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 11268 publications
GUIDE: A Benchmark for User Context Understanding and Assistance in GUI Workflow Videos
Saelyne Yang
Jaesang Yu
Yi-Hao Peng
Kevin Qinghong Lin
Jae Won Cho
Juho Kim
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2026)
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Graphical User Interface (GUI) agents have the potential to assist users in interacting with complex software. While prior research has primarily focused on automating user actions through clicks and keystrokes, this paradigm overlooks human intention, where users value the ability to explore, iterate, and refine their ideas while maintaining agency.To move beyond automation and toward collaboration, GUI agents must understand what users are doing and why. We introduce GUIDE (GUI Understanding, Intent, and Help Decision Evaluation), a benchmark that evaluates AI models on their ability to perceive user behavior, infer intent, and provide assistance in open-ended GUI tasks. GUIDE consists of 67.5 hours of screen recordings from 120 novice user demonstrations with think-aloud narrations that surface user intent, across 10 complex software (e.g., PowerPoint, Photoshop). GUIDE defines three tasks—(i) Behavior State Detection, (ii) Intent Prediction, and (iii) Help Prediction that test a model’s ability to recognize behavior state, reason about goals, and decide when and how to help. Evaluations across eight state-of-the-art multimodal models reveal that all models struggled with the tasks, achieving only 44.6% and 55.0% accuracy on behavior state and help prediction. However, providing user context such as behavioral state and intent significantly improved the performance, raising help prediction by up to 50.2%. These results highlight the critical role of structured user understanding in effective assistance.Our benchmark provides a path toward GUI agents that go beyond automation to become truly user-aware collaborators.
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Digital Shadow AI Risk Theoretical Framework (DART): A Framework for Managing Data Disclosure and Privacy Risks of AI tools at Work
Master's Thesis (2026)
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The accelerated integration of generative AI technologies and agentic AI tools, particularly those like ChatGPT, into workplace settings has introduced complex challenges concerning data governance, regulatory compliance, and organizational privacy (GDPR 2016; CCPA/CPRA). This study introduces the Digital Shadow AI Risk Theoretical Framework (DART)—a novel theoretical framework designed to systematically identify, classify, and address the latent risks arising from the widespread, and often unregulated, use of AI systems in professional environments (NIST, 2023; OECD AI Policy Observatory, 2023). DART introduces six original, interrelated constructs developed in this study: Unintentional Disclosure Risk, Trust-Dependence Paradox, Data Sovereignty Conflict, Knowledge Dilution Phenomenon, Ethical Black Box Problem, and Organizational Feedback Loops. Each construct reflects a unique dimension of risk that emerges as organizations increasingly rely on AI-driven tools for knowledge work and decision-making.
The framework is empirically tested through a mixed-methods research design involving hypothesis testing and statistical analysis of behavioral data gathered from cross-sectional surveys of industry professionals. Two cross-industry surveys (Survey-1: 416 responses, 374 analyzed; Survey-2: 203 responses, 179 analyzed) and CB-SEM tests supported seven of eight hypotheses; H4 (sovereignty) was not significant; H7 (knowledge dilution) was confirmed in replication. The findings highlight critical gaps in employee training, policy awareness, and risk mitigation strategies—underscoring the urgent need for updated governance frameworks, comprehensive AI-use policies, and targeted educational interventions. This paper contributes to emerging scholarship by offering a robust model for understanding and mitigating digital risks in AI-enabled workplaces, providing practical implications for compliance officers, risk managers, and organizational leaders aiming to harness the benefits of generative AI responsibly and securely. The novelty of DART lies in its explicit theorization of workplace-level behavioral risks—especially Shadow AI, which unlike Shadow IT externalizes organizational knowledge into adaptive systems—thereby offering a unified framework that bridges fragmented literatures and grounds them in empirical evidence.
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In "Elephants, Goldfish and the New Golden Age of Software Engineering," the author discusses how AI is changing knowledge work, especially software development. Written from the perspective of April 2026, the article points out that while AI speeds up coding, it can also quickly generate a lot of mistakes and messy code if it isn't carefully managed by human oversight and clear processes.
The paper outlines a practical approach to working with AI, broken down into three main sections:
* **Using AI as a Tool, Not a Toy:**
The author notes that people often get poor results by asking AI to do everything in a single prompt. Instead, users should have back-and-forth conversations with AI to question assumptions, set clear grading rules, and guide the research. The main point is that humans must still provide the final judgment; AI is simply a way to speed up and record that thinking.
