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 11430 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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This framework manages AI agents by establishing behavioral boundaries and a persistent identity. It uses a multi-layered stack, combining safety rules with brand guidelines, to shape an agent's reasoning. Features include authority decay to limit power if confidence drops and memory segmentation to prevent data tampering. Centralized oversight ensures these digital representatives remain aligned with company policies through continuous monitoring and testing.
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This writeup defines the Hydration Proxy Pattern, a framework for building stateful conversational data systems over stateless LLM APIs. It describes a platform-agnostic approach to decoupling persistence from the AI provider through secure server-side intermediation and hybrid storage tiers. The abstract provides a blueprint for managing the "Persistence Gap" in enterprise AI integrations, detailing high-level strategies for session history management, streaming, and multi-stage semantic grounding without disclosing specific internal implementation details.
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High-volume enterprise service organizations face a persistent challenge in transitioning from reactive support models to proactive, preventative ones. This paper introduces the Agentic Trend-to-Knowledge (ATK) methodology, a novel, autonomous framework designed to address this gap. The ATK methodology employs an AI agent that operates in a recurring, closed loop. It first uses a two-stage process for the autonomous thematic analysis of recent support cases to identify the most significant recurring issue. It then leverages Retrieval-Augmented Generation (RAG) to source relevant institutional knowledge. A key innovation is the agent's adaptive, bimodal response: if relevant knowledge is found, it drafts a proactive communication for human review; if a knowledge gap is detected, it autonomously creates a content creation task for the appropriate team. This transforms the agent from an automation tool into a proactive process owner that creates a virtuous cycle of continuous improvement for both case deflection and knowledge base quality. By automating the entire workflow from insight to action, the ATK framework provides a concrete methodology for shifting from a "human-in-the-loop" to a more strategic "human-on-the-loop" operational paradigm.
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Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deploying a model, observing user interactions, and updating the model intermittently based on feedback. This mode of learning contrasts with common supervised learning frameworks, which focus on loss or regret minimization over a shared sequence of prediction tasks. Motivated by this deployment-driven learning cycle, we revisit the classical model of learning from equivalence queries, introduced by Angluin, which provides a simple abstraction of such interactions: a learner repeatedly proposes hypotheses and, whenever the deployed hypothesis is inadequate, receives a counterexample tailored to that hypothesis. Under fully adversarial counterexample generation, however, this model exhibits overly pessimistic worst-case behavior. Moreover, most existing work on learning from equivalence queries considers the \emph{full-information} setting, where the learner observes not only a counterexample but also its correct label. This is an assumption that does not always align with natural interactive settings. To address these considerations, we restrict the environment to generate counterexamples in a less adversarial manner by introducing a broad class of counterexample generators, which we call \emph{symmetric}. Informally, such symmetric counterexample generators select counterexamples based only on the symmetric difference between the hypothesis and the target, and encompass natural feedback mechanisms such as random counterexamples, as well as generators that select counterexamples minimizing a prescribed complexity measure over the instance space. Within this framework, we study learning from equivalence queries under both full-information and bandit feedback. We establish tight bounds on the number of learning rounds in both settings and outline directions for future research. Our techniques rely on a game-theoretic perspective on symmetric adversaries and combine adaptive weighting algorithms with minimax arguments.
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Generative AI is reshaping software development, yet its psychological impact remains under-researched. During May and August 2025 we conducted reflexive thematic analysis of interviews with 12 senior engineers (≥5 years experience) recruited from Western technology hubs to explore shifts in professional identity. We identify a central transition from "coder to conductor," where AI acts as a cognitive partner. Key findings include: (1) a re-architecting of focus from implementation to strategy; (2) a shift in productivity metrics from output to impact; and (3) a dual-impact on agency, where AI empowers autonomy but threatens competence through de-skilling anxieties. These findings suggest that as implementation becomes commoditised, organisational training and career progression must prioritise architectural mastery and metacognitive oversight to ensure sustained developer motivation and system integrity.
