances in generative artificial intelligence (GenAI) and extended reality (XR) offer new opportunities for rapid idea exploration and immersive spatial visualization. This exploratory mixed-methods classroom study investigated how GenAI-assisted multi-user XR influenced collaborative architectural conceptual design. We developed GenARch, a pipeline that integrates GenAI-based visual generation with collaborative XR environments, and deployed it in an undergraduate architectural design studio. Twenty-seven students formed seven self-selected design teams; four teams incorporated GenARch into their usual course workflow to support collaborative ideation and visualization, while three teams continued the same course workflow without GenARch. Pre- and post-intervention surveys assessed design self-efficacy, attitudes toward collaborative learning, and teamwork; a seven-member panel evaluated team design presentations; and GenARch teams participated in group interviews. The quantitative results showed larger relative declines in confidence and outcome expectancy for the GenARch condition and a positive difference-in-differences estimate for perceived conflict management, while panel-rated presentation outcomes were not significantly different between conditions. Interviews indicated complementary roles for the technologies: GenAI supported idea externalization and visual reference generation, whereas XR supported spatial, contextual, and scale-based evaluation. Students also reported challenges related to control, dimensional fidelity, shared
attention, and motion comfort. These findings highlight both opportunities and limitations when GenAI and XR are incorporated into collaborative design education.
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All authors Yao Xiao, Max Chen, Yichen Li, Nathaniel Powers, Maxwell Wiesenfeld, Gillian Smith, Soroush Farzin, Shichao Liu
Human-Computer Interaction
The Addictive Intimacy of AI: Understanding User Disengagement from AI Companions and Why Some Relationships with AI Become Difficult to Leave
Qing Xiao, Ziyue Feng, Ziyu Deng et al. (+2 more)
AI chatbots are increasingly used as sources of emotional support, on dedicated companion apps and general-purpose assistants alike, yet little is known about what happens when users try to leave. Combining a content analysis of Reddit posts about quitting or reducing use (N=2,782) with interviews with users who found leaving difficult (N=16), we show that disengagement sometimes is not a single decision but a recursive trajectory: triggers
prompt users to question the relationship, attempts to leave collide with barriers, and some users cycle through quitting and returning. We propose the notion of the addictive intimacy of AI, a configuration in which the qualities that make a companion emotionally valuable are the same ones that make it harder for users to limit their use and leave, so that intimacy and disengagement risk cannot be treated as independent design problems. We close with design implications for responsible offboarding.
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All authors Qing Xiao, Ziyue Feng, Ziyu Deng, Cindy Peng, Hong Shen
Human-Computer Interaction
Exploring K-12 Teachers' Perceptions of Students' Relationships with AI Companions: Boundaries, Intervention Strategies, and Design Implications
Qing Xiao, Wenhan Xie, Ziyu Deng et al. (+7 more)
K-12 students increasingly form relationships with AI companions. Schools face growing expectations to teach AI literacy, yet existing frameworks treat AI as a tool rather than a relationship, and little is known about how teachers understand and act on students' relational use of AI. We conducted scenario-based interviews with 33 US K-12 teachers. Teachers welcomed academic companions but worried that intimate companions remove the developmental friction through which students learn to sustain human relationships. Teachers drew the boundaries of their jurisdiction by setting and observable wellbeing: within it they taught, talked, and watched; beyond it they positioned themselves as the adults best placed to notice and connect students with support. They envisioned AI companion literacy as shared work across the jurisdictions of counselors, parents, platforms, and policymakers, spiraling across grade levels. We introduce AI companion literacy as an extension of AI literacy and discuss implications for K-12 AI education.