* **The Elephant-Goldfish Model:**
As AI creates more code than humans can easily read, written design documents become more important than the code itself. To keep AI on track, the author suggests a two-part method:
* **The Elephant:** A long chat session where the human and AI discuss ideas and write a detailed design document *before* any code is written. This session holds all of the project's background information and decisions.
* **The Goldfish:** A brand-new AI chat session with no memory. The human asks this "goldfish" to read the design document. If the goldfish cannot understand the plan based only on that document, the document needs more details.
* Only after the design document is clear enough for the goldfish to understand does the human ask the AI to write the code based on those strict instructions.
* **Managing AI and the Future of Work:**
The author expects that regular employees will soon act like managers, overseeing multiple AI helpers. Because of this, workers need to learn basic management skills, like how to delegate tasks and set clear boundaries. Also, since AI will handle routine chores, humans will need to practice focusing for longer periods to do deeper, harder thinking. Ultimately, a worker's value will come from their planning and decision-making skills, rather than their ability to type code.
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ARM MTE Performance in Practice
Taehyun Noh
Yingchen Wang
Tal Garfinkel
Mahesh Madhav
Mattan Erez
Shravan Narayan
Usenix Security (2026)
See2Refine: Vision-Language Feedback Improves LLM-Based eHMI Action Designers
Ding Xia
Xinyue Gui
Mark Colley
Fan Gao
Dongyuan Li
Renhe Jiang
Takeo Igarashi
ACL 26 (2026)
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Automated vehicles lack natural communication channels with other road users, making external Human-Machine Interfaces (eHMIs) essential for conveying intent and maintaining trust in shared environments. However, most eHMI studies rely on developer-crafted message-action pairs, which are difficult to adapt to diverse and dynamic traffic contexts. A promising alternative is to use Large Language Models (LLMs) as action designers that generate context-conditioned eHMI actions, yet such designers lack perceptual verification and typically depend on fixed prompts or costly human-annotated feedback for improvement. We present See2Refine, a human-free, closed-loop framework that uses vision-language models (VLMs) for perceptual evaluation as automated visual feedback to improve an LLM-based eHMI action designer. Given a driving context and a candidate eHMI action, the VLM evaluates the perceived appropriateness of the action, and this feedback is used to iteratively revise the designer's outputs, enabling systematic refinement without human supervision. We evaluate our framework across three eHMI modalities (lightbar, eyes, and arm) and multiple LLM model sizes. Across settings, our framework consistently outperforms prompt-only LLM designers and manually specified baselines in both VLM-based metrics and human-subject evaluations. Results further indicate that the improvements generalize across modalities and that VLM evaluations are well aligned with human preferences, supporting the robustness and effectiveness of \systemName for scalable action design.
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From Correctness to Collaboration: A Human-Centered Taxonomy of AI Agent Behavior in Software Engineering
Sherry Y. Shi
Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26), ACM, New York, NY, USA (2026)
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The ongoing transition of Large Language Models in software engineering from code generators into autonomous agents requires a shift in how we define and measure success. While models are becoming more capable, the industry lacks a clear understanding of the behavioral norms that make an agent effective in collaborative software development in the enterprise. This work addresses this gap by presenting a taxonomy of desirable agent behaviors, synthesized from 91 sets of user-defined rules for coding agents. We identify four core expectations: Adhere to Standards and Processes, Ensure Code Quality and Reliability, Solve Problems Effectively, and Collaborate with the User. These findings offer a concrete vocabulary for agent behavior, enabling researchers to move beyond correctness-only benchmarks and design evaluations that reflect the realities of professional software development in large enterprises.
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Enterprise service centers, particularly in domains like People Operations, are critical hubs of organizational knowledge work. They face a persistent difficulty in disseminating the tacit, case-specific expertise of senior agents, which can lead to inconsistent service and slower onboarding for new hires. While existing Knowledge Management (KM) and Case-Based Reasoning (CBR) systems have improved the retrieval of historically similar cases, they inadvertently shift the cognitive burden of synthesizing this information to the time-constrained agent. This paper introduces the Dynamic Case Precedent (DCP) architecture, a novel socio-technical framework designed to address this gap. The DCP architecture moves beyond simple precedent recommendation to automated precedent synthesis. It achieves this by integrating a semantic retrieval model with the large-context reasoning capabilities of a generative Large Language Model (LLM). We propose a three-pillar framework—(1) Contextual Similarity Indexing, (2) Generative Insight Synthesis, and (3) Human-in-the-Loop Refinement. By analyzing multiple relevant historical cases to generate a concise summary of resolution patterns, the DCP architecture aims to reduce agent cognitive load, accelerate proficiency, and improve service consistency. This conceptual framework offers a new model for human-AI collaboration, framing the AI not as a mere information tool, but as an active partner in sensemaking.
