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 11644 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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Modern transformers have been scaled up in size, data, training-time compute, and more recently test-time compute based on Chain-of-Thought (CoT), but the consequences of trading off these parameters are unknown. We construct explicit tradeoffs between two of these aspects: the number of layers in a transformer and the number of CoT tokens. Our first result is general: with mild assumptions and a single-token prompt, for any integer k, a transformer using soft attention and SwiGLU MLP layers with L layers and n CoT tokens can be converted to a transformer with at most ckL layers and ⌈n/k⌉ CoT tokens, and vice versa, although with an increase to the size of other parameters. We then a multi-token prompt path following task, finding that additional preprocessing from the multi-token prompt changes the depth-CoT relationship in this case. We construct an attention-only network with L layers and 2^(L−2) heads that can traverse 2^(L−1) − 1 edges along a path with a single CoT token, demonstrating at least an exponential relationship between increases in depth and reductions in the number of CoT tokens needed to traverse the entire path. This relationship for the path following task holds empirically as well, as demonstrated by training networks with up to 5 layers, with the tradeoff between depth and the maximum number of edge traversals in a single CoT token exactly matching our theoretical constructions.
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Ten Insights from Other Domains That Inform Responsible AI Frameworks
Patrick Gage Kelley
Allison Woodruff
Angela McKay
Dunstan Allison-Hope
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (2026), 104–115
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The rapid growth of AI systems is being accompanied by new guidelines, principles, standards, regulations, and best practices (hereafter “frameworks”) that seek to ensure the responsible design, development, deployment, and use of AI systems.
Our premise is that the substance, implementation, and evolution of these AI frameworks can be informed by the practical experience of pursuing similar desired outcomes in other relevant domains (e.g., content moderation, human rights, climate change). This will help ensure that mistakes are not repeated and more rapid progress is made.
We used a “repetition test” to generate the following ten insights from other domains. Insights passing the “repetition test” are those that experts with thousands of hours of practical experience often repeat when describing the best practices that have emerged from their domain.
AI frameworks can draw from these ten insights, rather than invent entirely new approaches.
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Controlling the Conversation
Martino Banchio
Bing Liu
Andres Perlroth
2026
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We study how AI assistants design their conversations. An assistant, with a private objective, guides a user through her search via sequential queries, modeled as partitions of the search space. The user selects the subset with the highest average value. Using a prior-free framework, we minimize the platform's regret relative to an omniscient benchmark.
We provide an optimal randomized policy that achieves bounded regret of $\frac{1}{n-1}$, where $n$ is the number of items. This policy is implemented as a sequence of ``recommendations'', that is, the AI sequentially proposes candidates from the item set until one is accepted.
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VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
Dj Dvijotham
Mirko Montanari
ACL 2026
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Artificial intelligence is rapidly evolving, marked by the emergence of Large Language Model (LLM) agents – systems capable of complex reasoning, planning, and interaction with digital and physical environments. These agents, powered by advancements in LLMs, demonstrate remarkable capabilities across diverse domains, including finance, healthcare, web navigation, software development, and daily task assistance. Unlike traditional AI systems, LLM agents can perceive their surroundings, formulate multi-step plans, utilize external tools and APIs, access memory or knowledge bases, and execute actions to achieve specified goals. This ability to act upon the world, however, introduces significant safety and security challenges.
The safety paradigms developed for traditional LLMs, primarily focused on mitigating harmful textual outputs (e.g., toxicity, bias), are insufficient for safeguarding LLM agents. Agents interacting with dynamic environments and executing actions present a broader attack surface and new categories of risk. These include performing unsafe operations, violating privacy constraints through improper data handling or access control failures, deviating from user objectives (task misalignment), and susceptibility to novel manipulation techniques like indirect prompt injection and memory poisoning. Ensuring the trustworthy operation of these powerful agents is paramount, especially as they are integrated into high-stakes applications. To address this critical challenge, we introduce VeriGuard, a novel framework designed to enhance the safety and reliability of LLM agents by interactively verifying their policies and the actions. VeriGuard integrates a verification module that intercepts code-based actions proposed by the agent. In the first step, VeriGuard will generates and verifies the policies. The policies are rigorously checked against a set of predefined safety and security specifications Then each action will be verified to make sure it will align with the agent specification. This interactive verification loop ensures that the agent's behavior remains within safe operational bounds, effectively preventing the execution of harmful or unintended operations. By verifying each step, VeriGuard provides a robust safeguard, substantially improving the trustworthiness of LLM agents in complex, real-world environments.
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We study the computational cost of differential privacy in terms of memory
efficiency. While the trade-off between accuracy and differential privacy is
well-understood, the inherent cost of privacy regarding memory use remains
largely unexplored. This paper establishes for the first time an unconditional
space lower bound for user-level differential privacy by introducing a novel
proof technique based on a multi-player communication game.
Central to our approach, this game formally links the hardness of low-memory
private algorithms to the necessity of ``contribution capping''---tracking and
limiting the users who disproportionately impact the dataset. We demonstrate
that winning this communication game requires transmitting information
proportional to the number of over-active users, which translates directly to
memory lower bounds.