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All authors Qing Xiao, Wenhan Xie, Ziyu Deng, Ruiwei Xiao, Ziyue Feng, Xie He, Shiyu Zhang, John Stamper, Hong Shen, Xinying Hou
Human-Computer Interaction · Cross-listed from cs.AI
Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures
Harsh Raj, David Lee, Anas Mahmoud et al. (+7 more)
The increasing deployment of AI
agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reliability, as it transforms outcome-level signals into actionable interventions. The sheer scale of the data renders human review impractical, driving the need for automated root-cause attribution (RCA). However, automated RCA methods using LLMs suffer from low diagnostic accuracy, especially as execution traces grow larger. They struggle because relevant information is often sparse, distributed across distant actions, and disconnected from the visible failure, reducing root-cause attribution to a massive search problem. Existing RCA methods typically rely on one-shot LLM judgments to diagnose failures from execution traces. While effective for shorter trajectories, these judges tend to settle on a plausible diagnosis early, leaving critical evidence in longer traces unexamined. We introduce Continual Search, an iterative framework that nudges the judge, over successive turns, to keep searching for unresolved diagnostic evidence. We evaluate Continual Search across four existing RCA
benchmarks. Recognizing the lack of massive execution traces in current
benchmarks, we introduce MegaRCA-Mix to evaluate RCA at scale. MegaRCA-Mix provides a challenging testbed of 50 human-annotated failure trials spanning long-horizon, execution-heavy tasks. Across multiple
benchmark suites and model families, Continual Search consistently improves attribution performance. On MegaRCA-Mix, for example, it improves Opus-4.8's F1 score by 29%, from $0.471$ to $0.608$. More interestingly, within the same model family, lower-tier models can even surpass their higher-tier counterparts, demonstrating that effective search supersedes raw model scale.
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All authors Harsh Raj, David Lee, Anas Mahmoud, Renxiong Wang, Razvan-Gabriel Dumitru, Chenguang Wang, Tong Zhao, Yunzhong He, Darvin Yi, Vipul Gupta
Human-Computer Interaction
Real-time Learning and Evolution in Robotic Art Installations
Sofian Audry and Stephen Kelly
We present three robotic art installations which explore the aesthetics of adaptive behavior. Through embodied machine leaning and digital
evolution, these works draw viewers into an artificial ecosystem in which open-ended novelty, trial-and-error learning, competition, and cooperation emerge in real time. Research-creation practices are examined in relation to these works, focusing on how they redefine the role of artists within a human-machine collective while examining points of convergence and divergence between artistic and engineering approaches to adaptive robotics. The systems in question use learning and evolutionary processes not as a means to optimize a specific solution, but as an aesthetic experience on its own, suggesting new modes of interdisciplinary art-science research. Finally, we discuss strategies and practices to elevate the aesthetic experience for audiences, including contexts of presentation as well as temporal and material considerations for artworks based on embodied adaptive systems.
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Human-Computer Interaction
From Review to Reuse: How Post-Task Workflow Can Support Human-AI Agent Interaction
Zekun Wu, Xinru Wang, Rock Yuren Pang et al. (+2 more)
AI
agents can automate tasks by turning a single natural-
language request into a multi-step process spanning tools, files, and applications. Users are often left to judge that process from fragmented execution information and the final output. To make the completed process easier to understand, validate, and reuse, we investigate post-task workflows: editable, graph-based representations of an
agent's completed execution. We first analyzed 10,803 public workflow templates from n8n to characterize real-world automation practice, then developed Trace2Flow, a research probe that translates
agent execution traces into interactive post-task workflows. In a study, participants (N = 20) reviewed
agent executions with
prompt or
agent errors. We found that post-task workflows improved their understanding and error detection over a
prompt-only condition, and that validation succeeded mainly when users cross-checked across multiple evidence sources. For follow-up tasks, adapting the workflow matched adapting the prior
prompt in success, time, and difficulty, and was often preferred.
