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 11637 publications
When Android Apps Request Location with a Reason: A Developer Study and User Behavior Experiment
May Smith
Aman Jain
Proceedings on Privacy Enhancing Technologies (2027)
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Recent reports have highlighted how mobile apps share user location data with third parties, risking user privacy and platform trust. Although location data is highly sensitive, when users grant apps location access, they may not know the full extent to which it is used. We study how requiring Android apps to show a reason for location access could impact developers, users, and the platform.
We surveyed 323 Android app developers and found most supported such a requirement. The majority said it would have a positive impact on user privacy, trust for apps, and trust for Android, where impact on user trust for Android correlated most strongly with support. Many developers also said the intervention would increase the number of users granting location access. Yet their open-ended comments also revealed consistent concerns, such as apps providing dishonest reasons and platform verification.
To study the impact on user behavior, we conducted a randomized controlled experiment with 2579 US Android users. We tested how users' decisions to grant location access were impacted by app type, whether reasons were included in the requests, and the content of the reasons, including monetization. We did not find the reasons impacted users' decisions; decisions were instead driven by app type and demographics. Yet we did find the reasons could have a positive impact on user perception for the platform when the reasons did not include using data for ads. Our findings provide insights into developers' willingness to implement privacy-enhancing changes, and expose limits to improving user privacy by simply adding information to user interfaces.
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A 3D Scene Graphs Survey: Open Challenges and Future Directions
Dennis Rotondi
Francesco Argenziano
Sebastian Koch
Nathan Hughes
Martin Büchner
Johanna Wald
Lukas Schmid
Daniele Nardi
Abhinav Valada
Liam Paul
Luca Carlone
Kai Arras
Annual Review of Control, Robotics, and Autonomous Systems (ARCRAS), 10 (2027) (to appear)
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3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Their expressiveness has made them relevant to a broad range of problems in robotics and computer vision, including mapping, task and motion planning, scene understanding, and many others. However, the field remains fragmented: different communities adopt distinct formulations, construction pipelines, and evaluation protocols, making it difficult to compare methods, identify common assumptions, and assess remaining challenges for robust real- world deployment. This survey provides a unified and critical review of 3DSGs, with particular emphasis on open challenges and future directions. We first formalize 3DSGs under a common definition and analyze the principal modeling choices that characterize existing formulations, including node and edge attributes, hierarchical structure, dynamic scene representations, and affordance-aware extensions. We then review how 3DSGs are constructed from raw sensory observations, covering both learning-oriented and construction-oriented systems. Finally, we examine downstream applications and evaluation strategies, from intrinsic graph quality to task-level performance. To support the community, we also provide a dedicated website that organizes and extends the surveyed works.
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A Computer Vision Problem in Flatland
Erin Connelly
Annalisa Crannell
Timothy Duff
Rekha R. Thomas
SIAM Journal on Applied Algebra and Geometry, 10 (2026), pp. 14-45
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When is it possible to project two sets of labeled points of equal cardinality lying in a pair of projective planes to the same image on a projective line? We give a complete answer to this question, obtaining the following results. We first show that such a pair of projections exist if and only if the two point sets are themselves images of a common point set in projective space. Moreover, we find that for generic pairs of point sets, a common projection exists if and only if their cardinality is at most seven. In these cases, we give an explicit description of the loci of projection centers that enable a common image.
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“That Experience Still Haunts Me to This Day”: A Multi-Region Survey on AI-Generated Image-Based Sexual Abuse Victimization
Nicola Henry
International Journal of Human-Computer Interaction (2026)
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Image-based sexual abuse (IBSA) refers to the creation, sharing, or threats to share intimate images, whether real or synthetic. Advances in artificial intelligence (AI) now enable the generation of sexually explicit images from non-explicit source material, such as professional headshots and social media profiles. In this study, we found that across three regions (Australia, the United Kingdom, and the United States), 15% of a representative sample reported experiences of IBSA involving digitally altered images, including 6.9% who reported AI-generated IBSA (AI-IBSA) victimization specifically. Higher rates of victimization were observed among respondents under 35, LGBTQ+, and BIPOC participants, people with disabilities, and men. While victimization rates were higher among men, women were more likely to report a broader range of harms and more negative emotional and social impacts. The most common responses to victimization were platform-based actions, including blocking (69%) and reporting (58%). These findings highlight AI-IBSA as a growing and under-recognized form of harm, with important implications for prevention, platform governance, and support for victim-survivors.