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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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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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Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Benjamin Hersh
Jiahao Ren
Xingyue Chen
Robert Timothy Bettridge
Faraz Faruqi
Anthony 'Xiang' Chen
Steve Toh
Google XR, Google (2026)
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While large language models have accelerated software development through "vibe coding", prototyping intelligent Extended Reality (XR) experiences remains inaccessible due to the friction of complex game engines and low-level sensor integration. To bridge this gap, we contribute XR Blocks, an open-source, modular WebXR framework that abstracts spatial computing complexities into high-level, human-centered primitives. Building upon this foundation, we present Vibe Coding XR, an end-to-end rapid prototyping workflow that leverages LLMs to translate natural language intent directly into functional XR software. Using a web-based interface, creators can transform high-level prompts (e.g., "create a dandelion that reacts to hand") into interactive WebXR applications in under a minute. We provide a preliminary technical evaluation on a pilot dataset (VCXR60) alongside diverse application scenarios highlighting mixed-reality realism, multi-modal interaction, and generative AI integrations. By democratizing spatial software creation, this work empowers practitioners to bypass low-level hurdles and rapidly move from "idea to reality." Code and live demos are available at https://xrblocks.github.io/gem and https://github.com/google/xrblocks.
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KVCIS: Activation-Based Token Importance Prediction for Intelligent KV-Cache Compression
Zenodo (2026)
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We introduce KVCIS (KV-Cache Importance Scoring), a novel approach to KV-cache compression that predicts token importance from intermediate-layer activations before attention is computed. Unlike existing methods (H2O, StreamingLLM, Scissorhands) that make compression decisions based on attention scores computed during generation, KVCIS enables proactive compression at cache insertion time—determining how to store each token before paying the computational cost of attention. We discover a two-level importance structure in decoder-only transformers: the beginning-of-sequence (BOS) token acts as an "attention sink" receiving ~76% of attention, while the remaining ~24% is distributed across content tokens with 10-11× importance spread. A simple linear probe achieves R² = 0.998 overall and R² = 0.68–0.79 for discriminating among content tokens. Extensive validation across 3 model families (Llama, Mistral, Gemma), 8 layer depths, context lengths from 256 to 2048 tokens, and multiple downstream tasks demonstrates: 50% memory reduction with zero degradation on NarrativeQA (F1 = 0.064 matching baseline exactly), while uniform quantization degrades by 7.8% at the same compression ratio. KVCIS consistently achieves 5–8× better quality preservation than uniform quantization across all tested context lengths. The memory savings enable increased batch sizes and longer context support; the probe itself adds minimal overhead (~16KB direction vector, 0.06ms per token). This work extends activation-based probing from safety classification to inference optimization, demonstrating that intermediate-layer activations encode predictive signals about token importance for generation.
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Audio Description ( AD) provides essential access to visual media for blind and low vision ( BLV) audiences. Yet current AD production tools remain largely inaccessible to BLV video creators, who possess valuable expertise but face barriers due to visually- driven interfaces. We present ADCanvas, a multimodal authoring system that supports non- visual control
over audio description ( AD) creation. ADCanvas combines conversational interaction with keyboard- based playback control and a plain- text, screen reader–
accessible editor to support end- to- end AD authoring and visual question answering ( VQA). Combining screen- reader- friendly controls with a multimodal
LLM agent, ADCanvas supports live VQA, script generation, and AD modification. Through a user study with 12 BLV video creators, we find that users adopt
the conversational agent as an informational aide and drafting assistant, while maintaining agency through verification and editing. For example, participants
saw themselves as curators who received information from the model and filtered it down for their audience. Our findings offer design implications for
accessible media tools, including precise editing controls, accessibility support for creative ideation, and configurable rules for human- AI collaboration.
