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 11082 publications
Preview abstract
How many T gates are needed to approximate an arbitrary n-qubit quantum state to within
a given precision ϵ? Improving prior work of Low, Kliuchnikov and Schaeffer, we show that the
optimal asymptotic scaling is Θ(sqrt{2^n log(1/ε)} + log(1/ε)) if we allow an unlimited number of ancilla qubits. We also show that this is the optimal T-count for implementing an arbitrary
diagonal n-qubit unitary to within error ϵ. We describe an application to batched synthesis of
single-qubit unitaries: we can approximate a tensor product of m = O(log log(1/ϵ)) arbitrary
single-qubit unitaries to within error ϵ with the same asymptotic T-count as is required to
approximate just one single-qubit unitary.
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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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Semantic data models express high-level business concepts and metrics, capturing the business logic needed to query a database correctly. Most data modeling solutions are built as layers above SQL query engines, with bespoke query languages or APIs. The layered approach means that semantic models can’t be used directly in SQL queries. This paper focuses on an open problem in this space – can we define semantic models in SQL, and make them naturally queryable in SQL?
In parallel, graph query is becoming increasingly popular, including in SQL. SQL/PGQ extends SQL with an embedded subset of the GQL graph query language, adding property graph views and making graph traversal queries easy.
We explore a surprising connection: semantic data models are graphs, and defining graphs is a data modeling problem. In both domains, users start by defining a graph model, and need query language support to easily traverse edges in the graph, which means doing joins in the underlying data.
We propose some useful SQL extensions that make it easier to use higher-level data model abstractions in queries. Users can define a “semantic data graph” view of their data, encapsulating the complex business logic required to query the underlying tables correctly. Then they can query that semantic graph model easily with SQL.
Our SQL extensions are useful independently, simplifying many queries – particularly, queries with joins. We make declared foreign key relationships usable for joins at query time – a feature that seems obvious but is notably missing in standard SQL.
In combination, these extensions provide a practical approach to extend SQL incrementally, bringing semantic modeling and graph query together with the relational model and SQL.
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Preview abstract
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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AI coding assistants are rapidly becoming integral to modern software development. A key challenge in this space is the continual need to migrate and modernize codebases in response to evolving software ecosystems. Traditionally, such migrations have relied on rule-based systems and human intervention. With the advent of powerful large language models (LLMs), AI-driven agentic frameworks offer a promising alternative—but their effectiveness remains underexplored. In this paper, we introduce FreshBrew, a novel benchmark for evaluating AI-based agentic frameworks on project-level Java migrations. We benchmark several such frameworks, powered by state-of-the-art LLMs, and compare their performance against established rule-based tools. Our evaluation of AI agents on this benchmark of 228 repositories shows that the top-performing model, Gemini 2.5 Flash, can successfully migrate 56.5% of projects to JDK 17. Our empirical analysis reveals novel insights into the critical strengths and limitations of current agentic approaches, offering actionable insights into their real-world applicability. By releasing FreshBrew publicly upon acceptance, we aim to facilitate rigorous, reproducible evaluation and catalyze progress in AI-driven codebase modernization.
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Who Controls the Curriculum for AI? The Limits of Participatory Design for Educational AI
Michael Madaio
Learning Under Algorithmic Conditions, University of Minnesota Press (2026)
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Participatory design is a long-standing effort to shift control over technology design from technologists to users and communities impacted by technologies. For educational AI, this means involving students, families, teachers, and other stakeholders in shaping the design of AI systems. While promising, in this article, I situate the recent calls for participatory design of educational AI systems within a different historical tradition—that of contests over local control of educational curricula. I argue that approaches that attempt to steer the design and development of educational AI through participatory methods may inadvertently reproduce the history of political contestation of educational curricula, in ways that may privilege the most powerful communities, rather than those inequitably impacted. What might it look like to treat participatory AI design as a site for political contestation? How might these approaches avoid reproducing the same majoritarian tendencies that led to educational inequities in the first place?
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CrossCheck: Input Validation for WAN Control Systems
Rishabh Iyer
Isaac Keslassy
Sylvia Ratnasamy
Networked Systems Design and Implementation (NSDI) (2026) (to appear)
Preview abstract
We present CrossCheck, a system that validates inputs to the Software-Defined Networking (SDN) controller in a Wide Area Network (WAN). By detecting incorrect inputs—often stemming from bugs in the SDN control infrastructure—CrossCheck alerts operators before they trigger network outages.
