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.
Sort By
1 - 15 of 11415 publications
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.
View details
Preview abstract
Large language models have achieved remarkable capabilities across domains, yet mechanisms underlying sophisticated reasoning continue to be explored1,2. Recent reasoning-reinforced models, including OpenAI’s o-series and DeepSeek-r1, outperform other merely instruction-tuned models on complex cognitive tasks3,4, attributed to extended test-time computation through longer chains of thought5. Here we show that enhanced reasoning emerges not from extended computation alone, but from the systematic simulation of complex, multi-agent interactions—a society of thought—which enables the deliberate diversification and debate among internal cognitive perspectives characterized by distinct personality traits and domain expertise. Through quantitative analysis using classified outputs and mechanistic interpretability methods applied to reasoning traces6–8, we find that reasoning models like DeepSeek-r1 exhibit much greater perspective diversity than baseline models, activating broader and more conflict between heterogeneous personality- and expertise-related features during reasoning. This multi-agent structure manifests in conversational behaviors including question-answering sequences, perspective shifts, and reconciliation of conflicting views, as well as in socio-emotional roles that characterize back-and-forth conversation, which together account for over 60% of the accuracy advantage in reasoning tasks through both direct and indirect facilitation of cognitive strategies9,10. Controlled reinforcement learning experiments further reveal that priming models with conversational scaffolding—even when dialogues lead to incorrect solutions—substantially accelerates reasoning improvement compared to answer-only training. These findings indicate that the social organization of thought, rather than correctness alone, enables effective exploration of solution spaces. We suggest that reasoning models establish a computational parallel to collective intelligence in human groups11–13, where diversity enables superior problem-solving when systematically structured and suggest new opportunities for agent organization to harness the wisdom of crowds.
View details
Observing long-lived longwave contrail forcing
Aarón Sonabend
Joe Ng
Atmospheric Measurement Techniques (2026)
Preview abstract
Contrail microphysical simulations and climate simulations have indicated that contrail cirrus cause a substantial fraction of aviation’s climate impact. While the approximations and parameter selections in these simulations have been well-validated over the past two decades, the heat trapping of contrails has not been observed using satellite data beyond a few hours. This is because contrails lose their linear shape after a few hours, making them difficult to distinguish from natural cirrus clouds. Here we provide satellite-driven analysis of long-lived heat trapping by contrails over North and South America. We aggregate a dataset of GOES-16 estimated outgoing longwave radiation and advected trace density of flight paths, and apply causal inference to discern the effect of contrails while controlling for radiative and cloud confounders. As a means of validation, we also generate synthetic datasets with known ground truth, and confirm that applying the causal inference method is able to recover the synthetic ground truth. Since this method yields an estimate which has some differences from both “instantaneous radiative forcing” (iRF) and “effective radiative forcing” (ERF) estimates which have been reported in the literature so far, we introduce the new term “observational radiative forcing, 12 hours” (oRF12). Our analysis estimates the longwave oRF12 from contrails over the Americas averaged 47.9 gigajoules per flight kilometer (95% CI: 31 to 52 GJ/km) during April 2019 to April 2020.
View details
Preview abstract
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.
View details
Preview abstract
This paper introduces Operationalized Temporal Entity Resolution, a distributed system architecture designed to resolve data consistency challenges in modern Security Information and Event Management (SIEM) environments. processing petabytes of high-velocity telemetry. We address the critical failure mode of ”State Smearing”—a temporal discrepancy between an entity’s state at event time versus analysis time—which frequently corrupts forensic timelines, particularly regarding ephemeral assets like containers and DHCP leases. Our approach coalesces heterogeneous data from diverse log sources into a single, canonical representation, processing over 2 billion entity fragments daily. By leveraging a deterministic Dynamic Graph Resolution via modified distributed connected components and a novel Density-Aware Temporal Checkpointing algorithm, we generate precise validity intervals. This method embeds temporal state directly into the resolution graph, eliminating the need for computationally expensive query-time joins. Ultimately, this architecture enables security analysts to perform ”time-travel” queries to reconstruct historical states accurately. Analysis of a production environment demonstrates that 8–16% of threat detection rules critically depend on this enriched temporal merging.
