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 11281 publications
Preview abstract
Online video platforms face an exponential challenge in detecting and mitigating the flood of AI-generated "slop" and synthetic spam perpetuated by coordinated malicious actors. This content is increasingly designed to exploit the limitations of traditional media forensics, often utilizing generative AI to produce unique, localized variations of harmful or low-quality material at scale. Traditional content-centric moderation fails against this coordinated, adversarial generation strategy.
This paper presents a novel, scalable defense system deployed at a major Online Video Platform (OVP) to identify and terminate clusters of coordinated accounts exhibiting a prevalence of adversarial synthetic content. The approach leverages a multi-faceted architecture incorporating two core machine learning components: a robust Coordinated Bot-Net Detector (via Account Relatedness) and a Synthetic Pattern Classifier (formerly BT Classifier). Crucially, we introduce an advanced AI enhancement layer utilizing Large Language Models (LLMs), specialized via Low-Rank Adaptation (LoRA) and Automatic Prompt Optimization (APO), to achieve rapid, high-precision semantic understanding of emerging synthetic spam trends.
Operational data spanning a six-month period demonstrates the system's significant impact, resulting in the successful termination of 50K clusters comprising 130K channels of synthetic spam generators. Furthermore, the LLM-driven automation significantly improves operational efficiency, saving approximately 83 human review hours to cut down human reviews by 50%. This work details a critical, deployed solution that provides essential scalability and adversarial resilience against sophisticated generative attacks.
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We consider a setting where we have a ground set ℳ together with real-valued set functions f₁, … , f_n, and the goal is to partition ℳ into two sets S₁,S₂ such that |f_i(S₁) - f_i(S₂)| is small for every i. Many results in discrepancy theory can be stated in this form with the functions f_i being additive. In this work, we initiate the study of the unstructured case where f_i is not assumed to be additive. We show that even without the additivity assumption, the upper bound remains at most O(√{n log n}).
Our result has implications on the fair allocation of indivisible goods. In particular, we show that a consensus halving up to O(√{n log n}) goods always exists for n agents with monotone utilities. Previously, only an O(n) bound was known for this setting.
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Performance analysis of updated Sleep Tracking algorithms across Google and Fitbit wearable devices
Arno Charton
Linda Lei
Siddhant Swaroop
Marius Guerard
Michael Dixon
Logan Niehaus
Shao-Po Ma
Logan Schneider
Ross Wilkinson
Ryan Gillard
Conor Heneghan
Pramod Rudrapatna
Mark Malhotra
Shwetak Patel
Google, Google, 1600 Amphitheatre Parkway
Mountain View, CA 94043 (2026) (to appear)
Preview abstract
Background: The general public has increasingly adopted consumer wearables for sleep tracking over the past 15 years, but reports on performance versus gold standards such as polysomnogram (PSG), high quality sleep diaries and at-home portable EEG systems still show potential for improved performance. Two aspects in particular are worthy of consideration: (a) improved recognition of sleep sessions (times when a person is in bed and has attempted to sleep), and (b) improved accuracy on recognizing sleep stages relative to an accepted standard such as PSG.
Aims: This study aimed to: 1) provide an update on the methodology and performance of a system for correctly recognizing valid sleep sessions, and 2) detail an updated description of how sleep stages are calculated using accelerometer and inter-beat intervals
Methods: Novel machine learning algorithms were developed to recognize sleep sessions and sleep stages using accelerometer sensors and inter-beat intervals derived from the watch or tracker photoplethysmogram. Algorithms were developed on over 3000 nights of human-scored free-living sleep sessions from a representative population of 122 subjects, and then tested on an independent validation set of 47 users. Within sleep sessions, an algorithm was developed to recognize periods when the user was attempting to sleep (Time-Attempting-To-Sleep = TATS). For sleep stage estimation, an algorithm was trained on human expert-scored polysomnograms, and then tested on 50 withheld subject nights for its ability to recognize Wake, Light (N1/N2), Deep (N3) and REM sleep relative to expert scored labels.
Results: For sleep session estimation, the algorithm had at least 95% overlap on TATS with human consensus scoring for 94% of nights from healthy sleepers. For sleep stage estimation, comparing with the current Fitbit algorithm, Cohen’s kappa for four-class determination of sleep stage increased from an average of 0.56 (std 0.13) to 0.63 (std 0.12), and average accuracy increased from 71% (std 0.10) to 77% (std 0.078)
Conclusion: A set of new algorithms has been developed and tested on Fitbit and Pixel Watches and is capable of providing robust and accurate measurement of sleep in free-living environments.