We apply this framework, as an example, to the fundamental problem of
estimating the number of distinct elements in a stream and we prove that any
private algorithm requires almost $\widetilde{\Omega}(T^{1/3})$ space to
achieve certain error rates in a promise variant of the problem. This resolves
an open problem in the literature (by Jain et al. NeurIPS 2023 and Cummings et
al. ICML 2025) and establishes the first exponential separation between the
space complexity of private algorithms and their non-private $\widetilde{O}(1)$
counterparts for a natural statistical estimation task. Furthermore, we show
that this communication-theoretic technique generalizes to broad classes of
problems, yielding lower bounds for private medians, quantiles, and max-select.
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We revisit the distributed counting problem, where a server must continuously approximate the total number of events occurring across $k$ sites while minimizing communication. The communication complexity of this problem is known to be $\Theta(\frac{k}{\eps}\log N)$ for deterministic protocols. Huang, Yi, and Zhang (2012) showed that randomization can reduce this to $\Theta(\frac{\sqrt{k}}{\eps}\log N)$, but their analysis is restricted to the oblivious setting, where the stream of events is independent of the protocol's outputs.
Xiong, Zhu, and Huang (2023) presented a robust protocol for distributed counting that removes the oblivious assumption. However, their communication complexity is suboptimal by a polylog(k) factor and their protocol is substantially more complex than the oblivious protocol of Huang et al. (2012). This left open a natural question: could it be that the simple protocol of Huang et al. (2012) is already robust?
We resolve this question with two main contributions. First, we show that the protocol of Huang et al. (2012) is itself not robust by constructing an explicit adaptive attack that forces it to lose its accuracy. Second, we present a new, surprisingly simple, robust protocol for distributed counting that achieves the optimal communication complexity of $O(\frac{\sqrt{k}}{\eps} \log N)$. Our protocol is simpler than that of Xiong et al. (2023), perhaps even simpler than that of Huang et al. (2012), and is the first to match the optimal oblivious complexity in the adaptive setting.
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CSIG: Congestion Signaling for Datacenter Transports
Abhiram Ravi
Brad Karp
MJ Akhbari
Weida Huang
Konstantinos Prasopoulos
2026
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Optimizing burst-heavy datacenter workloads necessitates fine-grained network control and visibility. We introduce CSIG, a protocol that delivers precise, multi-bit bottleneck congestion signals via a fixed-length Ethernet header. The architecture captures 𝜇s-granularity switch metrics, such as available bandwidth, and signals them to end-hosts using in-band, line-rate operations. We propose Fast Ramp-Up, a congestion control primitive that leverages these bottleneck signals to reduce median RPC latency by 20% and unclaimed bandwidth by 60% in production. Beyond transport-level performance, CSIG enables flow-aware observability by embedding 𝜇s-scale metrics into every packet, allowing individual application transfers to pinpoint their bottleneck location, such as the topology tier limiting their performance. CSIG thus transforms network telemetry from post-hoc correlation into a real time, contextaware capability. We demonstrate CSIG’s broad deployability by validating it across five generations of commodity switch hardware (up to 102.4 Tbps), four NIC generations, and five transport stacks. Our design proves that a streamlined Layer 2 approach, focusing exclusively on the principal path bottleneck, provides transport-agnostic gains without requiring forklift hardware upgrades.
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Approaching the Privacy-Enhancing Technologies Adoption from a Requirements Engineering Perspective
Oleksandr Kosenkov
Danica Vranjanin
Abhinava Singh
Vadym Honcharenko
Volodymyr Spirin
2026
Preview abstract
In recent decades, Privacy Enhancing Technologies (PETs) have been gaining attention as a means to fulfill regulatory and user privacy requirements when processing personal data in software systems. Despite broad attention in research, support by regulators, contributions and adoption by large technological companies (like Google or Microsoft), and interest from software developers, adoption of PETs in practice is still challenging and lagging. For many years, existing research points to the same typical challenges to the adoption of PETs, such as complexity or absence of training; however, these challenges remain unresolved in practice despite the recent efforts.
In this industrial challenge paper, we take a deeper, practical requirements engineering-driven perspective on the challenges to PETs adoption across different stakeholders (namely, PETs developers and integrators, and PET adopters and users) and different disciplines (engineering, law, and business).
We suggest that the adoption of PETs should be facilitated with an in-depth understanding of engineering, business, and legal challenges to PETs, such as PETs' impact on software architecture, their business impact, and contribution to compliance. We suggest that requirements engineering research can play a crucial role in better exploring and modeling such viewpoints. In practice, requirements engineering could facilitate the coordination of the involved viewpoints to enable the adoption of existing and the development of new PETs.