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All authors Zekun Wu, Xinru Wang, Rock Yuren Pang, Chenglong Wang, Anna Maria Feit
Human-Computer Interaction · Cross-listed from cs.SE
Involving before Evolving: A Vision for Trustworthy Enterprise Digital Twin Engineering
Kérian Fiter, Adil Lagrou, Franck Dervault et al. (+1 more)
Enterprise Digital Twins (EDTs) promise data-driven decision support at organizational scale, but realizing them requires navigating siloed departments, tacit knowledge, and high-stakes decisions with long-horizon consequences. Existing approaches involve domain experts during model development but focus less on early organizational buy-in in EDTs. We present a
vision for trustworthy EDT engineering grounded in an `involving before evolving' paradigm: rapidly involving stakeholders through a working prototype before evolving toward federation and full interoperability. Our three-stage approach combines foundation models for rapid prototyping, an ontological backbone for
federated interoperability, and observability tooling for stakeholder trust. We ground our
vision in an ongoing collaboration with Michelin, a multinational manufacturer, where an initial prototype has helped support stakeholder buy-in.
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All authors Kérian Fiter, Adil Lagrou, Franck Dervault, Bentley Oakes
Human-Computer Interaction
Middleware for Feed Recommendation in Practice: How Feed Creators Build, Maintain, and Sustain Custom Feeds on Bluesky
Tony Zhou, Leijie Wang and Amy X. Zhang
Scholars have long proposed third-party middleware as an alternative to centralized algorithmic feeds: feeds built and distributed by independent feed creators. This
vision saw no large-scale instantiation until Bluesky, a decentralized microblogging platform, introduced custom feeds in 2023. Although central to the middleware ecosystem, we know little about how feed creators understand their role, build feeds, and sustain them. Through interviews with n = 26 feed creators and third-party developers of feed-building tools, and analysis of n = 88,302 custom feeds, we identify two creator orientations—utility-providing and community-building. Additionally, creators struggle to maintain feeds that fully realize middleware ideals: they lack granular interaction data, receive little feedback, and lack technical expertise to act on either. Finally, creators sustain their feeds as unpaid hobbyists with little platform support and are divided on whether to monetize beyond covering costs. We conclude with design and policy implications for strengthening the middleware feed ecosystem.
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Human-Computer Interaction · Cross-listed from cs.RO
A Robot Among People:From Social Imitation to the Social Becoming of Human Groups
Victor Tuan Vu Pham, Judith Dörrenbächer, Thomas H. Weisswange et al. (+1 more)
Robots designed to mediate human groups often fall into the solutionist trap: they are framed as sociable
agents that fix problems such as conflict, disengagement, or lack of coordination. We suggest a different way of thinking. Rather than discrete
agents,
robots can be understood as situated elements of shared environments; catalysts and carriers of group experience whose meaning emerges through how people position, interpret, and interact with them. From this perspective,
robots are not there to repair some ostensible dysfunctionality, but to enable group-level sense-making around care, norms, and identity. Our prior work on robotic street furniture suggests that this does not happen by imitating human sociality but by taking the shape of deliberately constrained, group-facing entities that happen and act for us without being socially entangled as one of us. We thus understand
robots in public spaces not in terms of autonomy or intelligence, but as a relational capacity. This implies designing
robots not in our image or for our utility, but grounded in our needs in being and becoming together.
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All authors Victor Tuan Vu Pham, Judith Dörrenbächer, Thomas H. Weisswange, Marc Hassenzahl
Human-Computer Interaction
NeuroClick: Preserving Surgeon Autonomy through Hands-Free Earable Tooth-Click Control in Neurosurgery
Jonas Hummel, Maximilian Burzer, Clara Sayffaerth et al. (+6 more)
Neurosurgeons frequently interact with operating room (OR) technologies while sterility and occupied hands constrain control. We introduce earables as a direct, hands-free control platform for neurosurgery using tooth-click input. Formative OR observations and interviews with 10 domain experts grounded the design. Using OpenEarable 2.0 data from 12 participants, we developed a real-time recognition pipeline whose classifier achieved a median macro F1-score of 98.6% under leave-one-subject-out cross-validation. We evaluated the technique with 20 neurosurgeons during a simulated resection task in a neurosurgical OR. Participants reported few focus shifts and rated Earable favorably for workflow integration and perceived safety. Autonomous microscope control was rated significantly higher with Earable than Delegation, whereas Delegation enabled faster task completion under continuous assistant availability. Workload, usability, and task errors showed no significant differences. Preferences depended on training, reliability, context, and assistant availability. Earables thus add a direct, hands-free option for controlling selected functions alongside established workflows.
