Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 11198 publications
AgentHands: Generating Interactive Hands Gestures for Spatially Grounded Agent Conversations in XR
Ziyi Liu
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, ACM
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Communicating spatial tasks via text or speech creates ``a mental mapping gap'' that limits an agent’s expressiveness. Inspired by co-speech gestures in face-to-face conversation, we propose \textsc{AgentHands}, an LLM-powered XR system that equips agents with hands to render responses clearer and more engaging. Guided by a design taxonomy distilled from a formative study (N=10), we implement a novel pipeline to generate and render a hand agent that augments conversational responses with synchronized, space-aware, and interactive hand gestures: using a meta-instruction, \textsc{AgentHands} generates verbal responses embedded with \textit{GestureEvents} aligned to specific words; each event specifies gesture type and parameters. At runtime, a parser converts events into time-stamped poses and motions, driving an animation system that renders expressive hands synchronized with speech. In a within-subjects study (N=12), \textsc{AgentHands} increased engagement and made spatially grounded conversations easier to follow compared to a speech-only baseline.
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Unveiling the Global Landscape of Android Security Updates
Haiyun Deng
Abbas Acar
Esteban Luques
Harun Oz
Ahmet Aris
Selcuk Uluagac
IEEE Transactions on Dependable and Secure Computing (2026)
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Android is the world’s leading mobile operating
system, with over three billion active devices. Detecting vulnerabilities and ensuring timely patch deployment are critical to
maintaining security. The Android Open Source Project (AOSP)
has enhanced the transparency of security updates through Security Patch Levels. However, challenges related to update speed
and availability persist. In 2022, Google reported that half of the
zero-day vulnerabilities discovered in the wild were variations of
vulnerabilities that had already been patched. Recent research
mainly highlights delays in update distribution, often attributing
them to fragmentation and focusing primarily on flagship devices
or limited time-frames. Our approach takes a device-centric
perspective to investigate Android update patterns, analyzing
567K security update records from 2014 to 2024, covering 904
distinct devices from six key Original Equipment Manufacturers
(OEMs) across 98 countries. Our extensive analysis revealed
notable differences in update release timing across OEMs, device types, and regions. Our study also examines documented
vulnerabilities and weaknesses, while assessing OEM compliance
with Android security guidelines. Our study shows that ∼89.7%
of vulnerabilities on unpatched Android devices are exploitable
without user interaction and with low attack complexity. We
also identified delays linked to fragmentation and OEM-specific
challenges, and provide actionable insights for improvement.
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Generative AI is reshaping software development, yet its psychological impact remains under-researched. During May and August 2025 we conducted reflexive thematic analysis of interviews with 12 senior engineers (≥5 years experience) recruited from Western technology hubs to explore shifts in professional identity. We identify a central transition from "coder to conductor," where AI acts as a cognitive partner. Key findings include: (1) a re-architecting of focus from implementation to strategy; (2) a shift in productivity metrics from output to impact; and (3) a dual-impact on agency, where AI empowers autonomy but threatens competence through de-skilling anxieties. These findings suggest that as implementation becomes commoditised, organisational training and career progression must prioritise architectural mastery and metacognitive oversight to ensure sustained developer motivation and system integrity.
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An experimental evaluation of an AI-powered interactive learning platform
Nicole Miller
Yael Haramaty
Lidan Hackmon
Lior Belinsky
Abraham Oritz Tapia
Lucy Tootill
Scott Siebert
Frontiers in Artificial Intelligence (2026) (to appear)
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Generative AI, which is capable of transforming static content into dynamic learning experiences, holds the potential to revolutionize student engagement in educational contexts. However, questions still remain around whether or not these tools are effective at facilitating student learning. In this research, we test the effectiveness of an AI-powered platform incorporating multiple representations and assessment through Learn Your Way, an experimental research platform that transforms textbook chapters into dynamic visual and audio representations. Through a between-subjects, mixed methods experiment with 60 US-based students, we demonstrate that students who used Learn Your Way had a more positive learning experience and had better learning outcomes compared to students learning the same content through a digital textbook. These findings indicate that AI-driven tools, capable of providing choice among interactive representations of content, constitute an effective and promising method for enhancing student learning.
