The proliferation of smart home devices has increased the complexity of controlling and managing them, leading to user fatigue. In this context, large language models (LLMs) offer a promising solution by enabling natural-language interfaces for Internet of Things (IoT) control. However, existing LLM-based approaches suffer from unreliable and inefficient device control due to the non-deterministic nature of LLMs, high inference latency and cost, and limited personalization. To address these challenges, we present IoTGPT, an LLM-based smart home agent designed to execute IoT commands in a reliable, efficient, and personalized manner. Inspired by how humans manage complex tasks, IoTGPT decomposes user instructions into subtasks and memorizes them. By reusing learned subtasks, subsequent instructions can be processed more efficiently with fewer LLM calls, improving reliability and reducing both latency and cost. IoTGPT also supports fine-grained personalization by adapting individual subtasks to user preferences. Our evaluation demonstrates that IoTGPT outperforms baselines in accuracy, latency/cost, and personalization, while reducing user workload.

Mobile GUI Agents, AI agents capable of interacting with mobile applications on behalf of users, have the potential to transform human computer interaction. However, current evaluation practices for GUI agents face two fundamental limitations. First, they either rely on single path offline benchmarks or online live benchmarks. Offline benchmarks using static, single path annotated datasets unfairly penalize valid alternative actions, while online benchmarks suffer from poor scalability and reproducibility due to the dynamic and unpredictable nature of live evaluation. Second, existing benchmarks treat agents as monolithic black boxes, overlooking the contributions of individual components, which often leads to unfair comparisons or obscures key performance bottlenecks. To address these limitations, we present MobiBench, the first modular and multi path aware offline benchmarking framework for mobile GUI agents that enables high fidelity, scalable, and reproducible evaluation entirely in offline settings. Our experiments demonstrate that MobiBench achieves 94.72 percent agreement with human evaluators, on par with carefully engineered online benchmarks, while preserving the scalability and reproducibility of static offline benchmarks. Furthermore, our comprehensive module level analysis uncovers several key insights, including a systematic evaluation of diverse techniques used in mobile GUI agents, optimal module configurations across model scales, the inherent limitations of current LFMs, and actionable guidelines for designing more capable and cost efficient mobile agents.

Even with advances in single-cell batteries, mobile users still experience low battery anxiety. By analyzing 19,855 hours of user behavior, we propose MixMax, a heterogeneous battery system consisting of three complementary battery types tailored to minimizing low battery time. While the heterogeneous battery system offers an opportunity to simultaneously improve capacity and charging speed, one must face non-trivial challenges to design charge/discharge policies during runtime and determine the ratio of enclosed batteries. They are highly dependent on each other, which entails almost infinite candidates for the choice. MixMax simplifies this by reformulating the problem as an optimization problem, breaking it down into manageable sub-problems. However, MixMax still faces the challenge of catering to all users due to their diverse battery usage patterns. To address this, we introduce a customized MixMax that groups users based on their usage patterns and provides tailored battery solutions. In evaluating MixMax, we fabricate coin-cell batteries, develop a precise battery emulator using the fabricated batteries, and prototype MixMax on a real-world smartphone. Our evaluation shows that MixMax reduces low battery time by up to 24.6% without compromising capacity, volume, weight, or user behavior, and its customized version can further reduce it by up to 46.2%.

Large Foundation Models (LFMs) have unlocked new possibilities in human-computer interaction, particularly with the rise of mobile Graphical User Interface (GUI) Agents capable of interacting with mobile GUIs. These agents allow users to automate complex mobile tasks through simple natural language instructions. However, the inherent probabilistic nature of LFMs, coupled with the ambiguity and context-dependence of mobile tasks, makes LFM-based automation unreliable and prone to errors. To address this critical challenge, we introduce VeriSafe Agent (VSA)(1): a formal verification system that serves as a logically grounded safeguard for Mobile GUI Agents. VSA deterministically ensures that an agent's actions strictly align with user intent before executing the action. At its core, VSA introduces a novel autoformalization technique that translates natural language user instructions into a formally verifiable specification. This enables runtime, rule-based verification of agent's actions, detecting erroneous actions even before they take effect. To the best of our knowledge, VSA is the first attempt to bring the rigor of formal verification to GUI agents, bridging the gap between LFM-driven actions and formal software verification. We implement VSA using off-the-shelf LFM services (GPT-4o) and evaluate its performance on 300 user instructions across 18 widely used mobile apps. The results demonstrate that VSA achieves 94.33%-98.33% accuracy in verifying agent actions, outperforming existing LFM-based verification methods by 30.00%-16.33%, and increases the GUI agent's task completion rate by 90%-130%.

