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 10732 publications
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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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YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks
Saptarashmi Bandyopadhyay
Vikas Bahirwani
Lavisha Aggarwal
Bhanu Guda
Lin Li
Andrea Colaco
2025
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Multimodal AI Agents are AI models that have the capability of interactively and cooperatively assisting human users to solve day-to-day tasks. Augmented Reality (AR) head worn devices can uniquely improve the user experience of solving procedural day-to-day tasks by providing egocentric multimodal (audio and video) observational capabilities to AI Agents. Such AR capabilities can help the AI Agents see and listen to actions that users take which can relate to multimodal capabilities of human users. Existing AI Agents, either Large Language Models (LLMs) or Multimodal Vision-Language Models (VLMs) are reactive in nature, which means that models cannot take an action without reading or listening to the human user's prompts. Proactivity of AI Agents, on the other hand, can help the human user detect and correct any mistakes in agent observed tasks, encourage users when they do tasks correctly, or simply engage in conversation with the user - akin to a human teaching or assisting a user. Our proposed YET to Intervene (YETI) multimodal Agent focuses on the research question of identifying circumstances that may require the Agent to intervene proactively. This allows the Agent to understand when it can intervene in a conversation with human users that can help the user correct mistakes on tasks, like cooking, using Augmented Reality. Our YETI Agent learns scene understanding signals based on interpretable notions of Structural Similarity (SSIM) on consecutive video frames. We also define the alignment signal which the AI Agent can learn to identify if the video frames corresponding to the user's actions on the task are consistent with expected actions. These signals are used by our AI Agent to determine when it should proactively intervene. We compare our results on the instances of proactive intervention in the HoloAssist multimodal benchmark for an expert agent guiding an user agent to complete procedural tasks.
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In this paper, we introduce a novel estimator for vision-aided inertial navigation systems (VINS), the Preconditioned Cholesky-based Square Root Information Filter (PC-SRIF). When working with linear systems, employing Cholesky decomposition offers superior efficiency but can compromise numerical stability. Due to this, existing VINS literature on (Square Root) Information Filters often opts for QR decomposition on platforms where single precision is preferred, avoiding the numerical challenges associated with Cholesky decomposition. While these issues are often attributed to the ill-conditioned information matrix in VINS, our analysis reveals that this is not an inherent property of VINS but rather a consequence of specific parametrizations. We identify several factors that contribute to an ill-conditioned information matrix and propose a preconditioning technique to mitigate these conditioning issues. Building on this analysis, we present PC-SRIF, which exhibits remarkable stability in performing Cholesky decomposition using single precision when solving linear systems in VINS. Consequently, PC-SRIF achieves superior theoretical efficiency compared to alternative estimators. To validate the efficiency advantages and numerical stability of PC-SRIF based VINS, we have conducted carefully controlled experiments, which provide empirical evidence in support of our theoretical findings. Empirically, in our VINS, PC-SRIF achieves more than 2x better efficiency than SRIF.
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Quasiparticle-induced decoherence of a driven superconducting qubit
Mykola Kishmar
Pavel Kurilovich
Vlad Kurilovich
Thomas Connolly
Andrey Klots
Igor Aleiner
arXiv (2025)
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We develop a theory for two quasiparticle-induced decoherence mechanisms of a driven superconducting qubit. In the first mechanism, an existing quasiparticle (QP) tunnels across the qubit’s Josephson junction while simultaneously absorbing a qubit excitation and one (or several) photons from the drive. In the second mechanism, a qubit transition occurs during the non-linear absorption process converting multiple drive quanta into a pair of new QPs. Both mechanisms can remain significant in gap engineered qubits whose coherence is insensitive to QPs without the drive. Our theory establishes a fundamental limitation on fidelity of the microwave qubit operations—such as readout and gates—stemming from QPs.
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The dominant paradigm in image retrieval systems today is to search large databases using global image features, and re-rank those initial results with local image feature matching techniques.
This design, dubbed \emph{global-to-local}, stems from the computational cost of local matching approaches, which can only be afforded for a small number of retrieved images.
However, emerging efficient local feature search approaches have opened up new possibilities, in particular enabling detailed retrieval at large scale, to find partial matches which are often missed by global feature search.
In parallel, global feature-based re-ranking has shown promising results with high computational efficiency.
In this work, we leverage these building blocks to introduce a \emph{local-to-global} retrieval paradigm, where efficient local feature search meets effective global feature re-ranking.
Critically, we propose a re-ranking method where global features are computed on-the-fly, based on the local feature retrieval similarities.
