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 11415 publications
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The field of Human-Computer Interaction is approaching a critical inflection point, moving beyond the era of static, deterministic systems into a new age of self-evolving systems. We introduce the concept of Adaptive generative interfaces that move beyond static artifacts to autonomously expand their own feature sets at runtime. Rather than relying on fixed layouts, these systems utilize generative methods to morph and grow in real-time based on a user’s immediate intent. The system operates through three core mechanisms: Directed synthesis (generating new features from direct commands), Inferred synthesis (generating new features for unmet needs via inferred commands), and Real-time adaptation (dynamically restructuring the interface's visual and functional properties at runtime). To empirically validate this paradigm, we executed a within-subject (repeated measures) comparative study (N=72) utilizing 'Penny,' a digital banking prototype. The experimental design employed a counterbalanced Latin Square approach to mitigate order effects, such as learning bias and fatigue, while comparing Deterministic interfaces baseline against an Adaptive generative interfaces. Participant performance was verified through objective screen-capture evidence, with perceived usability quantified using the industry-standard System Usability Scale (SUS). The results demonstrated a profound shift in user experience: the Adaptive generative version achieved a System Usability Scale (SUS) score of 84.38 ('Excellent'), significantly outperforming the Deterministic version’s score of 53.96 ('Poor'). With a statistically significant mean difference of 30.42 points (p < 0.0001) and a large effect size (d=1.04), these findings confirm that reducing 'navigation tax' through adaptive generative interfaces directly correlates with a substantial increase in perceived usability. We conclude that deterministic interfaces are no longer sufficient to manage the complexity of modern workflows. The future of software lies not in a fixed set of pre-shipped features, but in dynamic capability sets that grow, adapt, and restructure themselves in real-time to meet the specific intent of the user. This paradigm shift necessitates a fundamental transformation in product development, requiring designers to transcend traditional, linear workflows and evolve into 'System Builders'—architects of the design principles and rules that facilitate this new age of self-evolving software.
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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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Neural general circulation models for modeling precipitation
Stephan Hoyer
Dmitrii Kochkov
Janni Yuval
Ian Langmore
Science Advances (2026)
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Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. While hybrid models combining machine learning and physics have emerged with the premise of improving precipitation simulations, none have proven sufficiently skillful or stable enough to outperform existing models in simulating precipitation.
Here, we present the first hybrid model that is trained directly on precipitation observations. The model runs at 2.8 degrees resolution and is built on the differentiable NeuralGCM framework. This model is stable for decadal simulations and demonstrates significant improvements over existing GCMs, ERA5 reanalysis, and a Global Cloud-Resolving Model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle. Furthermore, it outperforms the ECMWF ensemble for mid-range weather forecasting.
This advance paves the way for more reliable simulations of current climate and for the ability to fully utilize the abundance of existing observations to further improve GCMs.
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For many practical applications of quantum computing, the slowest and most costly steps involve coherently accessing classical data. We help address this challenge by applying mass production techniques, which can sometimes allow us to perform operations many times in parallel for a cost that is comparable to a single execution[1-3]. We combine existing mass-production results with modern approaches for loading classical data using ``quantum read-only memory.'' We show that quantum mass production techniques offer no benefit when we consider a cost model that focuses purely on the number of non-Clifford gates. However, analyzing the constant factors in a more nuanced cost model, we find that it may be possible to obtain a reduction in cost of an order or magnitude or more for a variety reasonably-sized fault-tolerant quantum algorithms. We present several applications of quantum mass-production techniques beyond naive parallelization, including a strategy for reducing the cost of serial calls to the same data loading step.
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System for a Secure, Outcome-Based Synthetic Labor Market Using Trusted Execution Environments
Patent (2026)
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Some artificial intelligence provisioning models that function as tools for human users or rely on labor arbitrage can present challenges for organizations, such as managing personnel rather than task outcomes and introducing data security risks. An architecture is described for an outcome-based synthetic labor market in which autonomous computational agents can be compensated based on verified task completion. The framework can leverage trusted execution environments to create secure hardware enclaves for processing sensitive data, which can render the data cryptographically inaccessible to a host system or agent provider. This approach can facilitate a secure, transactional market for autonomous professional execution, which may enable a shift from managing labor resources to procuring verified outcomes from a pool of specialized agents.