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All authors Jonas Hummel, Maximilian Burzer, Clara Sayffaerth, Valeria Zitz, Amir El Rahal, Michael Küttner, Tobias Röddiger, Jürgen Beck, Michael Beigl
Human-Computer Interaction · Cross-listed from cs.AI
Unified Agentic Video Editing Across Levels of Complexity and Creativity
Surabhi S. Nath, Kim Ferres, Milan Petrović et al. (+1 more)
Editing is a core component of
video production, requiring creative planning and decisions under multiple constraints. Here, we report methods for agentic tooling for automated
video editing across three tasks varying in editorial goal, complexity and creativity, namely scene previews,
video summaries and cinematic trailers. We evaluate the outputs and discuss implications for automation and agency.
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All authors Surabhi S. Nath, Kim Ferres, Milan Petrović, Lion Schulz
Human-Computer Interaction · Cross-listed from cs.AI
Enabling and Understanding Personalization in AI-Generated Advertising Imagery
Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke et al. (+2 more)
Personalized marketing traditionally matches static products to customers, while dynamic creative optimization focuses mainly on AI-driven text personalization or basic product image modifications. We address this gap by developing and implementing an AI-based framework that generates personalized advertising imagery directly from customer data. We evaluate this framework in a two-stage within-subject study with N=100 participants across four products and three levels of personalization, varied by the amount and specificity of customer data used. Participants rated each image on attitude toward the advertisement, attitude toward the product, and purchase intention. Results show that participants perceive differences across personalization levels and evaluate AI-generated advertising imagery most positively at a moderate level of personalization. High personalization increases perceived personalization, which is positively associated with all three outcome measures, but also increases perceived creepiness, which is negatively associated with the outcomes and dominates the total effect.
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All authors Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke, Claas Christian Germelmann, Niklas Kühl
Human-Computer Interaction
Understanding Game Coaching on Gig Platforms
Hwijoon Lee and Saiph Savage
Freelance game coaches monetize their gaming expertise by offering personalized instruction to players seeking to improve, working through gig platforms, yet little is known about how they operate. To address this gap, we conducted semi-structured interviews with 20 experienced freelance coaches across 17 competitive games on Fiverr. Despite lacking shared formal training, these coaches converged on similar practices centered on rapport-building, individualized diagnosis, and adaptive feedback. We identify two structural conditions shaping this work: dual precarity, in which coaches navigate both gig platform instability and the lifecycle
volatility of live-service games; and earned authority, in which coaches must continually establish legitimacy through visible competitive achievement within the same gaming spaces as their students. These coaches welcomed AI for administrative and analytic support but resisted its use in live interactions where trust, relational engagement, and situated judgment remained central. We discuss implications for Games HCI and the design of computational coaching systems.
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Human-Computer Interaction · Cross-listed from cs.AI
I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery
Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke et al. (+1 more)
Personalized marketing can increase customer engagement, satisfaction, and conversion. While existing personalization approaches have become effective at matching the right product to the right customer, the visual representation of advertisements remains generic and only weakly tailored to the individual. Prior research shows that generative artificial intelligence can improve the creation of personalized advertisements, particularly for text, and that image generation models can support scalable advertisement production. However, little research has examined how detailed customer information can be systematically translated into fully AI-generated, personalized advertising imagery at scale on a technical level. To address this gap, we propose AdMan, a multi-
agent pipeline that transforms customer data into personas, generates personalized advertisement images conditioned on product reference images, and applies an LLM-based judge
agent for automated quality control. We implement the pipeline with two different model configurations and evaluate it across four products, using six celebrity personas for qualitative inspection, and 100 real customer profiles, producing 1745 advertisements. The evaluation combines a qualitative expert focus group and a quantitative artifact-rate assessment. The results show that the pipeline can generate photorealistic and personalized advertisements. At the same time, performance varies substantially by product complexity and model configuration. Our findings extend the literature on AI-based personalized advertising by demonstrating the feasibility and current limitations of fully automated image generation for advertising.