Our analysis at a large-scale WAN operator identifies invalid inputs as a leading cause of major outages, and we show how CrossCheck would have prevented those incidents. We deployed CrossCheck as a shadow validation system for four weeks in a production WAN, during which it accurately detected the single incident of invalid inputs that occurred while sustaining a 0% false positive rate under normal operation, hence imposing little additional burden on operators. In addition, we show through simulation that CrossCheck reliably detects a wide range of invalid inputs (e.g., detecting demand perturbations as small as 5% with 100% accuracy) and maintains a near-zero false positive rate for realistic levels of noisy, missing, or buggy telemetry data (e.g., sustaining zero false positives with up to 30% of corrupted telemetry data).
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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
Preview abstract
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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Preference Adaptive and Sequential Text-to-Image Generation
Ofir Nabati
Moonkyung Ryu
Sean Li
2025
Preview abstract
We address the problem of interactive text-to-image (T2I) generation, designing a reinforcement learning (RL) agent which iteratively improves a set of generated images for a user through a sequence of prompt expansions. Using human raters, we create a novel dataset of sequential preferences, which we leverage, together with large-scale open-source (non-sequential) datasets. We construct user-preference and user-choice models using an EM strategy and identify varying user preference types. We then leverage a large multimodal language model (LMM) and a value-based RL approach to suggest an adaptive and diverse slate of prompt expansions to the user. Our Preference Adaptive and Sequential Text-to-image Agent (PASTA) extends T2I models with adaptive multi-turn capabilities, fostering collaborative co-creation and addressing uncertainty or underspecification in a user's intent. We evaluate PASTA using human raters, showing significant improvement compared to baseline methods. We also open-source our sequential rater dataset and simulated user-rater interactions to support future research in user-centric multi-turn T2I systems.
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Improving Informally Romanized Language Identification
Adrian Benton
Christo Kirov
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Suzhou, China, 2318–2336
Preview abstract
The Latin script is often used informally to write languages with non-Latin native scripts. In many cases (e.g., most languages in India), there is no orthography, meaning that there is no conventional spelling of words in the Latin script, hence there will be high spelling variability in written text. Such romanization can render languages that are normally easily distinguished based on script highly confusable, such as Hindi and Urdu. In this work, we present methods to improve language identification of romanized text by improving methods to synthesize training sets. We find that training on synthetic samples which incorporate natural spelling variation yields higher language identification system accuracy than including available naturally occurring examples in the training set or even training higher capacity models. We demonstrate new state-of-the-art language identification performance on romanized text from 20 Indic languages in the Bhasha-Abhijnaanam evaluation set (Madhani et al., 2023a), improving test F1 from the reported 74.7% (using a pretrained neural model) to 85.4% using a linear classifier trained solely on synthetic data and 88.2% when also training on available harvested text.
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Gemini & Physical World: Large Language Models Can Estimate the Intensity of Earthquake Shaking from Multi-Modal Social Media Posts
Marc Stogaitis
Tajinder Gadh
Richard Allen
Alexei Barski
Robert Bosch
Patrick Robertson
Youngmin Cho
Nivetha Thiruverahan
Aman Raj
Geophysical Journal International (2025), ggae436
Preview abstract
This paper presents a novel approach for estimating the ground shaking intensity using real-time social media data and CCTV footage. Employing the Gemini 1.5 Pro’s (Reid et al. 2024) model, a multi-modal language model, we demonstrate the ability to extract relevant information from unstructured data utilizing generative AI and natural language processing. The model’s output, in the form of Modified Mercalli Intensity (MMI) values, align well with independent observational data. Furthermore, our results suggest that beyond its advanced visual and auditory understanding abilities, Gemini appears to utilize additional sources of knowledge, including a simplified understanding of the general relationship between earthquake magnitude, distance, and MMI intensity, which it presumably acquired during its training, in its reasoning and decision-making processes. These findings raise intriguing questions about the extent of Gemini's general understanding of the physical world and its phenomena. Gemini’s ability to generate results consistent with established scientific knowledge highlights the potential of LLMs like Gemini in augmenting our understanding of complex physical phenomena such as earthquakes. More specifically, the results of this study highlight the potential of LLMs like Gemini to revolutionize citizen seismology by enabling rapid, effective, and flexible analysis of crowdsourced data from eyewitness accounts for assessing earthquake impact and providing crisis situational awareness. This approach holds a great promise for improving early warning systems, disaster response, and overall resilience in earthquake-prone regions. This study provides a significant step toward harnessing the power of social media and AI for earthquake disaster mitigation.