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As artificial intelligence (AI) is rapidly integrated into healthcare, ensuring that this innovation helps to combat health inequities requires engaging marginalized communities in health AI futuring. However, little research has examined Black populations’ perspectives on the use of AI in health contexts, despite the widespread health inequities they experience–inequities that are already perpetuated by AI. Addressing this research gap, through qualitative workshops with 18 Black adults, we characterize participants’ cautious optimism for health AI addressing structural well-being barriers (e.g., by providing second opinions that introduce fairness into an unjust healthcare system), and their concerns that AI will worsen health inequities (e.g., through health AI biases they deemed inevitable and the problematic reality of having to trust healthcare providers to use AI equitably). We advance health AI research by articulating previously-unreported health AI perspectives from a population experiencing significant health inequities, and presenting key considerations for future work.
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Who is At-Risk? Surveying the Prevalence of Risk Factors and Tech-Facilitated Attacks in the United States
Sharon Heung
Claire Weizenegger
Mo Houtti
Tara Matthews
Ashley Walker
2026
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A growing body of qualitative research has identified contextual risk factors that elevate people’s chances of experiencing digital-safety attacks. However, the lack of quantitative data on the population level distribution of these risk factors prevents policymakers and tech companies from developing targeted, evidence-based interventions to improve digital safety. To address this gap, we surveyed 5,001 adults in the United States to analyze: (1) the frequency of and relationship between digital-safety attacks (e.g., scams, harassment, account hacking), and (2) how these attacks align with 10 contextual risk factors. Nearly half of our respondents identify as resource constrained, which significantly correlates with higher likelihood of experiencing four common attacks. We also present qualitative insights to expand our understanding of the factors beyond the existing literature (e.g., “prominence” included high-visibility roles in local communities). This study provides the first large-scale quantitative analysis correlating digital-safety attacks with contextual risk factors and demographics.
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The rapid expansion of the Internet of Things (IoT) and smart home ecosystems has led to a fragmented landscape of user data management across consumer electronics (CE) such as Smart TVs, gaming consoles, and set-top boxes. Current onboarding processes on these devices are characterized by high friction due to manual data entry and opaque data-sharing practices. This paper introduces the User Data Sharing System (UDSS), a platform-agnostic framework designed to facilitate secure, privacy-first PII (Personally Identifiable Information) exchange between device platforms and third-party applications. Our system implements a Contextual Scope Enforcement (CSE) mechanism that programmatically restricts data exposure based on user intent—specifically distinguishing between Sign-In and Sign-Up workflows. Unlike cloud-anchored identity standards such as FIDO2/WebAuthn, UDSS is designed for shared, device-centric CE environments where persistent user-to-device bind-ing cannot be assumed. We further propose a tiered access model that balances developer needs with regulatory compliance (GDPR/CCPA). A proof-of-concept implementation on a reference ARMv8 Linux-based middleware demonstrates that UDSS reduces user onboarding latency by 65% and measurably reduces PII over-exposure risk through protocol-enforced data minimization. This framework provides a standardized approach to identity management in the heterogeneous CE market.
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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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Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair
Michele Tufano
José Cambronero
Renyao Wei
Grant Uy
34th ACM International Conference on the Foundations of Software Engineering (FSE) (2026)
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Bug Reproduction Tests (BRTs) have been used in many Automated Program Repair (APR) systems, primarily for validating fixes and aiding fix generation. In practice, when developers submit a patch, they often implement the BRT alongside the fix. Our experience deploying agentic APR reveals that developers desire a BRT within AI-generated patches to increase their confidence. However, canonical APR systems tend to generate BRTs and fixes separately, and focus on producing only the fix in the final patch. In this paper, we study agentic APR in the context of cogeneration, where the APR agent is instructed to generate both a fix and a BRT in the same patch. We evaluate the effectiveness of different cogeneration strategies on 120 human-reported bugs at Google and characterize different cogeneration strategies by their influence on APR agent behavior. We develop and evaluate patch selectors that account for test change to select patches with plausible fixes (and plausible BRTs). Finally, we analyze the root causes of failed cogeneration trajectories. We show that cogeneration allows the APR agent to generate BRTs for at least as many bugs as a dedicated BRT agent, without compromising the generation rate of plausible fixes, thereby reducing engineering effort in maintaining and coordinating separate generation pipelines for fix and BRT at scale.
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Managing and Securing Google's Fleet of Multi-Node Servers
Richard Hanley
Havard Skinnemoen
Andrés Lagar-Cavilla
Michael Wong
Jon McCune
Jeff Andersen
Kishan Prasad
Patrick Leis
Shiva Rao
Chris Koch
Jad Baydoun
Anna Sapek
Communications of the ACM, 69:3 (2026), pp. 82 - 92
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Server hardware and software co-design for a secure, efficient cloud.