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A probabilistic framework for learning non‐intrusive corrections to long‐time climate simulations from short‐time training data
Benedikt Barthel
Rob Carver
Fei Sha
Themistoklis Sapsis
Journal of Advances in Modeling Earth Systems (2026)
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Despite advances in high performance computing, accurate numerical simulations of global atmospheric dynamics remain a challenge. The resolution required to fully resolve the vast range scales as well as the strong coupling with—often not fully-understood—physics renders such simulations computationally infeasible over time horizons relevant for long-term climate risk assessment. While data-driven parameterizations have shown some promise of alleviating these obstacles, the scarcity of high-quality training data and their lack of long-term stability typically hinders their ability to capture the risk of rare extreme events. In this work we present a general strategy for training variational (probabilistic) neural network models to non-intrusively correct under-resolved long-time simulations of turbulent climate systems. The approach is based on the paradigm introduced by Barthel Sorensen et al. (2024, https://doi.org/10.1029/2023ms004122) which involves training a post-processing correction operator on under-resolved simulations nudged toward a high-fidelity reference. Our variational framework enables us to learn the dynamics of the underlying system from very little training data and thus drastically improve the extrapolation capabilities of the previous deterministic state-of-the art—even when the statistics of that training data are far from converged. We investigate and compare three recently introduced variational network architectures and illustrate the benefits of our approach on an anisotropic quasi-geostrophic flow. For this prototype model our approach is able to not only accurately capture global statistics, but also the anistropic regional variation and the statistics of multiple extreme event metrics—demonstrating significant improvement over previously introduced deterministic architectures.
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Source-to-source compilers may perform inefficiently by executing transpilation passes on scripts that do not contain the specific language features a pass is designed to transform, potentially leading to redundant processing. A compiler can analyze a script to generate a per-script feature map, for example, by identifying language features in its abstract syntax tree (AST). Before executing a transpilation pass, the compiler can check this map and may bypass the pass for that script if the specific feature targeted by the pass is not present. This feature map can also be dynamically updated throughout the compilation process as other passes transform the code. This method of conditional pass execution based on content-aware analysis may reduce redundant AST traversals, which could decrease overall compilation time and computational resource consumption.
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Validating conversational artificial intelligence (AI) for regulated medical software applications may present challenges, as static test datasets and manual review may be limited in identifying emergent, conversational anomalies. A multi-agent AI system may be configured in a closed-loop for automated validation. The system can, for example, utilize an end user persona simulator agent to generate prompts for a target model and a domain /regulatory expert adjudicator agent to evaluate the target model’s responses against a configurable rubric. A meta-analysis agent can analyze anomalies to identify underlying vulnerabilities, which may then be used to programmatically synthesize new adversarial personas. This adaptive process can generate evidence to support regulatory compliance and continuous performance monitoring for medical software algorithms systems.
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Source-to-source compilers may perform inefficiently by executing transpilation passes on scripts that do not contain the specific language features a pass is designed to transform, potentially leading to redundant processing. A compiler can analyze a script to generate a per-script feature map, for example, by identifying language features in its abstract syntax tree (AST). Before executing a transpilation pass, the compiler can check this map and may bypass the pass for that script if the specific feature targeted by the pass is not present. This feature map can also be dynamically updated throughout the compilation process as other passes transform the code. This method of conditional pass execution based on content-aware analysis may reduce redundant AST traversals, which could decrease overall compilation time and computational resource consumption.
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Enterprise service delivery platforms, while vital for HR operations, create significant challenges in managing the risks of Personally Identifiable Information (PII) exposure. The integration of Generative AI offers new efficiencies but also amplifies these risks. Existing solutions—ranging from manual redaction and rule-based Data Loss Prevention (DLP) to inflexible data masking—fail to provide a nuanced, integrated approach. This paper introduces the Dual-Mode Privacy Guard (DMPG), a conceptual framework that establishes a model for Augmented Compliance. The framework provides a "defense-in-depth" strategy built on three pillars: (1) a Zero-Trust AI Foundation leveraging a verifiable, non-retention API gateway to ensure data privacy; (2) a proactive "Guardrail" that uses AI to detect and flag potential PII for human-in-the-loop review; and (3) an on-demand "Tool" that allows users to create securely anonymized data assets. By differentiating between proactive monitoring and reactive utility, the DMPG shifts the compliance paradigm from a manual burden to an AI-assisted process that enhances, rather than replaces, human oversight. This paper details the framework’s platform-agnostic architecture, using Salesforce as a reference implementation, and argues for its novelty as a model for operationalizing privacy principles within modern enterprise systems.
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We study the problem of allocating access point bandwidth to users of a wireless network in the presence of adversarial jamming. Specifically, we consider a setting in which the network designer acts first and allocates access point bandwidth to the users of the network, before an adversary applies a jamming strategy to reduce the bandwidth of a subset (or all) of the access points. We consider a strong adversary who has complete information and can optimize the jamming strategy, subject to power budget constraints. In turn, the network designer must allocate the resources in anticipation of the adversary's actions.
We explain that our model gives rise to a special network interdiction model, which differs from the standard setting in two ways: The first is that the interdictor is given the benefit of responding, rather than leading the game. The second is that the interdiction is fractional and performed at the node level of the network. The interdiction then propagates to all edges incident to the access point.