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This paper demonstrates that artificial intelligence can accelerate mathematical discovery by autonomously solving an open problem in theoretical physics. We present a neuro-symbolic system, combining the Gemini Deep Think large language model with a systematic Tree Search (TS) framework and automated numerical feedback, that successfully derived novel, exact analytical solutions for the power spectrum of gravitational radiation emitted by cosmic strings. Specifically, the agent evaluated the core integral for arbitrary loop geometries, directly improving upon recent AI-assisted attempts that only yielded partial asymptotic solutions. To substantiate our methodological claims regarding AI-accelerated discovery and to ensure transparency, we detail system prompts, search constraints, and intermittent feedback loops that guided the model. The agent identified a suite of 6 different analytical methods, the most elegant of which expands the kernel in Gegenbauer polynomials to naturally absorb the integrand's singularities. The methods lead to an asymptotic result for at large that both agrees with numerical results and also connects to the continuous Feynman parameterization of Quantum Field Theory. We detail both the algorithmic methodology that enabled this discovery and the resulting mathematical derivations.
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Voice activity detection (VAD) plays a vital role in enabling applications such as speech recognition. We analyze the impact of window size on the accuracy of three VAD algorithms: Silero, WebRTC, and Root Mean Square (RMS) across a set of diverse real-world digital audio streams. We additionally explore the use of hysteresis on top of each VAD output. Our results offer practical references for optimizing VAD systems. Silero significantly outperforms WebRTC and RMS, and hysteresis provides a benefit for WebRTC.
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We study the stochastic linear bandits with parameter noise model, in which the reward of action $a$ is $a^\top \theta$ where $\theta$ is sampled i.i.d.
We show a regret upper bound of $\tO \brk{\sqrt{d T \logKdelta \sigmax}}$ for a horizon $T$, general action set of size $K$ of dimension $d$, and where $\sigmax$ is the maximal variance of the reward for any action.
We further provide a lower bound of $\tOmega \brk{d \sqrt{T \sigmax}}$ which is tight (up to logarithmic factors) whenever $\log \brk{K} \approx d$.
For more specific action sets, $\ell_p$ unit balls with $p \leq 2$ and dual norm $q$, we show that the minimax regret is $\tTheta \brk{\sqrt{dT \vsigq}}$, where $\vsigq$ is a variance-dependent quantity that is always at most $4$.
This is in contrast to the minimax regret attainable for such sets in the classic additive noise model, where the regret is of order $d \sqrt{T}$.
Surprisingly, we show that this optimal (up to logarithmic factors) regret bound is attainable using a very simple explore-exploit algorithm.
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Distributed federated SQL query engines frequently
materialize query results into files in data lakes. Optimizing the
sizes of these files (e.g., balancing their sizes) is crucial for the
efficiency of not only the materialization queries themselves, but
also subsequent queries that read these files. Existing techniques
to manage file size include specifying output targets (e.g., number
of partitions), using cardinality estimation for materialized data
volume, performing full shuffles prior to materialization to obtain
accurate statistics, and/or post (background) compactions to
merge small files. All of these solutions have limitations in
practice; they typically do not provide any strong guarantees or
can be prohibitively expensive when they do (e.g., they require
background compactions to merge small files, which requires
additional I/O and CPU cost). Most of the existing techniques
can produce many small files during materialization and are
sensitive to data skew.
In this paper, we propose a novel method that streamlines
materialization within the same query execution and provides
guarantees on the file sizes using statistics at runtime. Our
approach does not require a full shuffle before the materialization
operator in order to get accurate statistics, making it attractive
and robust in practice. To show the effectiveness of our approach,
we present production metrics from F1 Query at Google that has
been running this functionality for the majority of its production
workload over many quarters. Our approach reduces the number
of files produced by a factor of 100 or more, and as a result,
it avoids creating tens of billions of files per day, saving storage
and computation cost.
