Large language models (LLMs) are increasingly evolving from conversational assistants into agents capable of operating external digital environments. Graphical user interface (GUI) agents play an important role in this transition, as many real-world workflows remain accessible only through user-facing software interfaces. However, despite recent progress on general computer-use benchmarks, domain-specific professional standard operating procedures (SOPs) remain challenging for GUI agents because they often involve implicit domain knowledge, software-specific conventions, and task-level verification requirements. We introduce OmegaUse-SOP, a human-in-the-loop SOP Engineering system for transforming human demonstrations of professional computer use into reusable SOP skills for GUI agents. Analogous to prompt engineering, SOP Engineering iteratively refines demonstrations, execution rules, and domain knowledge to convert professional SOPs into reusable GUI-agent skills. OmegaUse-SOP consists of four modules: Observe, Reason, Configure, and Execute. Together, these modules record expert operations as multimodal GUI traces, abstract low-level events into semantic step-level instructions, incorporate domain rules and task-specific parameters, and execute the resulting skills in live GUI environments through step-wise grounding, action generation, and verification. To demonstrate its effectiveness, we collaborate with a power-sector client and test OmegaUse-SOP on photovoltaic simulation workflows in PVsyst 7.2. The results suggest that OmegaUse-SOP can improve GUI-agent reliability on professional SOP tasks, highlighting a practical path toward deploying GUI agents in domain-specific professional software environments.
Yixiong Xiao, Lang An, Hucheng Yang et al.· 0 citations
Large language models (LLMs) are increasingly embedded in organizational work, yet their errors often pass human review. Prior research locates such failures in users' capability to review LLM output or their engagement in doing so. We develop an alternative, retrieval-based account of human oversight and posit that error detection is more effective when oversight-relevant information is accessible to users at the moment of review. Across two randomized lab-in-the-field experiments with 640 customer-facing employees, we show that self-generated explanations improve error detection and strengthen recall of verification-relevant reasoning, while cues that reactivate such reasoning help sustain detection under repeated LLM use. Theoretically, we identify information retrievability as a distinct precondition for effective oversight and specify generative encoding and cue-supported reactivation as mechanisms that build and sustain it. Practically, lightweight onboarding self-explanations and daily retrieval cues can make human oversight more resilient as LLM use becomes routine.
Xinyu Fu, Narayan Ramasubbu, Dennis Galletta· 0 citations
AI assistants often collaborate by proposing candidate edits, plans, or designs that users evaluate before adoption. Existing assistance methods focus on proposal quality or user-goal inference, often assuming that the user can reliably evaluate any proposal, which can fail in practice because of bounded rationality. We study evaluability-aware proposal planning, where proposals serve both as task interventions and as probes for learning latent preferences and evaluation constraints, where the resulting belief updates then guide later proposals. We formalise this setting as ProSE, a hidden-parameter sequential assistance problem, and instantiate it with a KL-regularised bounded-rational binary response model in which acceptance trades off value gain against a distance-dependent evaluability penalty. Analysing the planning consequence of this likelihood reveals that likely accepted proposals and informative probes need not coincide, which explains why planners that only pursue acceptance systematically underperform. We operationalise ProSE with \textsc{ProSE-Plan}, a depth-2 Bayes-adaptive planner that scores proposals by possible responses and response-induced posterior beliefs. In controlled graph simulations, \textsc{ProSE-Plan} improves over evaluability-unaware and myopic baselines when evaluation cost is the bottleneck, and a probe-commit ablation confirms that our approach selects informative proposals that simpler methods miss. Our results thus identify user evaluability as a planning-relevant dimension of AI assistance, complementary to generation quality and preference inference.
Physical human-robot interaction requires yielding transiently to contact yet recovering the commanded reference under sustained load. Finite-stiffness impedance control retains a static deflection there, while predictive alternatives typically optimize a nonlinear robot or impedance model online. Operational-space cancellation instead exposes a translational error double integrator with a fixed transition matrix and a configuration-scheduled input map, making interaction a predictive quantity rather than a property re-derived per configuration. We build on it a compact offset-free interaction-error MPC for torque-controlled manipulators: a force-domain random-walk state estimates persistent interaction and model error, and a 30-variable convex QP maps the correction through the current task inertia while constraining the applied joint torque. Conditional results establish impedance equivalence of the unconstrained passive feedback, offset-free regulation at feasible frozen configurations, and quadratic stabilizability of the scheduled backbone. In a 1kHz MuJoCo simulation of a 7-DOF Franka FR3, the estimator cuts steady-state error under a repeated 15N step from 2.77mm to 0.042mm when added to the otherwise identical 100Hz MPC. A stiffness-and-damping-calibrated impedance baseline attains 2.59mm but briefly saturates and needs 3.3x the peak positive joint power. Adding ideal measured-force cancellation to that baseline gives 1.39mm, so constant-load rejection is not unique to MPC; the sensorless controller still reaches 0.042mm in the moving task, a 65x reduction without force sensing and without the baseline's saturation or power cost. Demonstrated in simulation under a shared actuator budget, the contribution is an efficient operational-space realization complementing rather than replacing broader interaction-control architectures.