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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)
Preview abstract
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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Securing Elliptic Curve Cryptocurrencies against Quantum Vulnerabilities: Resource Estimates and Mitigations
Michael Broughton
Thiago Bergamaschi
Justin Drake
Dan Boneh
PRX Quantum, 7 (2026), pp. 031001
Preview abstract
The expected emergence of cryptographically relevant quantum computers (CRQCs) will represent a singular discontinuity in the history of digital security, with wide ranging impacts. This whitepaper seeks to elucidate specific implications that the capabilities of developing quantum architectures have on blockchain vulnerabilities and potential mitigation strategies. First, we provide new resource estimates for breaking the 256-bit Elliptic Curve Discrete Logarithm Problem over the secp256k1 curve, the core of modern blockchain cryptography. We demonstrate that Shor's algorithm for this problem can execute with either $\leq 1200$ logical qubits and $\leq 90$ million Toffoli gates or $\leq 1450$ logical qubits and $\leq 70$ million Toffoli gates. In the interest of responsible disclosure, we use a zero-knowledge proof to validate these results without disclosing attack vectors. On superconducting architectures with $10^{-3}$ physical error rates and planar connectivity, those circuits can execute in minutes using fewer than half a million physical qubits. We introduce a critical distinction between ``fast-clock'' (such as superconducting and photonic) and ``slow-clock'' (such as neutral atom and ion trap) architectures. Our analysis reveals that the first fast-clock CRQCs would enable ``on-spend'' attacks on public mempool transactions of some cryptocurrencies. We survey major cryptocurrency vulnerabilities through this lens, identifying systemic risks associated with advanced features in some blockchains such as smart contracts, Proof-of-Stake consensus, and Data Availability Sampling mechanism, as well as the enduring concern of ``abandoned'' assets. We argue that technical solutions would benefit from accompanying public policy and discuss various frameworks of ``digital salvage'' to regulate the recovery or destruction of dormant assets while preventing adversarial seizure. We also discuss implications for other digital assets and tokenization as well as challenges and successful examples of the ongoing transition to Post-Quantum Cryptography (PQC). Finally, we urge all vulnerable cryptocurrency communities to join the migration to PQC without delay.
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In modern Kubernetes environments, eBPF (Extended Berkeley Packet Filter) has become the de facto standard for high-performance dataplane enforcement. However, this architecture introduces a complex distributed state problem: the asynchronous synchronization between the Kubernetes control plane (Intent) and the kernel-space BPF maps (Reality).
A critical failure mode, termed “Silent Divergence,” occurs when the control plane believes a network policy or identity is applied, but the underlying kernel state is missing or corrupted. In this “Gray Failure” state, standard observability tools—including logs, liveness probes, and agent status checks—report health, while the network silently drops traffic.
This paper introduces eBPF-Auditor, a specialized consistency verification framework. Unlike standard agents that rely on event-based reconciliation, eBPF-Auditor performs a periodic “Two-Way State Audit” that mathematically verifies the intersection of Kubernetes Intent and BPF Reality. We demonstrate through fault injection and benchmarks on 5,000 pods that this approach successfully detects state drift with 100% accuracy and negligible sub-millisecond overhead (ms), making it a viable solution for high-frequency runtime verification in production hyperscale clusters.
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Mining Attribute Subspaces for Efficient Fine-tuning of 3D Foundation Models
Yu Jiang
Hanwen Jiang
Vincent Chu
Brandon Y. Feng
Zhangyang Wang
Qixing Huang
IEEE/CVF Conference on Computer Vision and Pattern Recognition (2026)
Preview abstract
With the emergence of 3D foundation models, there is growing interest in fine-tuning them for downstream tasks, where LoRA is the dominant fine-tuning paradigm. As 3D datasets exhibit distinct variations in texture, geometry, camera motion, and lighting, there are interesting fundamental questions: 1) Are there LoRA subspaces associated with each type of variation? 2) Are these subspaces disentangled (i.e., orthogonal to each other)? 3) How do we compute them effectively? This paper provides answers to all these questions. We introduce a robust approach that generates synthetic datasets with controlled variations, fine-tunes a LoRA adapter on each dataset, and extracts a LoRA sub-space associated with each type of variation. We show that these subspaces are approximately disentangled. Integrating them leads to a reduced LoRA subspace that enables efficient LoRA fine-tuning with improved prediction accuracy for downstream tasks. In particular, we show that such a reduced LoRA subspace, despite being derived entirely from synthetic data, generalizes to real datasets. An ablation study validates the effectiveness of the choices in our approach.
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