Such re-ranking-only global features, dubbed \emph{similarity embeddings}, leverage multidimensional scaling techniques to create embeddings which respect the local similarities obtained during search, enabling a significant re-ranking boost.
Experimentally, we demonstrate unprecedented retrieval performance on the Revisited Oxford and Paris datasets, setting new state-of-the-art results.
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Small Models, Big Results: Achieving Superior Intent Extraction through Decomposition
Danielle Cohen
Yoni Halpern
Noam Kahlon
Joel Oren
Omri Berkovitch
Sapir Caduri
Ido Dagan
Tal Efros
2025
Preview abstract
Understanding user intents from UI interaction trajectories remains a challenging, yet crucial, frontier in intelligent agent development. While massive, datacenter-based, multi-modal large language models (MLLMs) possess greater capacity to handle the complexities of such sequences, smaller models which can run on-device to provide a privacy-preserving, low-cost, and low-latency user experience, struggle with accurate intent inference. We address these limitations by introducing a novel decomposed approach: first, we perform structured interaction summarization, capturing key information from each user action. Second, we perform intent extraction using a fine-tuned model operating on the aggregated summaries. This method improves intent understanding in resource-constrained models, even surpassing the base performance of large MLLMs.
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Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs
Bowen Tan
Zheng Xu
Eric Xing
Zhiting Hu
International Conference on Machine Learning (ICML) (2025)
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Synthetic data offers a promising path to train models while preserving data privacy. Differentially private (DP) finetuning of large language models (LLMs) as data generator is effective, but is impractical when computation resources are limited. Meanwhile, prompt-based methods such as private evolution depend heavily on the manual prompts, and ineffectively use private information in their iterative data selection process. To overcome these limitations, we propose CTCL (Data Synthesis with ConTrollability and CLustering), a novel framework for generating privacy-preserving synthetic data without extensive prompt engineering or billion-scale LLM finetuning. CTCL pretrains a lightweight 140M conditional generator and a clustering-based topic model on large-scale public data. To further adapt to the private domain, the generator is DP finetuned on private data for fine-grained textual information, while the topic model extracts a DP histogram representing distributional information. The DP generator then samples according to the DP histogram to synthesize a desired number of data examples. Evaluation across five diverse domains demonstrates the effectiveness of our framework, particularly in the strong privacy regime. Systematic ablation validates the design of each framework component and highlights the scalability of our approach.
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Participatory AI Considerations for Advancing Racial Health Equity
Andrea G. Parker
Jatin Alla
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI) (2025) (to appear)
Society-Centric Product Innovation in the Era of Customer Obsession
International Journal of Science and Research Archive (IJSRA), Volume 14 - Issue 1 (2025)
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This article provides a comprehensive analysis of the evolving landscape of innovation in the technology sector, with a focus on the intersection of technological progress and social responsibility. The article explores key challenges facing the industry, including public trust erosion, digital privacy concerns, and the impact of automation on workforce dynamics. It investigates responsible innovation frameworks' emergence and implementation across various organizations, highlighting the transformation from traditional development approaches to more society-centric models. The article demonstrates how companies balance innovation speed with social responsibility, incorporate ethical considerations into their development processes, and address digital disparities across different demographics. By examining how companies balance the pace of innovation with ethical responsibilities, integrate social considerations into their processes, and address digital inequities across diverse demographics, the article underscores the transformative potential of these frameworks. Through insights into cross-functional teams, impact assessment tools, and stakeholder engagement strategies, it demonstrates how responsible innovation drives both sustainable business value and societal progress.
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We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise definition is much more nuanced than it appears, to the extent that a single all-encompassing definition may not exist. Thus, we employ a different strategy and aim to "sandwich" the concept of reconstruction attacks by addressing two complementing questions: (i) What conditions guarantee that a given system is protected against such attacks? (ii) Under what circumstances does a given attack clearly indicate that a system is not protected? More specifically,
* We introduce a new definitional paradigm -- Narcissus Resiliency -- to formulate a security definition for protection against reconstruction attacks. This paradigm has a self-referential nature that enables it to circumvent shortcomings of previously studied notions of security. Furthermore, as a side-effect, we demonstrate that Narcissus resiliency captures as special cases multiple well-studied concepts including differential privacy and other security notions of one-way functions and encryption schemes.
* We formulate a link between reconstruction attacks and Kolmogorov complexity. This allows us to put forward a criterion for evaluating when such attacks are convincingly successful.
