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 11553 publications
A 3D Scene Graphs Survey: Open Challenges and Future Directions
Dennis Rotondi
Francesco Argenziano
Sebastian Koch
Nathan Hughes
Martin Büchner
Johanna Wald
Lukas Schmid
Daniele Nardi
Abhinav Valada
Liam Paul
Luca Carlone
Kai Arras
Annual Review of Control, Robotics, and Autonomous Systems (ARCRAS), 10 (2027) (to appear)
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3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Their expressiveness has made them relevant to a broad range of problems in robotics and computer vision, including mapping, task and motion planning, scene understanding, and many others. However, the field remains fragmented: different communities adopt distinct formulations, construction pipelines, and evaluation protocols, making it difficult to compare methods, identify common assumptions, and assess remaining challenges for robust real- world deployment. This survey provides a unified and critical review of 3DSGs, with particular emphasis on open challenges and future directions. We first formalize 3DSGs under a common definition and analyze the principal modeling choices that characterize existing formulations, including node and edge attributes, hierarchical structure, dynamic scene representations, and affordance-aware extensions. We then review how 3DSGs are constructed from raw sensory observations, covering both learning-oriented and construction-oriented systems. Finally, we examine downstream applications and evaluation strategies, from intrinsic graph quality to task-level performance. To support the community, we also provide a dedicated website that organizes and extends the surveyed works.
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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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Outrunning LLM Cutoffs: A Live Kernel Crash Resolution Benchmark for All,
Chenxi Huang
Alex Mathai
Feiyang Yu
Aleksandr Nogikh
Eugene Wu
Kostis Kaffes
Junfeng Yang
Baishakhi Ray
Proceedings of the 43rd International Conference on Machine Learning (ICML) (2026)
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Repairing system crashes discovered by kernel fuzzers like Syzkaller is a critical yet underexplored challenge in software engineering. While recent works have introduced Large Language Model (LLM) based agents for Linux kernel crash-resolution, their evaluation benchmarks are usually static and thus, do not capture the evolving nature of the Linux kernel, and suffer from potential data contamination due to LLM knowledge cutoffs. To address the above problem, we present (i) Live-kBench, an evaluation framework for self-evolving benchmarks that continuously scrapes and evaluates agents on freshly discovered kernel bugs, and (ii) kEnv, an agent-agnostic standardized crash-resolution environment for kernel compilation, execution, and feedback. This design decouples agent workflows from heavy-weight execution, enabling fair and scalable comparison across diverse agent frameworks under identical conditions. To this end, we curate an inaugural dataset of 534 Linux kernel bugs and empirically demonstrate a significant performance gap, with agents achieving up to 25% higher equivalent patch rate on bugs fixed before the LLM knowledge cutoff. Using kEnv, we benchmark three state-of-the-art agents, showing that they resolve 74% of crashes on the first attempt (plausible patches); however only ~20% of generated patches closely match developer fixes. Additionally, exposing crash resolution feedback improves crash resolution rate by 29%. Live-kBench provides the community with an evaluation infrastructure for self-evolving benchmarks that is both time and attribute sensitive; complete with a public dashboard to track agent progress on Linux kernel bugs.
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In a prior column, we wrote about how measuring productivity can be viewed as a form of modeling and that all models are wrong, but some are useful. That discussion centered on the idea of ensuring that a productivity model was inclusive of multiple metrics and that those metrics covered the various facets of productivity and covered each facet reasonably well. In that article, we set aside the question of what makes a good individual productivity metric that can be combined with others into a (hopefully) useful model of productivity. In this article, we’ll share some things we consider when building an individual metric, including an example of a novel metric we built in the aftermath of the COVID pandemic.
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Physical Design Aware Verification Methodology for Closing Coverage Gaps in SharedBus MBIST
Shivam Tulsyan
Vasudevan Pillai A
Maheedhar Jalasutram
Prachi Sinha
Mayank Parasrampuria
2026
A Theoretical Framework for Modular Learning of Robust Generative Models
Proceedings of the 43rd International Conference on Machine Learning (ICML 2026)
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Training large-scale generative models is resource-intensive and relies heavily on heuristic dataset weighting. We address two fundamental questions: Can we train Large Language Models (LLMs) modularly, combining small, domain-specific experts to match monolithic performance, and can we do so robustly for any data mixture, eliminating heuristic tuning? We present a theoretical framework for modular generative modeling where a set of pre-trained experts are combined via a gating mechanism. We define the space of normalized gating functions $\mathcal{G}_{1}$ and formulate the problem as a minimax game to find a single robust gate that minimizes divergence to the worst-case data mixture. We prove the existence of such a robust gate using Kakutani's fixed-point theorem and show that modularity acts as a strong regularizer, with generalization bounds scaling with the lightweight gate's complexity. Furthermore, we prove that this modular approach can theoretically outperform models retrained on aggregate data, with the gap characterized by the Jensen-Shannon Divergence. Finally, we introduce a scalable Stochastic Primal-Dual algorithm and a Structural Distillation method for efficient inference. Empirical results on synthetic and real-world datasets confirm that our modular architecture effectively mitigates gradient conflict and can robustly outperform monolithic baselines.
