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 11637 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)
Preview abstract
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.
View details
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)
Preview abstract
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.
View details
Preview abstract
This study examines the psychological and ethical implications of generative-AI chatbot use among youth, introducing the CTRL framework (Cognitive Trust, Reliance, and Learning Diminution) to explain how repeated use fosters cognitive offloading and reduced verification behavior. Survey data from 420 participants analyzed through factor analysis and structural equation modeling reveal that higher trust predicts greater reliance and diminished critical evaluation, alongside elevated concerns around privacy and academic integrity. Findings highlight the need for AI literacy and responsible design to mitigate unintended cognitive impacts.
View details
Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI
Jiaxun Cao
Yu Dong
Chunxi Zhan
Rithvik Neti
Pardis Emami-Naeini
USENIX Symposium on Usable Privacy and Security (SOUPS) (2026)
Preview abstract
Users increasingly rely on consumer-facing generative AI (GenAI) for tasks ranging from everyday needs to sensitive use cases. Yet, it remains unclear whether and how existing security and privacy (S&P) communications in GenAI tools shape users’ adoption decisions and experiences. Understanding how users seek, interpret, and evaluate S&P information is critical for designing usable transparency that users can trust and act on. We conducted semi-structured interviews and design sessions with 21 U.S. GenAI users. Our findings suggest that available S&P information rarely drove initial adoption in practice, as participants often perceived it as incomplete, ineffective, or not credible. Instead, they relied on rough proxies (e.g., popularity) to infer S&P practices. After adoption, S&P uncertainty constrained participants’ willingness to use GenAI tools, especially for high-stakes purposes, and, in some cases, contributed to discontinued use. Participants therefore called for transparency that supports decisions and actions through trustworthy information (e.g., independent evaluations) and usable interfaces (e.g., on-demand disclosure). We categorize participants’ desired design practices into five dimensions to facilitate systematic future investigation into best practices. We conclude with recommendations for researchers, designers, and policymakers to improve S&P transparency in consumer-facing GenAI.
View details
Who is At-Risk? Surveying the Prevalence of Risk Factors and Tech-Facilitated Attacks in the United States
Sharon Heung
Claire Weizenegger
Mo Houtti
Patrick Gage Kelley
Tara Matthews
Ashley Walker
2026
Preview abstract
A growing body of qualitative research has identified contextual risk factors that elevate people’s chances of experiencing digital-safety attacks. However, the lack of quantitative data on the population level distribution of these risk factors prevents policymakers and tech companies from developing targeted, evidence-based interventions to improve digital safety. To address this gap, we surveyed 5,001 adults in the United States to analyze: (1) the frequency of and relationship between digital-safety attacks (e.g., scams, harassment, account hacking), and (2) how these attacks align with 10 contextual risk factors. Nearly half of our respondents identify as resource constrained, which significantly correlates with higher likelihood of experiencing four common attacks. We also present qualitative insights to expand our understanding of the factors beyond the existing literature (e.g., “prominence” included high-visibility roles in local communities). This study provides the first large-scale quantitative analysis correlating digital-safety attacks with contextual risk factors and demographics.
View details
Preview abstract
As AI agents increasingly operate with system-level privileges across cloud environments, they face significant security vulnerabilities—most notably prompt injection and unauthorized command execution. With 72% of agent deployments experiencing security incidents within 90 days, there is a critical need for robust, defense-in-depth strategies. This article introduces "Bottom-Up AI Agent Security," a comprehensive 14-layer framework designed to secure AI agents from the foundation up. The strategy covers five essential domains: code-level security, container hardening, cloud IAM, runtime monitoring, and human oversight. By providing actionable implementation templates (including Java, YAML, and GitHub Actions) and a structured 4-week deployment roadmap, this framework enables cloud architects to transform vulnerable AI deployments into hardened, production-ready systems with measurable reductions in risk and operational costs.
View details
Preview abstract
In large-scale distributed enterprises, traditional Knowledge Management (KM) systems face a critical failure mode: static documentation cannot keep pace with evolving operational realities and regional nuances. This "knowledge latency" forces employees out of self-service workflows and into costly support ticketing queues. This paper introduces SENTINEL, a geo-contextual AI framework designed to shift enterprise support from reactive retrieval to proactive interception. The architecture employs a novel dual-engine system integrated into an omni-present interface. The first engine utilizes Large Language Models (LLMs) to conduct pre-emptive, historical case-grounded audits of documentation, generating a "Contextual Density" score that identifies friction zones. The second engine is an autonomous Retrieval-Augmented Generation (RAG) agent that surfaces in-situ via a location-intelligent assistant window, resolving queries in real-time. By functioning as a strategic "defensive barrier" at the point of origin, SENTINEL demonstrates how a proactive AI assistant can drive high-fidelity, in-situ case deflection.