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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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"It didn’t feel right but I needed a job so desperately": Understanding People's Emotions & Help Needs During Financial Scams
G. Jake Chanenson
Tara Matthews
Jessica McClearn
Sarah Meiklejohn
Mia Hassoun
2026
Preview abstract
Online financial scams represent a long-standing and serious threat for which people seek help. We present a study to understand people’s in situ motivations for engaging with scams and the help needs they express before, during, and after encountering a scam. We identify the main emotions scammers exploited (e.g., fear, hope) and characterize how they did so. We examine factors—such as financial insecurity and legal precarity—which elevate people’s risk of engaging with specific scams and experiencing harm. We indicate when people sought help and describe their help-seeking needs and emotions at different stages of the scam. We discuss how these needs could be met through the design of contextually-specific prevention, diagnostic, mitigation, and recovery interventions.
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Exponential quantum advantage in processing massive classical data
Haimeng Zhao
Alexander Zlokapa
John Preskill
Hsin-Yuan (Robert) Huang
arXiv:2604.07639 (2026)
Preview abstract
Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In this work, we prove that a small quantum computer of polylogarithmic size can perform large-scale classification and dimension reduction on massive classical data by processing samples on the fly, whereas any classical machine achieving the same prediction performance requires exponentially larger size. Furthermore, classical machines that are exponentially larger yet below the required size need superpolynomially more samples and time. We validate these quantum advantages in real-world applications, including single-cell RNA sequencing and movie review sentiment analysis, demonstrating four to six orders of magnitude reduction in size with fewer than 60 logical qubits. These quantum advantages are enabled by quantum oracle sketching, an algorithm for accessing the classical world in quantum superposition using only random classical data samples. Combined with classical shadows, our algorithm circumvents the data loading and readout bottleneck to construct succinct classical models from massive classical data, a task provably impossible for any classical machine that is not exponentially larger than the quantum machine. These quantum advantages persist even when classical machines are granted unlimited time or if BPP=BQP, and rely only on the correctness of quantum mechanics. Together, our results establish machine learning on classical data as a broad and natural domain of quantum advantage and a fundamental test of quantum mechanics at the complexity frontier.
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Marginalized Bundle Adjustment: Multi-View Camera Pose from Monocular Depth Estimates
Shengjie Zhu
Xiaoming Liu
Vincent Chu
International Conference on 3D Vision (2026)
Preview abstract
Structure-from-Motion (SfM) is a classical 3D vision task for recovering camera parameters and scene geometry from multi-view images. Recent advances in deep learning enable accurate monocular depth estimation (MDE) that infers structure from a single image without depending on camera motion. But integrating MDE into SfM remains challenging. Unlike classical triangulated sparse pointclouds, MDE produces dense depthmaps with significantly higher error variance. Inspired by modern RANSAC estimators, we propose a Marginalized Bundle Adjustment (MBA) to accommodate MDE error variance with its density. With MBA, we show that MDE depthmaps are sufficiently accurate to support SoTA or competitive results in Structure-from-Motion and camera relocalization. Our benchmark demonstrates consistent remarkable results from two-view, few-frames small multiview, to thousands-frames large multiview system. Our method highlights the significant potential of MDE on multi-view 3D vision tasks.
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Audio Description ( AD) provides essential access to visual media for blind and low vision ( BLV) audiences. Yet current AD production tools remain largely inaccessible to BLV video creators, who possess valuable expertise but face barriers due to visually- driven interfaces. We present ADCanvas, a multimodal authoring system that supports non- visual control
over audio description ( AD) creation. ADCanvas combines conversational interaction with keyboard- based playback control and a plain- text, screen reader–
accessible editor to support end- to- end AD authoring and visual question answering ( VQA). Combining screen- reader- friendly controls with a multimodal
LLM agent, ADCanvas supports live VQA, script generation, and AD modification. Through a user study with 12 BLV video creators, we find that users adopt
the conversational agent as an informational aide and drafting assistant, while maintaining agency through verification and editing. For example, participants
saw themselves as curators who received information from the model and filtered it down for their audience. Our findings offer design implications for
accessible media tools, including precise editing controls, accessibility support for creative ideation, and configurable rules for human- AI collaboration.