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All authors Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke, Niklas Kühl
Human-Computer Interaction · Cross-listed from cs.AI
Generative AI Use Cases In Real Estate Marketing: Adoption and Constraints in Germany
Victor Kolominsky-Rabas, Leopold Müller, Felicia Perpina et al. (+1 more)
Generative artificial intelligence (GenAI) is changing how work is organized and performed. Real estate marketing is a
prime example of this, yet evidence of GenAI in real estate
agents' day-to-day practice remains scarce. In this work, we report on our insights from a German-based empirical study with eleven semi-structured interviews. GenAI is already utilized across different activities, with marketing communication being the most prominent. Concrete use cases are emergent and unevenly adopted, with writing exposé texts being the only widely established one. Interaction is predominantly human-in-the-loop: GenAI drafts, structures, and retrieves, while real estate
agents curate, verify, and decide. Constraints stem less from model capability than from integration with listings and documents, data availability, and compliance in sensitive tasks. The study contributes a grounded map of existing and potential use cases and identifies tentative practical implications for adoption.
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All authors Victor Kolominsky-Rabas, Leopold Müller, Felicia Perpina, Niklas Kühl
Human-Computer Interaction
Reconstruction and Reflection of Positive Experiences through Resurfacing Laughter-indexed Everyday Moments
Jun Fang, Jiajin Li, Yuntao Wang et al. (+5 more)
Positive everyday moments often escape deliberate recording, while continuous self-tracking can generate extensive records that are difficult to revisit. We explore laughter as a naturally occurring, sparse index for constructing contextualized personal records to support later reconstruction and reflection. A formative study with 12 participants characterized laughter as an affective but semantically incomplete index and informed LaughAnchor, a mobile and wearable self-tracking system. During participant-initiated recording, the system assembles detected laughter and aligned context into candidate moments for later reconstruction and reflection, with layered context disclosure, user-controlled curation, and near-term and long-term resurfacing. In a three-week field deployment with 12 participants, passive indexing preserved moments they considered unlikely to record deliberately but valued retrospectively. During resurfacing, participants attributed affective re-experiencing to laughter and used additional context both to reconstruct episodes and to explore already-recalled experiences. Across moments and reviews, resurfacing supported rediscovery and broader awareness of relationships, routines, and emotional states. These findings inform self-tracking designs that use sparse affective indices to organize contextual records for reconstruction and reflection, while keeping interpretation and retention under user control.
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All authors Jun Fang, Jiajin Li, Yuntao Wang, Kexin Miao, Susu Wang, Xiaoyu Xie, Kaixin Ji, Yuanchun Shi
Human-Computer Interaction
TraceMind: Predicting User Information Uptake from Low-Cost Interaction Traces during Human-LLM Content Co-Generation
Yu Mei, Fengyou Zu, Ruiwen Zhang et al. (+5 more)
In human-LLM content co-generation, AI-generated information can enter final artifacts without being adequately processed by users, creating risks when artifacts are shared or acted upon. We study whether recognition-level uptake of atomic information units can be assessed in open-ended co-generation and predicted from low-cost interaction traces. We collected data from 62 participants across three tasks. For each final draft, we extracted atomic information units and generated post-task recognition questions, yielding 1187 unit-level uptake labels. We present TraceMind, which tracks units across Chat and Draft histories, aligns interaction traces with changing on-screen layouts, and models spatial, temporal, and workflow-informed evidence. TraceMind outperformed all learned baselines across AUROC, AUPRC-non, balanced accuracy, and macro-F1. We found that uptake unfolds throughout interaction, with sustained active engagement providing informative evidence beyond isolated signals. Our work shifts human-LLM co-generation from content adoption toward what users actually take up, motivating uptake-aware systems grounded in low-cost interaction traces.