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While the Latin script is used informally by speakers of many languages with more complex native scripts, high quality Latin script corpora for such languages that reflect actual natural romanizations are scarce and often difficult to collect. In this work, we propose a method for mining romanized language corpora in languages for which we do not have any pre-existing samples of naturally romanized text, focusing on Tigrinya as a test case. First we examine the efficacy of learning romanizations for a language based on observed romanizations in other languages that use the same native script. We then extrinsically assess such methods by using a romanization model trained on Amharic data to bootstrap coverage of romanized Tigrinya in a language identification system. Manual evaluation by two L1 and one L2 Tigrinya speakers suggests our method extracts romanized Tigrinya text with acceptably high precision. We release code to run our mining pipeline on public web corpora, such as MADLAD-400.
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Superconducting qubits are a leading platform for realizing fault-tolerant quantum computers. Current generations demonstrate fast, high fidelity quantum gates and readout on the order of hundreds of nanoseconds, while maintaining coherence times exceeding one hundred microseconds. Achieving this state-of-the-art performance requires a tight co-design, balancing fundamental physics, microwave engineering, and semiconductor fabrication. Readout designs, in particular, benefit from this multidisciplinary approach.
In this talk, we discuss the current challenges for readout in superconducting quantum processors from the perspective of Google Quantum AI. We examine the intersection of device physics and microwave engineering constraints, illustrating how optimizing both is essential for scaling next-generation quantum systems.
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The Manager's Job Has Already Changed, Most Companies Haven't Noticed
AI Journal (2026)
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The traditional role of a manager, focused on overseeing people performing tasks with software tools, is undergoing a profound shift due to the rise of autonomous AI systems. Across functions like customer support, sales, and finance, software is no longer just a tool but is increasingly executing work end-to-end. This article examines the implications of this transition for the managerial role, arguing that the manager's job is evolving from an "operator-of-operators" to a "designer-of-boundaries." Key new responsibilities include defining the scope of autonomous systems, codifying implicit human judgment into explicit rules and policies, and designing effective human-in-the-loop escalation paths. This shift also impacts team structures, performance management conversations, and even professional identity, as experienced operators' roles are renegotiated. The article contends that companies must recognize and address these cultural and organizational changes to successfully leverage autonomous systems, and that board-level oversight needs to adapt to this new operational reality. The cultural work of redefining management and supporting the workforce through this transition is paramount and will ultimately determine the long-term value derived from these technological advancements.
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In "Elephants, Goldfish and the New Golden Age of Software Engineering," the author discusses how AI is changing knowledge work, especially software development. Written from the perspective of April 2026, the article points out that while AI speeds up coding, it can also quickly generate a lot of mistakes and messy code if it isn't carefully managed by human oversight and clear processes.
The paper outlines a practical approach to working with AI, broken down into three main sections:
Using AI as a Tool, Not a Toy:
The author notes that people often get poor results by asking AI to do everything in a single prompt. Instead, users should have back-and-forth conversations with AI to question assumptions, set clear grading rules, and guide the research. The main point is that humans must still provide the final judgment; AI is simply a way to speed up and record that thinking.
The Elephant-Goldfish Model:
As AI creates more code than humans can easily read, written design documents become more important than the code itself. To keep AI on track, the author suggests a two-part method:
* The Elephant: A long chat session where the human and AI discuss ideas and write a detailed design document *before* any code is written. This session holds all of the project's background information and decisions.
* The Goldfish: A brand-new AI chat session with no memory. The human asks this "goldfish" to read the design document. If the goldfish cannot understand the plan based only on that document, the document needs more details.
* Only after the design document is clear enough for the goldfish to understand does the human ask the AI to write the code based on those strict instructions.
* Managing AI and the Future of Work:
The author expects that regular employees will soon act like managers, overseeing multiple AI helpers. Because of this, workers need to learn basic management skills, like how to delegate tasks and set clear boundaries. Also, since AI will handle routine chores, humans will need to practice focusing for longer periods to do deeper, harder thinking. Ultimately, a worker's value will come from their planning and decision-making skills, rather than their ability to type code.
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QProf: Fleetwide Transitive Cost Profiling for Warehouse-Scale Computers
Sam Xi
Ali Sheikh
Vance Lankhaar
Symposium on Operating Systems Principles (2026)
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Warehouse-scale services form the backbone of major cloud services relied on by billions of users every day. As these systems grow increasingly complex, understanding them and finding opportunities for optimization becomes more and more difficult. In this paper, we present QProf, a distributed systems profiler built upon RPC tracing. Going well beyond prior work that focuses on fleetwide profiling of single programs, QProf focuses on cost profiling of entire services. By treating a distributed program as if it were a single process, QProf can produce call-graph profiles of entire systems, so it can measure the transitive cost of services through the entire stack of backend dependencies. QProf is backed by a novel tracing mechanism called skeletal tracing which is exceedingly low-overhead and invisible to applications, ensuring that tracing minimally perturbs application behavior. An ambient per-query CPU profiler ensures high profiling coverage across thousands of microservices without manual instrumentation, and new fan-in APIs provide support for representing batched requests in a tracing data model that is tree-centric. QProf processes billions of traces per day to produce aggregated datasets which can be queried by engineers. Despite the sampled nature of traces, QProf is highly accurate in measuring QPS and CPU across thousands of microservices in an extremely diverse datacenter fleet. QProf has been deployed for every job in Google’s production fleet for several years. It has a myriad of use cases, and we present several case studies showing how it has been used to find the “room-at-the-top” in complex systems and optimize them across many dimensions.