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Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning
Preview abstract
Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices interpretability while introducing significant resource and operational overhead. To address these limitations, we introduce Prompt-Level Distillation (PLD). We extract explicit reasoning patterns from a Teacher model and organize them into a structured list of expressive instructions for the Student model's System Prompt. Evaluated on the StereoSet and Contract-NLI datasets using Gemma-3 4B, PLD improved Macro F1 scores from 57\% to 90.0\% and 67\% to 83\% respectively, enabling this compact model to match frontier performance with negligible latency overhead. These expressive instructions render the decision-making process transparent, allowing for full human verification of logic, making this approach ideal for regulated industries such as law, finance, and content moderation, as well as high-volume use cases and edge devices.
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Preview abstract
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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Preview abstract
We study the problem of allocating access point bandwidth to users of a wireless network in the presence of adversarial jamming. Specifically, we consider a setting in which the network designer acts first and allocates access point bandwidth to the users of the network, before an adversary applies a jamming strategy to reduce the bandwidth of a subset (or all) of the access points. We consider a strong adversary who has complete information and can optimize the jamming strategy, subject to power budget constraints. In turn, the network designer must allocate the resources in anticipation of the adversary's actions.
We explain that our model gives rise to a special network interdiction model, which differs from the standard setting in two ways: The first is that the interdictor is given the benefit of responding, rather than leading the game. The second is that the interdiction is fractional and performed at the node level of the network. The interdiction then propagates to all edges incident to the access point.
In terms of technical results, we provide an allocation algorithm that is based on linear programming duality and show that the algorithm can solve the problem optimally, assuming knowledge of the adversary's budget constraints. We conduct experiments on synthetic data to show the extent to which the algorithm improves the total utilized bandwidth over the algorithm that optimizes bandwidth allocation while being oblivious to the adversary's existence.
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Uncovering Relationships between Android Developers, User Privacy, and Developer Willingness to Reduce Fingerprinting Risks
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26) (2026)
Preview abstract
The major mobile platforms, Android and iOS, have introduced changes that restrict user tracking to improve user privacy, yet apps continue to covertly track users via device fingerprinting. We study the opportunity to improve this dynamic with a case study on mobile fingerprinting that evaluates developers’ perceptions of how well platforms protect user privacy and how developers perceive platform privacy interventions. Specifically, we study developers’ willingness to make changes to protect users from fingerprinting and how developers consider trade-offs between user privacy and developer effort. We do this via a survey of 246 Android developers, presented with a hypothetical Android change that protects users from fingerprinting at the cost of additional developer effort.
We find developers overwhelmingly (89%) support this change, even when they anticipate significant effort, yet prefer the change be optional versus required. Surprisingly, developers who use fingerprinting are six times more likely to support the change, despite being most impacted by it. We also find developers are most concerned about compliance and enforcement. In addition, our results show that while most rank iOS above Android for protecting user privacy, this distinction significantly reduces among developers very familiar with fingerprinting. Thus there is an important opportunity for platforms and developers to collaboratively build privacy protections, and we present actionable ways platforms can facilitate this.
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AI-PROPELLER: Warehouse-Scale Inter-Procedural Code Layout Optimization with AlphaEvolve
Chaitanya Mamatha Ananda
Rajiv Gupta
Aiden Grossman
2026
Preview abstract
Post-link optimizers (PLOs) such as Propeller and BOLT have demonstrated that precise, profile-guided code layout can extract significant performance gains from heavily optimized binaries. However, these systems are currently restricted to intra-procedural techniques, leaving the global potential of inter-procedural layout largely untapped. Inter-procedural code layout is historically difficult due to a combinatorially intractable search space and complex call-return semantics that are challenging to model. Consequently, the performance potential of fine-grained inter-procedural layout remains unproven in practice.Ours uses AlphaEvolve, an agentic workflow to evolve the compiler heuristic in Propeller into a fine-grained inter-procedural optimizer. While AlphaEvolve synthesizes novel code layout policies, Vizier fine-tunes the resulting policy hyperparameters.
To ensure high-fidelity, we move away from approximate static cost models and the agentic workflow generates multiple layout variants that are executed on actual hardware to measure real performance counters, providing a precise reward signal for the evolutionary loop. Ours has been evaluated on several benchmarks including large warehouse-scale applications and experiments show performance improvements of 0.23% to 1.6% on these benchmarks optimized with state-of-the-art FDO and PLO.
This is the first time ever that real-world applications have been optimized with fine-grained inter-procedural code layout.
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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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