In terms of technical results, we provide an allocation algorithm that is based on linear programming duality and show that the algorithm can solve the problem optimally, assuming knowledge of the adversary's budget constraints. We conduct experiments on synthetic data to show the extent to which the algorithm improves the total utilized bandwidth over the algorithm that optimizes bandwidth allocation while being oblivious to the adversary's existence.
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When managing complex, unpredictable (non-deterministic) AI agents using simple, fixed control systems (like finite state machines), operational failures and accountability issues often arise. This document introduces a probabilistic governance and telemetry framework to resolve these problems. Instead of following a rigid sequence of steps, this framework defines a multi-dimensional operational boundary, a 'behavioral volume', and assigns the agent a goal. This allows the agent to use its own reasoning to achieve the goal while remaining within the defined boundaries. A separate telemetry layer monitors the agent's actions by calculating metrics, such as alignment scores and drift velocity, to measure how much the agent deviates from its intended behavior. This system provides a method for guiding, monitoring, and securing autonomous agents, effectively managing the performance and security of an unpredictable AI workforce in complex environments.
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Bridging the Dimensionality Gap: A Taxonomy and Survey of 2D Vision Model Adaptation for 3D Analysis
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The remarkable success of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in 2D computer vision has catalyzed significant research into their adaptation for the complex domain of 3D analysis. However, a fundamental dichotomy exists between the regular, dense grid of 2D images and the irregular, sparse nature of 3D data formats such as point clouds and meshes. This paper provides a comprehensive survey and a novel intellectual framework for navigating this burgeoning field. Our core contribution is a new taxonomy that organizes adaptation strategies into three distinct families: (1) Data-centric methods, which project 3D data into 2D formats to leverage off-the-shelf 2D models; (2) Architecture-centric methods, which design intrinsic network modules to directly process 3D data; and (3) Hybrid methods, which synergistically combine pre-trained 2D features with 3D modeling processing pipelines to benefit from both rich visual priors and explicit geometric reasoning. Through this taxonomic lens, we conduct a systematic review and qualitative synthesis of the field. We illuminate the fundamental trade-offs between these families concerning computational complexity, reliance on large-scale pre-training, and the preservation of geometric inductive biases. Based on this analysis, we identify and discuss critical open challenges and chart promising future research directions, including the development of 3D foundation models, advancements in self-supervised learning for geometric data, and the deeper integration of multi-modal signals. This survey serves as an essential resource and roadmap for researchers seeking to understand and advance the state-of-the-art in 3D computer vision.
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ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding
Sunny Rajagopalan
Alireza Golestaneh
Shubhra Chandra
Min Zhou
Jonathan Vronsky
Songbai Yan
2026
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We present ALF (Advertiser Large Foundation model), a multi-modal transformer architecture for understanding advertiser behavior and intent across text, image, video and structured data modalities. Through contrastive learning and multi-task optimization, ALF creates unified advertiser representations that capture both content and behavioral patterns. Our model achieves state-of-the-art performance on critical tasks including fraud detection, policy violation identification, and advertiser similarity matching. In production deployment, ALF reduces false positives by 90\% while maintaining 99.8\% precision on abuse detection tasks. The architecture's effectiveness stems from its novel combination of multi-modal transformations, intersample attention mechanism, spectrally normalized projections, and calibrated probabilistic outputs.
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The Perfection Paradox: From Architect to Curator in AI-Assisted API Design
JJ Geewax
David R Karger
Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26), ACM, Barcelona, Spain, TBD
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Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement Proposals(AIPs). Through a controlled study with 16 industry experts, we compared AI-generated API specifications against human-authored ones. While quantitative results indicated AI superiority in 10 of 11 usability dimensions and an 87% reduction in authoring time, qualitative analysis revealed a paradox: experts frequently misidentified AI work as human (19% accuracy) yet described the designs as unsettlingly “perfect.” We characterize this as a “Perfection Paradox”—where hyper-consistency signals a lack of pragmatic human judgment. We discuss the implications of this perfection paradox, proposing a shift in the human designer’s role from the “drafter” of specifications to the “curator” of AI-generated patterns.
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A Framework for Interactive Machine Learning and Enhanced Conversational Systems
Jerry Young
Richard Abisla
Sanjay Batra
Mikki Phan
Nature, Springer-Verlag (2026)
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Conversational systems are increasingly prevalent, yet current versions often fail to support the full range of human speech, including variations in speed, rhythm, syntax, grammar, articulation, and resonance. This reduces their utility for individuals with dysarthria, apraxia, dysphonia, and other language and speech-related disabilities. Building on research that emphasizes the need for specialized datasets and model training tools, our study uses a scaffolded approach to understand the ideal model training and voice recording process. Our findings highlight two distinct user flows for improving model training and provide six guidelines for future conversational system-related co-design frameworks. This study offers important insights on creating more effective conversational systems by emphasizing the need to integrate interactive machine learning into training strategies.
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