View details
XProf: An Open, Scalable and Extensible Profiling System for the Modern ML Stack
Robert Hundt
Naveen Kumar
Jose Baiocchi Paredes
Scott Goodson
Kelvin Le
Yin Zhang
Kan Cai
Sai Ganesh Bandiatmakuri
Yogesh SY
Ani Udipi
Vikas Aggarwal
Ninth Conference on Machine Learning and Systems (2026)
Preview abstract
Optimizing Large Models across thousands of accelerators requires deep system expertise. To address modern machine learning (ML) optimization needs, we present XProf, the ML profiler for the OpenXLA ecosystem. XProf delivers actionable optimization suggestions and in-depth performance analysis, empowering ML researchers and framework users to improve efficiency without specialized systems knowledge. XProf provides a unified, full-stack view of both host (CPU) and device (accelerator - TPUs/GPUs) performance, leveraging tools like the Roofline Model for comprehensive analysis. XProf’s distributed architecture is designed to monitor thousands of chips with minimal workload overhead (<1%). This architecture is made pluggable through the open-source PJRT C API extension, which has facilitated its adoption by third-party accelerator vendors. XProf has been instrumental in achieving significant efficiency gains at Google and winning MLPerf submissions. This paper presents the design and architecture of XProf, showcases its differentiating tools and capabilities, and highlights its impact within Google and across the industry as a state of the art ML profiler. XProf is available as part of the OpenXLA project at https://github.com/openxla/xprof.
View details
Preview abstract
Here’s a thought experiment. Say I wave a magic wand across a codebase and an entire class of technical debt, poof, goes away and immediately evaporates if introduced in the future. For example, maybe I make it so that dead feature flags are simply no longer a problem: they just delete themselves as soon as the engineer wills it. Or maybe large-scale migrations just migrate themselves. Maybe we magically have 100% test coverage, without an engineer lifting a finger.
What will happen to developer productivity?
Surely, developer productivity increases overall.
But will the productivity metrics that we all use as a proxy for “developer productivity” move up and to the right. Let’s explore this idea.
View details
LibEvoBench: Probing Temporal Knowledge Startification in Code Generation Models
Daniele Cipollone
Sergey Titov
Egor Bogomolov
Arie van Deursen
Advances in Neural Information Processing Systems (NeurIPS) (2026)
Preview abstract
Large software projects often depend on older versions of libraries, even as APIs continue to evolve across releases. This creates a challenge for LLMs: they must maintain knowledge of multiple API versions, not merely the latest or most common one. However, current LLMs, trained on temporally mixed corpora and lack explicit mechanisms for such version-specific reasoning, leading to anachronistic errors -- calling APIs as they exist in a different library version. To systematically evaluate this phenomenon, we introduce LibEvoBench, a multi-task benchmark spanning multiple versions of widely used Python libraries, along with a new metric, the Software Evolution Understanding Score (SEUS), to measure models' consistency when working with evolving APIs. Our results show that state-of-the-art models are largely version-oblivious: performance degrades for evolving APIs, while for stable APIs it remains the same across versions. Moreover, simply specifying the target version provides no benefit, while relevant documentation significantly boosts models' accuracy. These findings highlight a systematic limitation of current training paradigms and motivate new approaches for temporally grounded knowledge in code generation.
View details
Claim-Gated Source-Risk Auditing for Generative Search
Chuhong Xu
Mark Zhou
Iris Qian
Zhaoyi Li
2026
Preview abstract
Generative search can cite a passage accurately
while omitting a material relationship that changes how the
passage should be interpreted. This paper introduces an original
methodological framework and implementation blueprint for
auditing that gap; it is not an AI detector, classifier, or empirical
performance study. The framework combines a five-dimensional
query–source–answer record, an operational source-role taxonomy,
a derived omission endpoint, and explicit rules for uncertainty
and human review. Its central contribution is a claim-gated
evaluation protocol specifying the sampling, evidence capture,
blinded annotation, adjudication, agreement, accuracy, calibration,
error analysis, and paired intervention tests required before
detector, prevalence, or defense-effect claims are admissible. A
1,000-row synthetic workbook serves only as a specification fixture
for testing serialization and machine-checkable invariants. Its
score vectors are normalized within 5 × 10−4
and its recorded
labels match their vector argmax, but it lacks the provenance and
independent labels required for semantic validation. We therefore
make no claim about Web prevalence, detector accuracy, or
deployment benefit. The result is an implementation-oriented
audit methodology with falsifiable evidence gates for future realWeb evaluation.