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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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Every team building Large Language Models (LLMs) faces a core challenge: offline benchmarks show performance gains and user engagement rises post deployment, but isolating cause from effect remains difficult. Simultaneous marketing, media coverage, and seasonal demand obscure whether model updates truly drive engagement gains. This paper presents a novel causal estimation approach that leverages non uniform quality improvements across capabilities within a single model version. Because capabilities improve unevenly (e.g., strong gains in coding versus modest gains in writing), users experience varied quality depending on their task distribution. This variation in experienced quality provides causal signal for estimation. We validate this approach on synthetic data with known ground truth. The raw estimator recovers 81% to 88% of the true effect, with the remainder lost to measurement error in usage estimates. Applying an errors in variables disattenuation adjustment (using a test retest reliability ratio mean correlation of 0.861) corrects the estimate to 1.017 (bootstrapped 95% CI: 0.915 to 1.109). By contrast, naive methods fail significantly, recovering only 63% without version controls and 62% using real time rather than frozen usage patterns. Permutation tests confirm the framework distinguishes true causal effects from noise. Sensitivity analysis indicates recovery improves monotonically from 78% to 87% as pre period length increases from 3 to 7 weeks, highlighting a clear tradeoff between sample duration and estimator precision. Seed sensitivity tests further confirm stability across random draws.
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CATWILD: Compiler Autotuning For TPU workloads in the Wild
Kirill Gugaev
Mike Burrows
Mangpo Phothilimthana
Naveen Kumar
Idan Nachman
Nacho Cano
David Liu
Zongwei Zhou
Tengyu Sun
Berkin Ilbeyi
Jie Sun
Vineetha Govindaraj
Ziqiang Feng
Hyojun Kim
Matheus Camargo
Chao Wang
Alex Wertheim
Domagoj Babic
Aleksey Orekhov
Emma Sevastian
Wenqi Cao
Shobha Vasudevan
Karthik Srinivasa Murthy
Jose Baiocchi Paredes
Emma Wang
Ryan Lefever
Arissa Wongpanich
Νikhil Sarda
Mehrdad Khani
Matt Bierbaum
Sami Abu-El-Haija
Samrat Ghosh
2026
Preview abstract
Compilers play a fundamental role at achieving peak performance for machine learning (ML) workloads. However, given the diverse nature of workloads and accelerators, compilers' heuristics and analytical cost models often result in sub-optimal performance, and thus waste precious datacenter resources. Furthermore, the multitude of tunable parameters and their complex interplay often make it impossible for human experts to manually find optimal configurations. In this paper, we present
CATWILD, a system that automatically optimizes ML jobs in Google's TPU fleet using compiler autotuning techniques. We describe CATWILD’s design and implementation, and evaluate its performance using a handful of representative metrics. We further report experiences and lessons learned from its five-year development and operation. To the best of our knowledge, CATWILD represents the first ML compiler autotuning solution deployed in datacenters at scale. Its successful rollout yielded substantial benefits, optimizing over 70\% of daily TPU training jobs and achieving significant chip savings
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Data-usage descriptors as search metadata: the case of food security data and the National Data Platform (2015-2025)
Julia Lane
Rafael Ladislau
Lauren Chenarides
Simon Porter
Manish Parashar
Scientific Data, 13 (2026)
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Scientific data is a critical input into scientific research. Yet the research data landscape is constantly changing as new datasets emerge, others are retired, or some disappear altogether. Without a systematic way to track how datasets are used across a research field, researchers have no reliable method for identifying relevant data resources or locating communities that work with them. Data-usage descriptors can substantially advance research productivity by reducing the time that researchers spend finding new and relevant datasets in their research field, and the communities that use them. This paper describes how to generate data-usage descriptors by finding how datasets are used in publications and then linking the dataset information to the publication metadata. It also shows how usage descriptors can be used to find other related datasets and their usage. It concludes by arguing that the approach represents a critical piece of foundational infrastructure that could be deployed in repositories as part of a referenceable, navigable, and contextual data framework. This article contains a reproducible workflow for constructing data-usage descriptors, based on analyzing the full text of publications in the Dimensions database. The illustrative use case is research on food security. The illustrative repository is the National Data Platform.
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The AI architecture decisions that cannot wait
AI Accelerator Institute (2026)
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As organizations pursue AI transformation in large environments, ambitions often collide with the practical urgency of fixed-deadline data center exits. This article argues that a non-negotiable migration date should not be viewed merely as an infrastructure constraint, but as a critical deadline for establishing AI readiness. AI systems amplify the strengths and weaknesses of the underlying architecture; fragmented data or inconsistent infrastructure will lead to unreliable AI outcomes. To build a foundation for future intelligence, architects must prioritize resilience, standardization, and governance early in the migration process. Ultimately, successful AI transformation depends less on the speed of deployment and more on foundational architectural decisions such as Infrastructure-as-Code and unified telemetry—made before the migration concludes.
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FabScore: Fine-Grained Evaluation of Fabrications in Automated AI Research
James Xu Zhao
See-Kiong Ng
Dongfu Jiang
Bryan Hooi
Qianyun Guo
Hui Chen
Pang Wei Koh
Muhao Chen
Yiwei Wang
2026
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In automated AI research, scientific rigor is not merely a matter of producing coherent papers and executable code; rather, it fundamentally requires that the experimental results claimed in a paper are faithfully supported by the accompanying implementation and verifiable through actual execution logs. When this alignment breaks down, AI-generated research may contain fabrications: discrepancies between the methods described in the paper and what the code actually implements, or between the reported results and those obtained by running the code. This motivates a systematic evaluation of fabrications that systematically examines the consistency between a paper's claimed experimental outcomes and its accompanying code files. In automated AI research, scientific rigor is not merely a matter of producing coherent papers and executable code; rather, it fundamentally requires that the experimental results claimed in a paper are faithfully supported by the accompanying implementation and verifiable through actual execution logs. When this alignment breaks down, AI-generated research may contain fabrications: discrepancies between the methods described in the paper and what the code actually implements, or between the reported results and those obtained by running the code. This motivates a systematic evaluation of fabrications that systematically examines the consistency between a paper's claimed experimental outcomes and its accompanying code files. This is a placeholder. This is a placeholder. This is a placeholder.