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Budget-Aware Tool Use Enables Effective Agent Scaling
Tengxiao Liu
Jin Miao
I-Hung Hsu
Samira Daruki
COLM 2026 (2026)
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Scaling test-time computation improves performance across different tasks on large language models (LLMs), yet mainstream scaling methods remain challenging for tool-augmented LLM agents. Sequential scaling tends to yield shallow tool use and under-exploration, whereas parallel scaling inflates cost through repeated tool calls. The dual costs of tokens and tool calls further complicate cost accounting and hinder fair comparison. In this work, we study the test-time scaling of widely used, tool-reliant search agents under resource constraints, analyzing performance with a unified cost metric that incorporates both tokens and tool calls. To this end, we propose Cost-effective Agent Test-Time Scaling (CATS), a budget-aware framework designed to support more cost-effective scaling by guiding resource allocation between sequential and parallel exploration. Experiments across search-intensive benchmarks show that CATS produces more favorable scaling curves, attaining higher accuracy with fewer tool calls and lower overall cost. Our work introduces a cost-conscious design for agent test-time scaling and contributes empirical insights that enable a more transparent and principled understanding of scaling in tool-augmented agents.
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While modern text-to-image (T2I) models excel at generating high-quality images, they are typically trained to optimize with respect to generalized, population-level preferences. This homogeneous approach ignores the diverse, individual tastes and aesthetic judgments of different users. In this work, we propose a novel framework that learns fine-grained user preferences without relying on computationally expensive visual language models (VLMs) or prompt-sensitive text profiles. Instead, we introduce a robust, continuous user representation that models a user’s reward function as a linear combination of K base user types. We learn user-specific weights $\lambda_u$ via logistic regression on pairwise preference data to construct a continuous user embedding. This embedding is integrated into the diffusion process via an IP-Adapter, and fine-tuned using Diffusion-DPO. Our approach consistently generates images aligned with individual reward functions, achieving a 66.2% win rate against a pre-trained SDXL baseline and a 63.2% win rate against the state-of-the-art PPD framework.
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Optical Transport Optimized for AI Infrastructure - Fiber Is The New Wavelength (𝝺)
Ahsaan Yousaf
2026
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With ML/AI as growth drivers for WAN, highlight unique challenges and drive the industry for next gen coherent pluggable and embedded terminal optics solutions with performance and RSP tradeoffs in mind.
Together with other hyperscalers, drive the industry (system and component vendors) to develop highly space and power efficient ILA solutions, compatible with our existing infrastructure. The right technology evolution will have to consider overall TCO and operational/practical aspects such as power consumption and failure domains.
Zero Touch Networking: Highlighting importance of standard open models and APIs across IP/Optical layers across module/host vendors enabling vendor agnostic config, telemetry for autonomous operations
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The field of Human-Computer Interaction is approaching a critical inflection point, moving beyond the era of static, deterministic systems into a new age of self-evolving systems. We introduce the concept of Adaptive generative interfaces that move beyond static artifacts to autonomously expand their own feature sets at runtime. Rather than relying on fixed layouts, these systems utilize generative methods to morph and grow in real-time based on a user’s immediate intent. The system operates through three core mechanisms: Directed synthesis (generating new features from direct commands), Inferred synthesis (generating new features for unmet needs via inferred commands), and Real-time adaptation (dynamically restructuring the interface's visual and functional properties at runtime). To empirically validate this paradigm, we executed a within-subject (repeated measures) comparative study (N=72) utilizing 'Penny,' a digital banking prototype. The experimental design employed a counterbalanced Latin Square approach to mitigate order effects, such as learning bias and fatigue, while comparing Deterministic interfaces baseline against an Adaptive generative interfaces. Participant performance was verified through objective screen-capture evidence, with perceived usability quantified using the industry-standard System Usability Scale (SUS). The results demonstrated a profound shift in user experience: the Adaptive generative version achieved a System Usability Scale (SUS) score of 84.38 ('Excellent'), significantly outperforming the Deterministic version’s score of 53.96 ('Poor'). With a statistically significant mean difference of 30.42 points (p < 0.0001) and a large effect size (d=1.04), these findings confirm that reducing 'navigation tax' through adaptive generative interfaces directly correlates with a substantial increase in perceived usability. We conclude that deterministic interfaces are no longer sufficient to manage the complexity of modern workflows. The future of software lies not in a fixed set of pre-shipped features, but in dynamic capability sets that grow, adapt, and restructure themselves in real-time to meet the specific intent of the user. This paradigm shift necessitates a fundamental transformation in product development, requiring designers to transcend traditional, linear workflows and evolve into 'System Builders'—architects of the design principles and rules that facilitate this new age of self-evolving software.
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