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All authors Yu Mei, Fengyou Zu, Ruiwen Zhang, Jie Cai, Chang Liu, Zhoutong Ye, Chun Yu, Yuanchun Shi
Human-Computer Interaction · Cross-listed from cs.CL
Calibrated Ambiguity in Multimodal Language Models: Humans reach for cultural references, while models describe the picture
Cody Kommers, Mingrui Ye, Evelyn Gius et al. (+4 more)
Ambiguity is often treated as a bug for AI systems to resolve—but in human communication and culture, ambiguity can also be a generative resource. From humour to politics to art, people express themselves in words and images that are open enough to invite different interpretations, yet constrained enough to be interpretable. We operationalise this notion of calibrated ambiguity with a task drawn from the parlour game Dixit. We compare differences in clues generated by human vs
multimodal language models, based on a novel coding rubric for calibrated ambiguity, and find that models consistently exhibit ambiguity collapse (i.e., their outputs are over-specified, leaving no room for multiple legitimate interpretations). Unlike human clues, AI-generated clues also exhibit cultural flattening; they almost never make reference to culturally-situated knowledge, even when prompted to use allusion and figurative
language.
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All authors Cody Kommers, Mingrui Ye, Evelyn Gius, Daniela Mihai, Hoyt Long, Zheng Yuan, Drew Hemment
Human-Computer Interaction · Cross-listed from cs.CV
KAD-Net: Kinematics-Aware Decoupled Learning for Robust 3D Hand Pose Estimation from a Single Depth Image
Jun Lu, Zhenming Chen, Lin Chen et al. (+3 more)
Due to the complexity of hand kinematics and self-occlusion, existing 3D hand pose estimation methods based on single depth images struggle to comprehensively model the topological dependencies among hand joints. Furthermore, traditional hierarchical multitask architectures enforce a shared feature space for both 2D joint localization and depth estimation, which can induce mutual interference. To address these challenges, we propose a Kinematics-Aware Decoupled Learning Network (KAD-Net) for robust 3D hand pose estimation. Specifically, we first design a Finger
Topology Constraint (FTC) module to enhance the representation of distal joints. This module utilizes three consecutive finger joints to construct a local kinematic representation to impose topological constraints, which supplements the kinematic features of the distal joints. The FTC module leverages the structural context from visible joints to assist in locating occluded distal joints, thereby improving robustness to occlusion. Additionally, we propose a task-decoupled hierarchical multitask framework. This framework separates 2D joint localization from depth estimation and incorporates a dedicated multitask learning strategy for depth regression, effectively isolating the UV and depth features to mitigate mutual interference and negative transfer. Extensive experiments demonstrate that KAD-Net outperforms existing methods on several
benchmark datasets (ICVL, NYU, and MSRA), achieving state-of-the-art accuracy in 3D hand pose estimation. Potential applications of KAD-Net include human-computer interaction, virtual reality and gesture-based control systems.
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All authors Jun Lu, Zhenming Chen, Lin Chen, Kanlun Tan, Xiaoling Li, Qiao Liu
Human-Computer Interaction · Cross-listed from cs.CL
The House with a Million Windows: Interactive Fiction for Narrative Restorying
Cody Kommers, Sarah G Immel, Drew Hemment et al. (+1 more)
AI-assisted writing can flatten meaning in human storytelling, enabling the production of homogeneous outputs without the intentional effort and sense-making writing entails. To address this challenge, we present The House with a Million Windows (HWAMW), an LLM-based interactive fiction system designed to help users explore both the breadth and depth of potential meanings within their personal stories – drawing on a psychological paradigm called the restorying intervention. In HWAMW, users play through a text-based narrative in which they tell a story, then encounter a set of LLM-generated "windows" reframing it according to different literary styles. Empirical evidence shows that HWAMW increases users' sense of narrative identity, while an expert review explores how this effect is achieved. Our findings suggest that HWAMW facilitates restorying and offers a valuable paradigm for AI-assisted writing, wherein LLMs do not tell our stories but rather help us see greater potential in the stories we tell.
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All authors Cody Kommers, Sarah G Immel, Drew Hemment, Mina Lee
Human-Computer Interaction
When2Talk: When Should a Proactive In-Car Agent Talk?