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Mobility-Embedded POIs: Learning What a Place Is and How It’s Used from Human Movement
Shushman Choudhury
Shang-Ling Hsu
Cyrus Shahabi
Forty-third International Conference on Machine Learning (2026)
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Recent progress in geospatial foundation models (GeoFMs) has highlighted the
importance of learning general-purpose representations for real-world locations,
particularly Points of Interest (POIs) where human activity concentrates. Yet, ex-
isting POI representations remain largely static, drawing from textual metadata
(e.g., category labels, descriptions) and spatial attributes (e.g., coordinates, neigh-
borhood context), all of which describe what a place is, but not how it is actu-
ally used. We argue that human mobility provides a complementary and dynamic
signal, capturing real-world visitation patterns that reveal how places function in
practice. To this end, we introduce Mobility Embedded POIs (ME-POIs), a
pretraining framework that learns POI representations directly from sequences of
human visits. Each visit is encoded as a contextualized embedding that captures
the POI’s static attributes as well as its temporal and sequential context, includ-
ing when the visit occurs and which visits surround it. These visit embeddings
are aligned with learnable POI embeddings via a contrastive objective, grounding
POI representations in their real-world usage patterns. To address the long tail of
sparsely visited POIs, we transfer visitation distributions from data-rich anchors
to sparse locations, leveraging multi-scale spatial proximity to capture local and
regional patterns, and functional similarity to enable transfer across semantically
related POIs. We demonstrate the utility of ME-POIs for a set of automated map
enrichment tasks, critical in geospatial intelligence. We show empirically that
by embedding visitation dynamics, ME-POIs outperform text- and location-only
baselines, proving that mobility-informed embeddings provide a stronger founda-
tion for modeling place function and change.
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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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Human-AI Interaction for Time-Critical Sensemaking in Missing Persons Investigations
Pola Labedzka
Miri Zilka
Dorian Peters
John Dudley
2026
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Every year an estimated 200,000 people go missing in the UK alone. Missing persons investigations involve challenging time-critical sensemaking tasks based on fragmented data sources. This paper describes a mixed-methods participatory study evaluating data science and AI-driven techniques (summarisation, fact extraction, and data visualisation) for supporting these investigations as part of a human-centered workflow. A series of human-AI interfaces were iteratively designed and tested with search officers and domain experts at Police Scotland. Based on findings, we describe: (1) user and information needs for missing persons investigations; (2) Insights on the benefits and challenges of applying LLM-based techniques in high-risk contexts; and (3) lessons for integrating AI for information and sensemaking tasks in policing more broadly. We highlight that in high-stakes contexts, where accuracy and context-sensitivity are paramount, AI techniques must be balanced with other approaches and designed in close partnership with end-users.
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OpenClaw in Practice: Building Laptop-Less Engineering Workflows with an Agent Harness
Hackernoon (2026)
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This article introduces OpenClaw, an AI-powered workflow harness designed to reduce operational friction for developers. Unlike traditional chatbots, OpenClaw manages and persists context across complex engineering tasks, enabling asynchronous operations and mobile-first interactions. The post explores practical use cases, including incident triage from mobile, asynchronous pull request reviews, quick infrastructure scripting, and automating routine operational tasks. It also delves into the key architectural layers of an OpenClaw-like system—Connectors, Gateway/Session Manager, Agent Runtime, Memory/Configuration, and Skills/Tools. The article emphasizes the importance of security, observability, and proper integration with existing developer ecosystems, positioning OpenClaw as a shift towards reducing context switching and enhancing developer productivity by automating the workflows around coding.
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The rise of agentic AI systems is fundamentally altering the nature of vendor relationships, moving beyond traditional counterparty models where vendors are singular, known organizations. This article explores how AI-mediated workflows create porous organizational boundaries, with vendors' autonomous systems often invoking other undisclosed third-party systems. This shift introduces new risks and failure modes, including counterparty drift, cross-organizational hallucinations, fragmented audit trails, and reviewer fatigue. We argue that existing procurement and third-party risk management frameworks are insufficient for this new reality. Drawing on emerging best practices and converging regulatory trends (e.g., DORA, EU AI Act), the article proposes revisions to governance practices. These include mandating disclosure of autonomous system chains, negotiating cross-organizational audit trail access, and explicitly defining human override and accountability points. The article concludes by highlighting unresolved challenges in cross-organizational liability, trust frameworks for machine-speed coordination, and vendor concentration risk, urging procurement and risk leaders to adapt their strategies for an era of networked autonomous systems.
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