Yongyan Cao, Jinshan Tang· 0 citations
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Community-based fact-checking systems, such as X's Community Notes program, offer a potentially scalable approach against online misinformation diffusion. However, their efficacy is substantially undermined by the delay of fact-check delivery. To address this challenge, we investigated the promise of organic user comments as rapid corrective signals by analyzing a large-scale dataset of over 2.2 million comments directed to 1,841 community fact-checked misleading posts. We developed a high-performance language model pipeline to identify ``fact-checking (FC) comments'', i.e., comments that correct source posts with reasoning or evidence (91% accuracy). Using this pipeline, we find that 99.4% of misleading posts receive their first FC comments before the official community note is created. Notably, the median time to the initial FC comment is only 0.1 hours, while the creation of community notes has a median latency of 9.7 hours since the publication of misleading posts. Additionally, compared to random posts, those misleading posts with displayed notes have significantly more FC comments. This suggests that FC comments are a unique pattern for misleading posts. We further identified characteristics of FC comments' intensity and speed: (i) FC comments' volume is significantly associated with content richness and misinformation type, favoring multimedia content and missing-context scenarios; (ii) the speed of FC comments remains robust across diverse topics and emotional intensities. These results suggest that platforms could use FC comments as early-warning signals, and potential resource for complementing community notes or automated synthesis.
Shuning Zhang, Dai Shi, Yuwei Chuai et al.· 0 citations
Extended Reality (XR) provides immersive, interactive 3D experiences. To enable these experiences, the devices must track user motion so the system can respond to actions such as grabbing, looking at, or moving an object. However, motion tracking has raised privacy concerns since it records a person's motion patterns. These motion patterns have been studied extensively across various fields (i.e., gait identification and profiling) and have been shown to reveal sensitive information. With the adoption of XR, these patterns became easier to record and obtain than ever. This creates a fundamental privacy tension: motion tracking enables core XR functionality yet requires users to compromise their privacy. Prior systematization-of-knowledge (SoK) studies on XR privacy have examined the field broadly, with motion-related research distributed across several privacy domains rather than treated as a distinct area of study. However, XR motion privacy has gained significant momentum since the prior SoK, with the literature nearly quadrupling in size and thereby warranting a dedicated systematization of this topic. This SoK examines 134 relevant papers on privacy concerns in motion patterns recorded by XR headsets, including how adversaries can obtain users' motion patterns, the inferences they can draw from them, and methods for protecting users. Based on this review, we synthesize a taxonomy of motion modalities, representations, and inference risks; develop an XR motion threat model; systematize the attack and defense approaches in the XR motion literature; identify gaps in the literature; and provide guidelines for future studies evaluating motion privacy mechanisms. Together, our SoK clarifies the state of XR motion privacy and provides recommendations for future evaluations.
Azim Ibragimov, Alina Vasina, Uliana Polshcha et al.· 0 citations
For noisy real-world environments such as those in open public spaces, spoken dialogue systems for both autonomous robots and avatars should be carefully designed to provide enhanced speech signals. These signals can be used either for speech recognition or, in the case of an avatar system, transmitted as clean speech to a remote operator. This work proposes an audio system that can be used for both these scenarios and was demonstrated as a proof-of-concept at the 2025 World Expo in Osaka. The first scenario is an attentive listening system with the android ERICA, and the second is a conversation support system with mobile Teleco robots, with one of them acting as an avatar for a remote operator. Both systems feature multi-party conversation and use a single multi-channel microphone array. We describe how our audio system not only enhances the speech of multiple speakers in a noisy environment, but provides a form of spatial audio which allows for more immersiveness in avatar-based conversational interactions.
Divesh Lala, Yogeeswaran Muthukumaran, Vincent Fernandes et al.· 0 citations
Graphic designers often blend visual concepts to communicate multiple ideas within a single image, leveraging positive and negative space to create balance, emphasis, and aesthetic appeal. While computational methods have begun to support automatic concept blending, they largely overlook the role of spatial composition in the design. To address this gap, we present an automatic pipeline that explicitly applies positive and negative space throughout the blending process. Our approach first identifies plausible regions for concept integration by combining semantic reasoning from vision-language models with geometric constraints derived from real-world examples. Conditioned on these regions, the system generates blended compositions using a hybrid pixel-vector pipeline: diffusion-based inpainting produces a fast, coarse initialization, which is then refined through vector-based optimization at the point level to ensure structural coherence and balanced semantic expression. A multimodal agent orchestrates this process as a planner and evaluator, enabling iterative improvement and interpretable control. Through an evaluation using both baseline comparisons and a user study, we demonstrate greater expressiveness, creativity, and concept recognizability by effectively leveraging positive and negative space. We further demonstrate the generalizability of our approach across diverse applications, including controllable image and infographic generation.