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Autonomous research agents can now produce competitive solutions and complete manuscripts, yet their papers routinely contain fabricated citations, method descriptions disconnected from the code, and scores on incorrect scales---failures invisible to evaluations that assess fluency rather than evidentiary grounding. The core problem is verifiability: no existing system maintains a traceable chain from each claim in the paper to its grounding evidence, and current evaluation protocols assess output fluency rather than evidentiary grounding. We address this with Chaine-of-Evidence (CoE), a verifiability standard requiring every claim to trace to its grounding evidence, and instantiate it in Scientist One, an end-to-end research system that maintains evidence chains natively, and CoE Audit, an evaluation protocol with four integrity checks targeting the most damaging chain failures. Auditing 60 papers from four systems, we find every baseline exhibits at least one failure: phantom citations at 4--25%, method-code alignment in at most 2/15 papers. Scientist One achieves zero phantom citations (0/830), the highest alignment rate (7/15), and competitive solver scores.
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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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Towards conversational artificial intelligence for disease management
Khaled Saab
David Stutz
Yong Cheng
Kavita Kulkarni
Sara Mahdavi
Joelle Barral
James Manyika
Ryutaro Tanno
Adam Rodman
Nature (2026)
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Although large language models have shown promise in diagnostic dialogue, their capabilities for effective management reasoning, including disease progression, therapeutic response and safe medication prescription, have remained underexplored. We have advanced the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) using a new large-language-model-based agentic system optimized for multivisit clinical management and dialogue. To ground the reasoning of AMIE in authoritative clinical knowledge, we leveraged the long-context capabilities of Gemini, combining in-context retrieval with structured reasoning to align its output with up-to-date clinical practice guidelines and drug formularies. In a randomized, blinded virtual Objective Structured Clinical Examination study, AMIE was compared to 21 primary care physicians (PCPs) across 100 multivisit case scenarios designed to reflect the guidance of the UK National Institute for Health and Care Excellence and BMJ Best Practice guidelines. AMIE was non-inferior to PCPs in management reasoning, as assessed by specialists, and scored better both with respect to preciseness of treatment and investigation, and in terms of its alignment with and grounding in clinical guidelines. To benchmark medication reasoning, we developed RxQA, a multiple-choice question benchmark that was derived from two national drug formularies (from the USA and UK) and validated by board-certified pharmacists. Although AMIE and PCPs both benefited from the ability to access external drug information, AMIE outperformed PCPs on higher-difficulty questions. Although further research will be needed before real-world translation of AMIE, its strong performance across evaluations marks a significant step towards use of conversational artificial intelligence as a tool in disease management.
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The Synthetic Gap: Automating Forensic Investigation of "AI Slop" with the Scaled Abuse Forensics Examiner (SAFE)
Vahid Jalali
Longling Wang
Geethik Narayana Kamineni
Utkarsh Chaudhary
Crystal Zhao
Lucas Liu
2026
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Generative AI capabilities have enabled malicious actors to flood online platforms with "AI slop"—mass-produced, low-quality synthetic media designed to overwhelm traditional integrity systems. These adversarial campaigns often utilize coordinated networks to distribute unique, localized variations of synthetic content, rendering static detection methods ineffective. The signals to detect coordination often have recall gaps. The content is not exactly duplicative to be in the same repetitive video cluster. The abusers however show similar patterns of behavior which need forensics. Manual forensic investigations cannot scale to match the velocity of these generative attacks.
To address this, we present SAFE (Scaled Abuse Forensics Examiner), an automated multi-agent architecture designed for the scalable forensics of adversarial synthetic media. The system decomposes the investigation process into specialized agents: a Cluster Understanding Agent specialized in analyzing the relations between channels in a cluster, a Behavior Understanding Agent that identifies inorganic spatiotemporal patterns, and a Content Understanding Agent that utilizes LoRA-adapted Large Language Models (LLMs) and few-shot learning to detect existing policy violations and spirit of the policy violations respectively . A Root Agent synthesizes these multimodal signals to render a final verdict. Early deployment results indicate that SAFE significantly accelerates the identification of novel synthetic threats, reducing forensic investigation time compared to human-in-the-loop workflows.
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