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EI-Lite: Electrical Impedance Sensing for Micro-gesture Recognition and Pinch Force Estimation
Junyi Zhu
Jiayu Wang
Emily Guan
JaeYoung Moon
Stiven Morvan
Andrea Colaco
stefanie mueller
Karan Ahuja
Yiyue Luo
Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology, Association for Computing Machinery, New York, NY, USA (2025), pp. 1-14
Preview abstract
Micro-gesture recognition and fine-grain pinch press enables intuitive and discreet control of devices, offering significant potential for enhancing human-computer interaction (HCI). In this paper, we present EI-Lite, a lightweight wrist-worn electrical impedance sensing device for micro-gesture recognition and continuous pinch force estimation. We elicit an optimal and simplified device architecture through an ablation study on electrode placement with 13
users, and implement the elicited designs through 3D printing. We capture data on 15 participants on (1) six common micro-gestures (plus idle state) and (2) index finger pinch forces, then develop machine learning models that interpret the impedance signals generated by these micro-gestures and pinch forces. Our system is capable of accurate recognition of micro-gesture events (96.33% accuracy), as well as continuously estimating the pinch force of the index finger in physical units (Newton), with the mean-squared error (MSE) of 0.3071 (or mean-force-variance of 0.55 Newtons) over 15 participants. Finally, we demonstrate EI-Lite’s applicability via three applications in AR/VR, gaming, and assistive technologies.
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Our Approach to Protecting AI Training Data
Cindy Muya
Jason Novak
Cindee Madison
Ben Kamber
Niha Vempati
Jeremy Wiesner
David Deutscher
Google, Google, Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043 (2025) (2025)
Preview abstract
Google has over 25 years experience protecting data from inappropriate access and unauthorized use. In the era of AI, Google has extended these best practices in data protection to ensure that the right data is used the right way to train models. This paper presents a number of these best practices, describes how Google applies them in its systems, and describes how Google Cloud customers can use Google Cloud capabilities to implement these practices themselves.
Protecting data requires both technical controls to enable safe data use at scale, and governance processes to ensure that companies have visibility and control over how their data is used. This fundamentally requires: understanding data and ensuring it has sufficient metadata in the form of attributes, controlling the data and implementing policies to allow (or disallow) certain usage based on those attributes, transforming data to enable its usage in policy compliant ways, and human oversight and governance.
Protecting data in AI inherits these requirements and introduces new requirements to account for unique AI-specific risks including memorization/recitation and the costs of training foundational models. Meeting these new risks requires new capabilities including enhanced understanding of data and model lineage as well as an increased ability to control data usage through checks on data for policy compliance at the time a training job is configured before it is run.
This white paper offers an in-depth look at data protection best practices and Google’s data protection capabilities, and is one of a series of publications about Google's Secure AI Framework (SAIF). Building upon its secure development practices, Google has developed and deployed a number of capabilities to understand, control, and transform data in its infrastructure so that data is both protected and used appropriately. This involves robust annotation systems to represent metadata and enable granular understanding of data at both an item and dataset level, policy engines that evaluate machine readable policies on that data using the metadata attributes, and sensors to understand how data is flowing across Google’s systems and raise alerts when policy violations occur. Moreover, Google has developed de-identification and anonymization systems to transform data to make it policy compliant and safer to use for AI training.
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Preview abstract
The accelerating pace of innovation is
fundamentally reshaping product development,
creating a complex environment that demands rapid
decision-making and efficient information
management. To remain competitive, organizations
must integrate Generative AI (GenAI) tools into
their Product Lifecycle Management (PLM)
processes. This integration is crucial because
traditional PLM systems, often built on decades-old
architectures, struggle to manage modern product
complexity, vast data volumes, and interconnected
supply chains.1 Limitations such as data silos,
inflexible change management, and inadequate
collaboration capabilities hinder the agility required
today.3 GenAI offers transformative potential by
automating complex tasks, enhancing data analysis,
and facilitating more dynamic design and
collaboration within the PLM ecosystem.5 This
integration is not merely an upgrade but an
essential evolution to overcome the inherent
architectural and process constraints of legacy
systems, which impede the speed and data fluidity
necessary in the current market.
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DORA AI Capabilities Model
Ameer Abbas
Derek DeBellis
Vivian Hu
Amanda Lewis
Dave Stanke
Kevin Storer
Lucia Subatin
Cedric Yao
DORA, Google (2025)
Preview abstract
Generative AI is rapidly reshaping the software development landscape, presenting both exciting opportunities and complex challenges.
This report offers research-backed guidance for leaders and practitioners to effectively navigate this transformation, providing actionable insights to maximize AI's benefits while mitigating potential risks.
Based on extensive data and developer interviews, the report moves beyond the hype to offer a nuanced perspective on AI's impact on individuals, teams, and organizations.
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