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Enhancing Performance of the Tesseract Decoder for Quantum Error Correction
DRAGANA GRBIC
Laleh Beni
Noah Shutty
2025
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In this paper I describe the performance enchantments I implemented in a quantum-error-correction decoder developed at Google. The decoder is an open-source project and I am documenting the speedups I achieved in this paper.
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On the Design of the Binaural Rendering Library for Eclipsa Audio Immersive Audio Container
Tomasz Rudzki
Gavin Kearney
AES 158th Convention of the Audio Engineering Society (2025)
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Immersive Audio Media and Formats (IAMF), also known as Eclipsa Audio, is an open-source audio container developed to accommodate multichannel and scene-based audio formats. Headphone-based delivery of IAMF audio requires efficient binaural rendering. This paper introduces the Open Binaural Renderer (OBR), which is designed to render IAMF audio. It discusses the core rendering algorithm, the binaural filter design process as well as real-time implementation of the renderer in a form of an open-source C++ rendering library. Designed for
multi-platform compatibility, the renderer incorporates a novel approach to binaural audio processing, leveraging a combination of spherical harmonic (SH) based virtual listening room model and anechoic binaural filters. Through its design, the IAMF binaural renderer provides a robust solution for delivering high-quality immersive audio across diverse platforms and applications.
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Preview abstract
JuMP and MathOptInterface.jl give access to many solvers, both very common in the industry and more specialised. Google offers its own in-house solvers as part of the open-source package OR-Tools: Glop, a simplex solver; CP-SAT, an award-winning constraint-programming solver; PDLP, a first-order solver for large-scale linear programming.
ORTools.jl is a recent package that gives access to these solvers through MathOptInterface.jl. It supports both local and remote use, meaning that users do not need a local installation to solve linear and integer problems thanks to Google Cloud. More recently, ORTools.jl started offering a native interface for constraint programming building upon the work in MathOptInterface.jl.
However, OR-Tools have more than this to offer, including a scalable routing solver for large-scale VRPs or state-of-the-art set-cover solver. MathOptInterface.jl does not yet propose an interface for these problems and we would like to gauge the community’s interest in these specific solvers.
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Storage on Android has evolved significantly over the years, with each new Android version introducing changes aimed at enhancing usability, security, and privacy. While these updates typically help with restricting app access to storage through various mechanisms, they may occasionally introduce new complexities and vulnerabilities. A prime example is the introduction of scoped storage in Android 10, which fundamentally changed how apps interact with files. While intended to enhance user privacy by limiting broad access to shared storage, scoped storage has also presented developers with new challenges and potential vulnerabilities to address. However, despite its significance for user privacy and app functionality, no systematic studies have been performed to study Android’s scoped storage at depth from a security perspective. In this paper, we present the first systematic security analysis of the scoped storage mechanism. To this end, we design and implement a testing tool, named ScopeVerif, that relies on differential analysis to uncover security issues and implementation inconsistencies in Android’s storage. Specifically, ScopeVerif takes a list of security properties and checks if there are any file operations that violate any security properties defined in the official Android documentation. Additionally, we conduct a comprehensive analysis across different Android versions as well as a cross-OEM analysis to identify discrepancies in different implementations and their security implications. Our study identifies both known and unknown issues of scoped storage. Our cross-version analysis highlights undocumented changes as well as partially fixed security loopholes across versions. Additionally, we discovered several vulnerabilities in scoped storage implementations by different OEMs. These vulnerabilities stem from deviations from the documented and correct behavior, which potentially poses security risks. The affected OEMs and Google have acknowledged our findings and offered us bug bounties in response.
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Large Language Models (LLMs) have demonstrated impressive capabilities across a range of natural language processing tasks. In particular, improvements in reasoning abilities and the expansion of context windows have opened new avenues for leveraging these powerful models.
NL2SQL is challenging in that the natural language question is inherently ambiguous, while the SQL generation requires a precise understanding of complex data schema and semantics. One approach to this semantic ambiguous problem is to provide more and sufficient contextual information.
In this work, we explore the performance and the latency trade-offs of the extended context window (a.k.a., long context) offered by Google's state-of-the-art LLM (\textit{gemini-1.5-pro}).
We study the impact of various contextual information, including column example values, question and SQL query pairs, user-provided hints, SQL documentation, and schema. To the best of our knowledge, this is the first work to study how the extended context window and extra contextual information can help NL2SQL generation with respect to both accuracy and latency cost.
We show that long context LLMs are robust and do not get lost in the extended contextual information. Additionally, our long-context NL2SQL pipeline based on Google's \textit{gemini-pro-1.5} achieve a strong performance with 67.41\% on BIRD benchmark (dev) without finetuning and expensive self-consistency based techniques.
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