The growing trend of multi-device ownerships creates opportunities to use applications across devices. However, the current methods of app development/usage remain in the single-device paradigm, which is far below user expectations. For example, it is currently impossible for users to dynamically partition an existing app across different devices to utilize multiple surfaces. We introduce FLUID, a novel multi-device platform that supports simultaneous operation of multiple devices. FLUID aims to i) distribute the user interfaces (UIs) of a single app across multiple devices, ii) support unmodified legacy apps without extra engineering, and iii) support numerous apps with customized UIs. Previous approaches, like screen mirroring and app migration, do not satisfy those goals altogether. However, FLUID is designed to satisfy the goals. It can efficiently deploy UI objects to different devices by identifying only UI states necessary for accurate rendering. And FLUID can execute the distributed UI objects by supporting cross-device method invocations transparently and synchronizing the replicated UIs across devices. Furthermore, FLUID automatically handles unexpected events that may degrade its usability by efficiently maintaining the distributed UIs up to date. Our evaluation using 20 legacy apps shows that FLUID can transparently support numerous apps and is fast enough for interactive use.

The rapid growth of the Internet of Things (IoT) in shared spaces has led to an increasing demand for sharing IoT devices among multiple users. Yet, existing IoT platforms often fall short by offering an all-or-nothing approach to access control, not only posing security risks but also inhibiting the growth of the shared IoT ecosystem. This paper introduces FLUID-IoT, a framework that enables flexible and granular multi-user access control, even down to the User Interface (UI) component level. Leveraging a multi-user UI distribution technique, FLUID-IoT transforms existing IoT apps into centralized hubs that selectively distribute UI components to users based on their permission levels. Our performance evaluation, encompassing coverage, latency, and memory consumption, affirm that FLUID-IoT can be seamlessly integrated with existing IoT platforms and offers adequate performance for daily IoT scenarios. An in-lab user study further supports that the framework is intuitive and user-friendly, requiring minimal training for efficient utilization.

Despite the physical advance of an existing single-cell battery system, mobile users are still suffering from low battery anxiety. With a careful analysis of users' battery usage behavior collected for 19,855 hours, we propose a heterogeneous battery system, MixMax, consisting of three complementary battery types tailored to minimizing the low battery time. While composing a heterogeneous battery system opens up a chance to simultaneously improve the capacity and the charging speed, one must face non-trivial challenges to determine the ratio of enclosed batteries and charge/discharge policies during the run-time. They are highly dependent on each other, which entails almost infinite candidates for the choice. MixMax gracefully unwinds the dependencies as it formulates the decision-making problem into an optimization problem and decomposes it into multiple sub-problems instead. To evaluate MixMax, we fabricate coin-cell batteries and experiment with them to model an accurate battery emulator which sophisticatedly reproduces the dynamics of battery systems. Our experimental results demonstrate that MixMax can reduce the low battery time by up to 24.6% without compromising capacity, volume, weight, and more importantly, users' battery usage behavior. In addition, we prototype MixMax on a smartphone, presenting the practicality of MixMax on mobile systems.

Online meetings are indispensable in collaborative remote work environments, but they are vulnerable to distractions due to their distributed and location-agnostic nature. While distraction often leads to a decrease in online meeting quality due to loss of engagement and context, natural multitasking has positive tradeoff effects, such as increased productivity within a given time unit. In this study, we investigate the impact of real-time transcriptions (i.e., full-transcripts, summaries, and keywords) as a solution to help facilitate online meetings during distracting moments while still preserving multitasking behaviors. Through two rounds of controlled user studies, we qualitatively and quantitatively show that people can better catch up with the meeting flow and feel less interfered with when using real-time transcriptions. The benefits of real-time transcriptions were more pronounced after distracting activities. Furthermore, we reveal additional impacts of real-time transcriptions (e.g., supporting recalling contents) and suggest design implications for future online meeting platforms where these could be adaptively provided to users with different purposes.