View details
Preview abstract
Advancements in large language models (LLMs) have made them increasingly useful for complex reasoning tasks which previously required domain experts. One such task is quality evaluation of query responses produced by a search engine. Evaluation generates metrics necessary to study the quality, impact, and usefulness of product changes and features. Typically, to compute evaluation metrics, human experts are asked to rate various attributes of search responses. This process is generally quite expensive and requires several days to complete. As an alternative, LLMs are now being used to perform rating tasks with lower costs and latency. In addition, many new metrics are being developed to evaluate Google's new AI-based offerings, which require ratings too. As a result, there is much higher demand for LLM rating prediction tasks in comparison with the allocated TPU (Tensor Processing Unit) budget. A larger portion of the company's TPU resources are reserved for serving live user traffic. In this paper, we present the AI Rater Service (AIRS), an inference pipeline that employs several software engineering techniques to generate AI ratings with high reliability and low latency. AIRS maximizes LLM inference throughput by optimizing TPU resource utilization across various evaluation workflows, while minimizing latency for higher priority tasks.
View details
Preview abstract
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.
View details
Expert evaluation of LLM world models: A high-Tc superconductivity case study
Haoyu Guo
Maria Tikhanovskaya
Paul Raccuglia
Alexey Vlaskin
Chris Co
Scott Ellsworth
Matthew Abraham
Lizzie Dorfman
Peter Armitage
Chunhan Feng
Antoine Georges
Olivier Gingras
Dominik Kiese
Steve Kivelson
Vadim Oganesyan
Brad Ramshaw
Subir Sachdev
Senthil Todadri
John Tranquada
Eun-Ah Kim
Proceedings of the National Academy of Sciences (2026)
Preview abstract
Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. This work evaluates the performance of six different LLM-based systems for answering scientific literature questions, including commercially available closed models and a custom retrieval-augmented generation (RAG) system capable of retrieving images alongside text. We conduct a rigorous expert evaluation of the systems in the domain of high-temperature cuprate superconductors, a research area that involves material science, experimental physics, computation, and theoretical physics. We use an expert-curated database of 1726 scientific papers and a set of 67 expert-formulated questions. The evaluation employs a multi-faceted rubric assessing balanced perspectives, factual comprehensiveness, succinctness, evidentiary support, and image relevance. Our results demonstrate that RAG-based systems, powered by curated data and multimodal retrieval, outperform existing closed models across key metrics, particularly in providing comprehensive and well-supported answers, and in retrieving relevant visual information. This study provides valuable insights into designing and evaluating specialized scientific literature understanding systems, particularly with expert involvement, while also highlighting the importance of rich, domain-specific data in such systems.
View details
Verification-Driven Interface Convergence: Methodology for Validating Functional Interoperability And Accelerating Subsystem Design for Concurrently Developed Compute and Component IPs in Coherent Subsystem
Alisha Parvez
Preethi Ashok Kumar
Shreya Singh
Jyothi Kumari
Rohit Jindal
2026
Preview abstract
To meet aggressive time-to-market goals, modern mobile SoC architectures require the concurrent development of custom Compute-IPs and surrounding subsystem integration logic (1PIPs and 3PIPs). However, this parallel execution creates a critical verification deadlock: the subsystem cannot be validated until both the Compute-IP and volatile, in-flight 1PIPs reach physical RTL maturity. Consequently, "integration-killer" bugs—such as protocol handshaking deadlocks and clock/reset sequencing mismatches—remain hidden until late in the design cycle when RTL rework costs are prohibitive. To break this bottleneck, we present a verification-driven methodology utilizing a silicon-proven Golden Proxy, Direct-Execution Traffic Profiles, Programmable Sequencers and Automated Protocol Converters. This framework completely decouples parallel hardware dependencies, pre-pulling critical inter-IP mismatch discoveries months ahead of traditional integration milestones.
View details