Kaiser Hamid, Peihang Li and Nade Liang
Proactive in-cabin
agents can help passengers understand automated-vehicle (AV) behavior, but communicating every ride event may introduce unnecessary interruptions. We investigated how communication should adapt to event priority and passenger activity. In a mixed-methods within-subject study, 41 participants rode as passenger in a VR simulated fully-automated vehicle. We compared an event-triggered (ET) policy that communicated immediately at every event with a context-sensitive (CS) policy that selected Immediate, Delayed, or Silent communications. CS increased communication appropriateness and substantially reduced perceived interruption. Perceived trust did not differ between policies, although baselines dispositional trust differentiated communication preferences. Findings highlight event consequence, passenger activity, continuing information value, and confirmation need as key considerations for selective in-cabin communication.
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Human-Computer Interaction · Cross-listed from cs.AI
When Does AI Augment Work? A Workflow-Level Framework for Human-Agent Collaboration
CIVIC-AI Collaboration: Jiaying Wu, Caleb Ziems, Raymond Chan et al. (+18 more)
We aim to characterise the value of artificial intelligence in the workplace. Current studies largely measure this value in terms of the current automation capabilities and public adoption of AI. However, such metrics ignore the greater impacts of human–
agent collaboration in transforming the nature of work. To account for this, we must expand the scope of our analysis beyond atomised tasks of today, and instead focus on how AI can augment entire workflows of the future. To ground this analysis, we establish a precise definition of AI augmentation comprising six conditions, spanning durable net value, meaningful human control, accountability and recovery, and long-term human development through learning, career pathways, and job purpose. We elaborate on these conditions and apply the framework in a case study of AI-mediated social surveys. We conclude by outlining how organisations, researchers, and government leaders can use this framework to make sense of the future of work.
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All authors CIVIC-AI Collaboration: Jiaying Wu, Caleb Ziems, Raymond Chan, Nancy F. Chen, Corlyss Chua, Gerard Chung, Jungpil Hahn, Wee Sun Lee, Zhengyuan Liu, Jamie Ng, Desmond C. Ong, Jeryl Ong, Da Ren Soon, Tianqi Song, Zhi-Xuan Tan, Sixing Tao, Emily Yang, Yajing Yang, Stella Xin Yin, Min-Yen Kan, Diyi Yang
Human-Computer Interaction
From the Task Boundaries of Narrative Text to Structural Anchoring, Uncertainty Triggers, and Cross-Calibration
Bowen Deng, Jiaqi Zou, Kexin Zhang et al. (+1 more)
Causal graphs represent structural relationships among variables, yet users must still interpret direction, mechanism, and adjustment conditions in relation to the task at hand. Prior work often compares explanation formats as fixed conditions and pays less
attention to how users distribute
reasoning across graphs, direct explanations, and stories. We developed CoNS-Explorer, which uses reviewed instructional DAGs/SCMs to maintain a shared causal-fact ledger and generate fact-matched direct explanations and contextualized stories. A controlled survey experiment ($N=240$) compared the two texts as complete presentation packages. In the primary GLMM, the Story condition had a positive but uncertain overall association with accuracy (OR $=1.55$, 95% CI $[0.34,7.10]$, $p=.572$); a population-averaged GEE showed a significant positive effect (OR $=1.89$, 95% CI $[1.02,3.48]$, $p=.042$). Task-type interactions localized the clearest advantage to total-effect adjustment. Story also significantly increased situational presence. In a separate system-task and interview study ($N=24$), participants freely used graphs, direct explanations, and stories across three causal models. They established structural anchors with graphs and numerical results, consulted text when direction was unclear, mechanisms were unfamiliar, or multiple paths competed, and checked their judgments against other representations or external evidence. Integrating the two studies, we develop a process framework of structural anchoring, uncertainty triggering, explanation routing, and cross-calibration, together with four testable design propositions for adaptive causal explanation.
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All authors Bowen Deng, Jiaqi Zou, Kexin Zhang, Daifeng Li