The advent of large language models (LLMs) has opened up new opportunities in the field of mobile task automation. Their superior language understanding and reasoning capabilities allow users to automate complex and repetitive tasks. However, due to the inherent unreliability and high operational cost of LLMs, their practical applicability is quite limited. To address these issues, this paper introduces MobileGPT, an innovative LLM-based mobile task automator equipped with a human-like app memory. MobileGPT emulates the cognitive process of humans interacting with a mobile app – explore, select, derive, and recall. This approach allows for a more precise and efficient learning of a task's procedure by breaking it down into smaller, modular sub-tasks that can be re-used, re-arranged, and adapted for various objectives. We implement MobileGPT using online LLMs services (GPT-3.5 and GPT-4) and evaluate its performance on a dataset of 185 tasks across 18 mobile apps. The results indicate that MobileGPT can automate and learn new tasks with 82.7 and is able to adapt them to different contexts with near perfect (98.75 accuracy while reducing both latency and cost by 62.5 compared to the GPT-4 powered baseline.

Mobile apps offer a variety of features that greatly enhance user experience. However, users still often find it difficult to use mobile apps in the way they want. For example, it is not easy to use multiple apps simultaneously on a small screen of a smartphone. In this paper, we present A-Mash, a mobile platform that aims to simplify the way of interacting with multiple apps concurrently to the level of using a single app only. A key feature of A-Mash is that users can mash up the UIs of different existing mobile apps on a single screen according to their preferences. To this end, A-Mash 1) extracts UIs from unmodified existing apps (dynamic UI extraction) and 2) embeds extracted UIs from different apps into a single wrapper app (cross-process UI embedding), while 3) making all these processes hidden from the users (transparent execution environment). To the best of our knowledge, A-Mash is the first work to enable UIs of different unmodified legacy apps to seamlessly integrate and synchronize on a single screen, providing an illusion as if they were developed as a single app. A-Mash offers great potential for a number of useful usage scenarios. For instance, a user can mashup UIs of different IoT administration apps to create an all-in-one IoT device controller or one can mashup today's headlines from different news and magazine apps to craft one's own news headline collection. In addition, A-Mash can be extended to an AR space, in which users can map UI elements of different mobile apps to physical objects inside their AR scenes. Our evaluation of the A-Mash prototype implemented in Android OS demonstrates that A-Mash successfully supports the mashup of various existing mobile apps with little or no performance bottleneck. We also conducted in-depth user studies to assess the effectiveness of the A-Mash in real-world use cases.

Being able to use a single app across multiple devices can bring novel experiences to the users in various domains including entertainment and productivity. For instance, a user of a video editing app would be able to use a smart pad as a canvas and a smartphone as a remote toolbox so that the toolbox does not occlude the canvas during editing. However, existing approaches do not properly support the single-app multi-device execution due to several limitations, including high development cost, device heterogeneity, and high performance requirement. In this paper, we introduce FLUID-XP, a novel cross-platform multi-device system that enables UIs of a single app to be executed across heterogeneous platforms, while overcoming the limitations of previous approaches. FLUID-XP provides flexible, efficient, and seamless interactions by addressing three main challenges: i) how to transparently enable a single-display app to use multiple displays, ii) how to distribute UIs across heterogeneous devices with minimal network traffic, and iii) how to optimize the UI distribution process when multiple UIs have different distribution requirements. Our experiments with a working prototype of FLUID-XP on Android confirm that FLUID-XP successfully supports a variety of unmodified real-world apps across heterogeneous platforms (Android, iOS, and Linux). We also conduct a lab study with 25 participants to demonstrate the effectiveness of FLUID-XP with real users.

A recent trend in the global mobile/IoT industry is the emergence of next-generation smart devices with various screens, thus mobile/IoT market leaders are highly focused on building a new multi-device computing ecosystem based on such new smart devices. Market leaders are not only simply varying their screen sizes, but also competitively launching new devices equipped with innovative screens like foldable and dual-screen phones. However, the current mobile computing ecosystem is restricted by the single device paradigm that allows a user to interact with only one screen tethered to a single device, limiting the potential that the emerging multi-device computing trend provides.

The growing trend of multi-device ownerships creates a need and an opportunity to use applications across multiple devices. However, in general, the current app development and usage still remain within the single-device paradigm, falling far short of user expectations. We present FLUID, a new multi-device platform that allows users to migrate or replicate individual user interfaces (UIs) of a single app on multiple devices. In addition, FLUID aims to require no extra development effort to support a wide range of legacy apps that follow the trend of using custom-made UIs. To this end, FLUID analyzes which UI states are necessary to correctly render UI objects, deploys only those states on different devices, and supports cross-device function calls transparently. In this demo, we demonstrate several interesting use cases supported by our Android-based FLUID prototype.