Shishi Xiao, Adam J. Coscia, David H. Laidlaw· 0 citations
Immigrant-led nonprofit groups, particularly those operating in politically sensitive contexts, face exclusion from formal registries and digital platforms. This paper reports a three-phase mixed-methods study with Iranian immigrant nonprofit practitioners: 27 semi-structured interviews, a co-design session, and 7 evaluation and feedback interviews on a prototyped AI assistant, AMINA. Our findings highlight how legitimacy barriers, capacity gaps, and politically charged misinformation constrain nonprofit operations. We translate these insights into design goals for an inclusive nonprofit AI assistant: support for everyday group operations, recognition of informal nonprofit efforts, proactive countering of misinformation, and multilingual, accessible interaction. User evaluations show AMINAs potential to reduce reporting burdens and foster transparency through proactive reminders, and catalyze collaboration across dispersed networks. We contribute to CSCW and HCI by characterizing the cooperative work of transnational immigrant nonprofits, extending scholarship on informality and misinformation, and demonstrating how AI can act as a collaborative partner that strengthens, rather than displaces, the human connections at the core of nonprofit ecosystems, while also posing major risks.
Introduction: Clinical research informatics (CRI) platforms support biomedical discovery by integrating advanced computational tools into research workflows. Emerging technologies such as spatial omics and AI-enabled imaging expand research capabilities but introduce complex interfaces that increase cognitive burden and alter established analytical processes. Traditional usability frameworks identify general usability issues but often miss challenges specific to high-dimensional biomedical data. Methods: We conducted two complementary studies involving 39 participants to evaluate conventional usability heuristics and identify CRI-specific criteria. Study 1 included 19 undergraduates completing interactive tasks, and Study 2 involved 20 clinical professionals completing an asynchronous hierarchical task framework. Observational and interview data were analyzed using deductive coding based on standard usability heuristics and emerging CRI-specific themes. Results: Simultaneous presentation of complex data overlays and analytical tools overwhelmed users, particularly those with limited spatial-omics experience. Participants relied on trial-and-error exploration and struggled with unlabeled tools in data-rich environments. Feedback indicated that users benefit from phased onboarding, contextual guidance, and progressive feature introduction rather than immediate access to all functionality. Discussion: High-dimensional research platforms require domain-specific usability criteria beyond traditional frameworks. We propose three specialized heuristics: Active Parameter Transparency, Point-of-Use Guidance, and Phased Feature Disclosure. These heuristics help developers manage complexity, provide contextual support, and improve accessibility for multidisciplinary research teams.
Yulia A. Levites Strekalova, Rachel Liu Galvin, Jessica M. Ray et al.· 0 citations
Augmented reality (AR) facilitates human-robot collaboration (HRC) by enabling in-situ spatial visualizations of the robot and the joint task. However, in safety-critical HRC scenarios such as search-and-rescue, spatial visualizations may also reshape visual attention in ways that create competing situational awareness (SA) demands, potentially introducing new safety concerns. While prior AR-HRC work suggests potential benefits for SA, rigorous evaluations that jointly consider robot and environmental awareness across multiple levels of SA remain limited. We address this through a between-subjects study with 30 participants comparing custom AR and mobile interfaces presenting equivalent information, measuring robot and environmental SA with the Situation Awareness Global Assessment Technique (SAGAT) across all three levels, with concurrent eye tracking to identify the attentional mechanisms underlying any SA differences. Both interfaces achieved high usability; relative to the mobile baseline, AR improved perception-level awareness of the robot but yielded no gains in higher-level robot awareness or in environmental awareness at any level. Gaze analysis explained this: AR freed attention from the map, but that attention was re-invested in the conformal visuals rather than the physical environment. Freeing the eyes from a screen is not the same as directing them to the world, a distinction AR interfaces for safety-critical HRC must design around.
Zhehan Qu, Christian Fronk, Jaewoong Jeong et al.· 0 citations
Out-of-view guidance is well established in virtual and augmented reality, but its effectiveness may depend on the visual bandwidth available to the user. We test this under simulated prosthetic vision (SPV), where visual guidance must share the same sparse representation used to inspect the scene. Nineteen participants performed object search under two SPV conditions differing in electrode density and phosphene spread (10x10 and 20x20) and four guidance conditions (no guidance, visual, haptic, audio) all driven by the same horizontal target-offset variable. All three modalities reduced search time and head movement. The tested auditory and haptic cues produced approximately 25% faster overall search and 11-13% faster target acquisition than the visual cue, despite similarly direct orienting trajectories. The tested haptic and auditory cues also shortened post-acquisition search. Final head-target angular offset was reduced substantially more in the 10x10 SPV condition; there, all three cues also reduced vertical localization error by approximately 45-58% despite providing no elevation information. Under severe visual constraints, guidance performance depended on cue implementation and search stage.
Adyah Rastogi, Apurv Varshney, Tobias H\"ollerer et al.· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