View details
Digital Shadow AI Risk Theoretical Framework (DART): A Framework for Managing Data Disclosure and Privacy Risks of AI tools at Work
Master's Thesis (2026)
Preview abstract
The accelerated integration of generative AI technologies and agentic AI tools, particularly those like ChatGPT, into workplace settings has introduced complex challenges concerning data governance, regulatory compliance, and organizational privacy (GDPR 2016; CCPA/CPRA). This study introduces the Digital Shadow AI Risk Theoretical Framework (DART)—a novel theoretical framework designed to systematically identify, classify, and address the latent risks arising from the widespread, and often unregulated, use of AI systems in professional environments (NIST, 2023; OECD AI Policy Observatory, 2023). DART introduces six original, interrelated constructs developed in this study: Unintentional Disclosure Risk, Trust-Dependence Paradox, Data Sovereignty Conflict, Knowledge Dilution Phenomenon, Ethical Black Box Problem, and Organizational Feedback Loops. Each construct reflects a unique dimension of risk that emerges as organizations increasingly rely on AI-driven tools for knowledge work and decision-making.
The framework is empirically tested through a mixed-methods research design involving hypothesis testing and statistical analysis of behavioral data gathered from cross-sectional surveys of industry professionals. Two cross-industry surveys (Survey-1: 416 responses, 374 analyzed; Survey-2: 203 responses, 179 analyzed) and CB-SEM tests supported seven of eight hypotheses; H4 (sovereignty) was not significant; H7 (knowledge dilution) was confirmed in replication. The findings highlight critical gaps in employee training, policy awareness, and risk mitigation strategies—underscoring the urgent need for updated governance frameworks, comprehensive AI-use policies, and targeted educational interventions. This paper contributes to emerging scholarship by offering a robust model for understanding and mitigating digital risks in AI-enabled workplaces, providing practical implications for compliance officers, risk managers, and organizational leaders aiming to harness the benefits of generative AI responsibly and securely. The novelty of DART lies in its explicit theorization of workplace-level behavioral risks—especially Shadow AI, which unlike Shadow IT externalizes organizational knowledge into adaptive systems—thereby offering a unified framework that bridges fragmented literatures and grounds them in empirical evidence.
View details
Phoenix: Rowhammer Attacks on DDR5 with Self-Correcting Synchronization
Michele Marazzi
Kaveh Razavi
Salman Qazi
Diego Meyer
Patrick Jattke
IEEE Security & Privacy (S&P) (2026)
Agentic AI Infrastructure in Practice: Learn These Key Hurdles to Deploy Production AI Agents Efficiently
https://swisscognitive.ch/ (2026)
Preview abstract
The emergence of Agentic AI—autonomous systems capable of reasoning, decision-making, and multi-step execution—represents a paradigm shift in enterprise technology. Moving beyond simple generative tasks, these agents offer the potential to solve long-standing industry pain points, with over 90% of enterprises planning integration within the next three years. However, the transition from successful proof-of-concept (PoC) to a resilient, production-grade system presents significant hurdles.
This article categorizes these challenges into three primary domains:
Technical and Engineering Hurdles: Issues such as "entangled workflows" that complicate debugging, the struggle to maintain output quality and mitigate hallucinations, and the unpredictability caused by shifting underlying models or data sources.
People, Process, and Ecosystem Hurdles: The high operational costs and unclear ROI of large models, the necessity of a new "Agent Ops" skillset, the complexity of integrating agents with disparate enterprise systems, and a rapidly evolving regulatory landscape.
The Pace of Change and Security risks: The technical debt incurred by shifting software frameworks and the expanded attack surface created by autonomous agents.
The article concludes that successful deployment requires a shift from informal "vibe-testing" to rigorous engineering discipline. By adopting code-first frameworks, establishing robust evaluation metrics (KPIs), and prioritizing functional deployment over theoretical optimization, organizations can effectively manage the lifecycle of Agentic AI and realize its transformative business value.
View details
Preview abstract
In today's AI landscape, success depends not just on prompting large language models but on orchestrating them into intelligent systems that are scalable, compliant, and cost-effective. GenAI on Google Cloud is your hands-on guide to bridging that gap. Whether you're an ML engineer or an enterprise leader, this book offers a practical game plan for taking agentic systems from prototype to production.
Written by practitioners with deep experience in AgentOps, data engineering, and GenAI infrastructure, this guide takes you through real-world workflows from data prep and deployment to orchestration and integration. With concrete examples, field-tested frameworks, and honest insights, you'll learn how to build agentic systems that deliver measurable business value.
> Bridge the production gap that stalls 90% of vertical AI initiatives using systematic deployment frameworks
> Navigate AgentOps complexities through practical guidance on orchestration, evaluation, and responsible AI practices
> Build robust multimodal systems for text, images, and video using proven agent architectures
> Optimize for scale with strategies for cost management, performance tuning, and production monitoring
View details
Preview abstract
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.