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On-the-Fly OVD Adaptation with FLAME: Few-shot Localization via Active Marginal-Samples Exploration
Yehonathan Refael
Amit Aides
Aviad Barzilai
Vered Silverman
Bolous Jaber
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops (2026), pp. 886-894
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Open-vocabulary object detection (OVD) models offer remarkable flexibility applications by enabling object detection from arbitrary text queries. Still, the zero-shot performance of the pre-trained models is hampered by the inherent semantic ambiguity of natural language, result to low precision, leading to insufficient crucial downstream applications. For instance, in the remote sensing (RS) domain, a query for "ship" can yield varied and contextually irrelevant results. To address this, for real time applications, we propose a novel cascaded architecture that synergizes the broad capabilities of a large, pre-trained OVD model with a lightweight, few-shot classifier. Our approach utilizes the frozen weights of the zero-shot model to generate initial, high-recall object-embedding proposals, which are then refined by a compact classifier trained in real-time on a handful of user-annotated examples. The core of our contribution is an efficient one step active learning strategy for selecting the most informative samples for user annotation. Our method identifies (extremely) small amount of an uncertain candidates near the theoretical decision boundary using density estimation and then applies clustering to ensure a diverse training set. This targeted sampling enables our cascaded system to elevate performance on standard remote sensing benchmarks. Our work thus presents a practical and resource-efficient framework for adapting foundational models to specific user needs, drastically reducing annotation overhead while achieving high accuracy without costly full-model fine-tuning.
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Agentic AI Infrastructure in Practice: Learn These Key Hurdles to Deploy Production AI Agents Efficiently
https://swisscognitive.ch/ (2026)
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The emergence of Agentic AI—autonomous systems capable of reasoning, decision-making, and multi-step execution—represents a paradigm shift in enterprise technology. Moving beyond simple generative tasks, these agents offer the potential to solve long-standing industry pain points, with over 90% of enterprises planning integration within the next three years. However, the transition from successful proof-of-concept (PoC) to a resilient, production-grade system presents significant hurdles.
This article categorizes these challenges into three primary domains:
Technical and Engineering Hurdles: Issues such as "entangled workflows" that complicate debugging, the struggle to maintain output quality and mitigate hallucinations, and the unpredictability caused by shifting underlying models or data sources.
People, Process, and Ecosystem Hurdles: The high operational costs and unclear ROI of large models, the necessity of a new "Agent Ops" skillset, the complexity of integrating agents with disparate enterprise systems, and a rapidly evolving regulatory landscape.
The Pace of Change and Security risks: The technical debt incurred by shifting software frameworks and the expanded attack surface created by autonomous agents.
The article concludes that successful deployment requires a shift from informal "vibe-testing" to rigorous engineering discipline. By adopting code-first frameworks, establishing robust evaluation metrics (KPIs), and prioritizing functional deployment over theoretical optimization, organizations can effectively manage the lifecycle of Agentic AI and realize its transformative business value.
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We introduce AMS (Activation-based Model Scanner), a tool for verifying whether a language model is safe to deploy by analyzing its internal activation patterns. While "uncensored" and maliciously fine-tuned models pose increasing risks, current detection methods rely on behavioral testing that is slow, incomplete, and easily evaded. AMS takes a fundamentally different approach: measuring the geometric structure of safety-relevant concepts in the model's activation space. Safe models exhibit strong class separation (4-8σ) between harmful and benign content; models with removed or degraded safety training show collapsed separation (<2σ). Using contrastive prompt pairs and direction vector analysis, AMS performs model-level verification rather than prompt-level classification. We validate AMS across 14 model configurations spanning 3 architecture families (Llama, Gemma, Qwen), 3 quantization levels (FP16, INT8, INT4), and multiple model categories (instruction-tuned, base, abliterated, uncensored). In our validation set: (1) all four instruction-tuned models pass with 3.8-8.4σ separation; (2) three tested uncensored models (Dolphin, Lexi, LLama-3-8b-Uncensored) flagged as CRITICAL with 1.1-1.3σ on harmful content; (3) an abliterated Llama variant flagged as WARNING (3.33σ); (4) Llama base model shows 0.69σ, confirming absence of safety training; (5) quantization has minimal impact (<5% drift). One model labeled "uncensored" (DarkIdol) unexpectedly passed, suggesting either mislabeling or a technique that preserves activation geometry. AMS also provides identity verification via direction vector comparison. Scanning completes in 10-40 seconds per model on GPU hardware. We discuss threshold calibration, limitations of our validation scope, and directions for broader evaluation.
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