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OBASE: Object-Based Address-Space Engineering to Improve Memory Tiering
Kan Wu
Suli Yang
Andrea Arpaci-Dusseau
Remzi Arpaci-Dusseau
Kim Keeton
USENIX Symposium on Operating Systems Design and Implementation (OSDI '26) (2026)
Preview abstract
Hardware and OS mechanisms for memory tiering are widely deployed, yet datacenters still overprovision DRAM. The root cause is hotness fragmentation: allocators place objects by size rather than access pattern, so hot and cold objects become interleaved within the same pages. A single hot object marks its page as active, trapping surrounding cold data in expensive DRAM. Our analysis of Google datacenter workloads shows that up to 97% of the bytes in active pages are cold and unreclaimable. We propose address-space engineering: dynamically reorganizing virtual memory so that hot objects cluster into uniformly hot pages and cold objects into uniformly cold pages. We present OBASE, a compiler-runtime system for unmanaged languages that serves as an object-aware frontend for page-aware OS backends. OBASE tracks accesses via lightweight pointer instrumentation and migrates objects at runtime using a lock-free protocol that is safe under concurrency. By reorganizing the address space, OBASE enables unmodified backends (kswapd, TMO, TPP, Memtis) to tier memory effectively. Across ten concurrent data structures, six backends, and production traces from Meta and Twitter, OBASE improves page utilization by 2–4x and reduces memory footprint by up to 70%, with only 2–5% overhead.
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The AI Divide: Bridging the Implementation Gap in State and Territorial Public Health Departments
Monica Bharel
AJPH (2026)
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Generative AI presents an opportunity for state and territorial health agencies to manage escalating workloads and workforce shortages. The 2025 ASTHO Profile found an implementation gap with most agencies having an AI policy, but a third reporting no AI use. While 80% prioritize data governance, only 15% use AI for public health tasks like disease surveillance, opting instead for administrative efficiency (30%) or content creation (28%). Implementation is fragmented, with 34% avoiding AI. The top challenges are lack of established staff guidance (61%) and workforce skills (54%). Closing this gap requires expanding "AI literacy" training and adopting shared enterprise infrastructure models.
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Watch and Learn: Learning to use computers from online videos
Luke Song
Yu Su
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2026), pp. 5421-5431
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Computer-using agents (CUAs) must plan task workflows across diverse and evolving applications, yet progress is limited by the lack of large-scale, high-quality training data. Existing datasets are narrow, static, and costly to annotate, while synthetic data often yields oversimplified or misaligned behaviors.
We present Watch & Learn (W&L), a framework that converts readily available Internet videos of human computer use into executable UI trajectories at scale. Instead of directly generating actions or relying on handcrafted heuristics, we cast trajectory annotation as an inverse dynamics problem that predicts user actions from consecutive screen states, which simplifies learning and generalizes across domains.
Through a task-aware retrieval and labeling pipeline, W&L yields over 53K high-quality trajectories that enhance CUAs both as in-context exemplars and as supervised training data. On OSWorld, it consistently improves general-purpose and specialized CUAs, while on WindowsAgentArena it achieves state-of-the-art performance among 7B-scale models under the 15-step limit. These results show that web-scale human demonstration videos can serve as a practical and scalable foundation for advancing real-world CUAs.
Project page: https://chanh.ee/wandl/
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Mid-air gestures in Extended Reality (XR) often lead to fatigue, discomfort and imprecision, limiting their suitability for extended use. Surface-based interactions offer a compelling alternative, providing improved accuracy, speed, and comfort. However, current egocentric vision-based methods struggle with reliable surface inputs due to challenges in hand tracking and surface-plane estimation from oblique and occluded viewing angles. To this extent, we introduce SurfaceXR, a novel sensor fusion approach that combines headset based hand tracking with micro-vibration data sampled from commodity smartwatch IMUs to enable precise and robust inputs on arbitrary surfaces. Our system is designed with flexibility in mind - it can function using only hand tracking, only IMU sensing, or optimally with both modalities combined. Our user study across 12 participants validates SurfaceXR's effectiveness in augmenting surface touch tracking and 8 class hand-surface gesture recognition, demonstrating significant improvements over single-modality approaches. Enabled by SurfaceXR, we demonstrate a series of interactive apps for both AR and VR, ranging from on-surface sketching, text entry and gesture based navigation.
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Taming the Variants Multi-Architecture Continuous Testing at Google
Chandrakanth Chittappa
Ali Esmaeeli
Laura Macaddino
Sam Manfreda
David Margolin
Dharma Naidu
Sabuj Pattanayek
Sachin Sable
Ruslan Sakevych
Dushyant Acharya
Adrian Berding
Kevin Crossan
Wolff Dobson
Abhay Singh
19th IEEE International Conference on Software Testing, Verification and Validation (ICST) 2026, Daejeon, Republic of Korea, IEEE
Preview abstract
Enterprises are increasingly adopting multiple general-purpose computer architectures in the data center. This leads to new testing challenges as it creates demand to qualify the software for the additional architectures. Naively double-testing all software for both architectures is costly and unnecessary. Further, reconfiguring CI/CD to take advantage of the new architecture can be non-trivial at scale. This paper introduces CI/CD variants and an optimized testing cycle to solve these twin challenges. We empirically evaluate our solution's impact on human and machine expenses using 44k projects at Google on real production data. First, we estimate saving ~25% of machine expenses at the negligible cost of a few delayed breakage detections per day. Second, we estimate a 90+% reduction in human cost for migrating the configuration. All features described in this paper are now Generally Available at Google and we report this as an empirical case study in scaling CI/CD to new architectures.
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