View details
Preview abstract
We introduce AMS (Activation-based Model Scanner), a tool that detects modifications to safety training in language models by measuring the geometric structure of safety-relevant concepts in activation space. Safety training creates measurable separation between harmful and benign content classes; certain safety modifications collapse or rotate this structure, while others leave it intact. We validate AMS across 14 model configurations spanning 4 architecture families (Llama, Gemma, Qwen, Mistral) and four safety-modification categories (instruction-tuned, base, abliterated, uncensored fine-tunes). Leave-one-out cross-validation of thresholds achieves 71% accuracy (10/14); bootstrap 95% confidence intervals on σ point estimates have median width 3.4σ and a substantial fraction of cells cross the PASS threshold under resampling. We further measure behavioral compliance on 20 stratified JailbreakBench prompts per model and find that σ on the harmful-content concept predicts compliance with Pearson r=−0.546 ( p=0.043 ); the rank-order Spearman correlation is weaker ( ρ=−0.423 , p=0.13 ). The structural signal predicts behavior directionally but with meaningful noise. Mechanistic analysis identifies a four-class taxonomy of safety-training modifications distinguished by activation-space signature: 1) training removal collapses cluster separation (e.g., base models, Dolphin variants: 0.5– 1.4σ ); 2) weight-orthogonalization-style abliteration both collapses separation and rotates the refusal direction (Llama-3.1-abliterated: σ=3.33 , direction cos sim 0.30); 3) rotation-without-collapse abliteration preserves cluster separation while rotating the refusal direction (Gemma-2-9b-abliterated: σ=4.54 , direction cos sim 0.84); and 4) behavioral fine-tuning that preserves both magnitude and direction (DarkIdol-1.2-Uncensored: σ=5.45 , direction preserved, 97% behavioral compliance). 1) and 2) AMS’s Tier 1 σ -threshold detects classes; 3) Tier 2 direction-similarity verification detects class; and 4) Class is undetectable by activation-only probing and represents a documented failure mode of the approach. We discuss threshold calibration, limitations of single-run measurement, and the open problem of detecting behavioral-only safety modifications.
View details
Preview abstract
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.
View details
Approximate vs Precise: An experiment in what impacts user choice when apps request location access
Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26), April 13–17, 2026, Barcelona, Spain (2026)
Preview abstract
User location data is highly sensitive, yet commonly requested by mobile apps for both core functionality and monetization. To improve user privacy, the major mobile platforms, Android and iOS, made changes so that when apps request precise location access, users can choose to share only their approximate location. However, the platforms have diverging interfaces: Android offers a side-by-side choice and iOS offers a corner toggle. This study evaluates which factors impact users’ choices when apps request location access via a randomized controlled experiment with 2579 US Android users. We tested the impact of app type, whether a reason for the request was provided, and the quality and content of the reason, including monetization. We do not find the reasons have an effect. Instead, we find users’ choices are impacted by app type and user demographics. We find that when users are given a side-by-side choice to allow approximate versus precise location access, they make reasonable choices. Of users who allowed access, the vast majority (90.7%) chose precise for a rideshare app versus the majority (71.3%) chose approximate for a local news app. Concerningly, the majority also allowed location access to a wallpaper app, and older users were significantly more likely to allow apps precise location access. We conclude by discussing implications for app platforms and future work.
View details
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
Preview abstract
Server hardware and software co-design for a secure, efficient cloud.
View details
Preview abstract
**Agentic Engineering** is the rigorous discipline of treating Large Language Models as semi-autonomous systems that execute complex, multi-step workflows (trajectories) based on verifiable specifications, rather than using them as simple autocomplete engines.
Here is a brief summary of its core principles:
* **Main Goals:** It aims to maximize the agent's autonomous run-time, multiply a single engineer's impact by running parallel tasks, and offload tedious boilerplate coding.
* **The "Harness":** A raw model is virtually useless without heavy investment in a harness—comprising tools, system prompts, and strict guardrails—to reliably guide the model and enforce coding policies.
* **Loss of Micro-Control:** Engineers must surrender idiosyncratic stylistic preferences; if the agent's code passes automated linters and tests, it is accepted.
* **Meta-Debugging:** When failures occur, engineers no longer fix code syntax. Instead, they debug the workflow itself—adjusting the agent's tools, search queries, or prompt constraints to ensure repeatable success.
View details
