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 11583 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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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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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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Inhibitory normalization of error signals improves learning in neural circuits
Roy Henha Eyono
Jonathan Cornford
Arna Ghosh
Daniel Levenstein
Neural Computation (2026)
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Normalization is a critical operation in neural circuits. In the brain, there is evidence that normalization is implemented via inhibitory interneurons and allows neural populations to adjust to changes in the distribution of their inputs. In artificial neural networks (ANNs), normalization is used to improve learning in tasks that involve complex input distributions. However, it is unclear whether inhibition-mediated normalization in biological neural circuits also improves learning. Here, we explore this possibility using ANNs with separate excitatory and inhibitory populations trained on an image recognition task with variable luminosity. We find that inhibition-mediated normalization does not improve learning if normalization is applied only during inference. However, when this normalization is extended to include back-propagated errors, performance improves significantly. These results suggest that if inhibition-mediated normalization improves learning in the brain, it acts primarily on learning signals rather than solely on sensory inputs.
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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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Optimized Deferral for Imbalanced Settings
Anqi Mao
Proceedings of the 43rd International Conference on Machine Learning (ICML 2026)
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Learning algorithms can be significantly improved by routing complex or uncertain inputs to specialized experts, balancing accuracy with computational cost. This approach, known as learning to defer, is essential in domains like natural language generation, medical diagnosis, and computer vision, where an effective deferral can reduce errors at low extra resource consumption. However, the two-stage learning to defer setting, which leverages existing predictors such as a collection of LLMs or other classifiers, often faces challenges due to an expert imbalance problem. This imbalance can lead to suboptimal performance, with deferral algorithms favoring the majority expert. We present a comprehensive study of two-stage learning to defer in expert imbalance settings. We cast the deferral loss optimization as a novel cost-sensitive learning problem over the input-expert domain. We derive new margin-based loss functions and guarantees tailored to this setting, and develop novel algorithms for cost-sensitive learning. Leveraging these results, we design principled deferral algorithms, MILD (Margin-based Imbalanced Learning to Defer), specifically suited for expert imbalance settings. Extensive experiments demonstrate the effectiveness of our approach, showing clear improvements over existing baselines on both image classification and real-world Large Language Model (LLM) routing tasks.
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XMob: Accelerated Differentiable Mesoscopic Traffic Simulation in JAX
Sam Griesemer
John Burge
Shantanu Shahane
Yechen Li
Naples, Italy (2026)
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This paper introduces XMob, a novel differentiable traffic simulation framework built in JAX to advance traditional models like SUMO’s mesoscopic simulator. By leveraging JAX’s capabilities for vectorized, hardware-accelerated computation (GPU/TPU), XMob achieves orders-of-magnitude speedups, enabling large-scale urban network simulations and extensive counterfactual analyses. A key innovation is XMob’s inherent differentiability, facilitating direct integration with gradient-based optimization for tasks such as demand calibration and network parameter estimation, significantly outperforming black-box approaches. Furthermore, XMob can be used in Physics-Informed Machine Learning (PIML) pipelines to enhance data-driven augmentation, embedding domain principles like flow conservation and shockwave theory. This ensures physically plausible and robust predictions, even for unobserved scenarios such as lane modifications. The hybrid architecture, combining a deterministic JAX core with incremental machine learning, offers a scalable and efficient solution for modern traffic simulation and optimization challenges.
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Nine changes needed to deliver a radical transformation in biodiversity measurement
Neil Burgess
Andy Purvis
Scott J. Goetz
William Sutherland
Anil Madhavapeddy,
Tanya Birch
PNAS Perspective (2026)
Preview abstract
Biodiversity is declining in many parts of the world. The measurement and monitoring of
biological diversity are fundamental to the assessment of the causes and consequences of
environmental changes, identification of key areas for the protection of biodiversity or
ecosystem services, determining the effectiveness of actions, and the creation of
decision-support tools critical to the maintenance of a sustainable planet. The measurement
of biodiversity is rapidly changing due to advances in citizen science, image recognition,
acoustic monitoring, environmental DNA, genomics, remote sensing and artificial
intelligence. In this perspective, we outline the exciting opportunities that these
developments offer, but also consider the challenges, especially the potential poisoning of
data by AI, lack of standardisation across methods, coverage gaps in data, concerns over
losing databases, and undervaluing of on-the-ground expertise and data-generation. Our key
recommendations are (1) ensure new technologies are calibrated with existing data; (2) use
emerging technologies to fill data gaps; (3) create living databases of trusted information to
increase reliability of data and reduce the risk of poisoning by false - or AI hallucinated -
information; (4) ensure data generation is valued; (5) ensure the respect and incorporation
of Indigenous Knowledge; (6) increase in-country capacity in the tropics; and (7) increase the
resilience of global datasets to technical and societal change. Radical new collaborations are
needed between computer scientists, engineers, molecular biologists, data scientists, field
ecologists, citizen scientists, Indigenous peoples, and local communities to create the
rigorous, resilient, accessible biodiversity information systems required to underpin policies
and practices that ensure the maintenance and restoration of ecological systems.
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A simple and efficient implementation of strong call by need by an abstract machine
Małgorzata Biernacka
Witold Charatonik
Journal of Functional Programming, Volume 36 (2026)
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We present an abstract machine for a strong call-by-need strategy in the lambda calculus. The machine has been derived automatically from a higher-order evaluator that uses the technique of memothunks to implement laziness. The derivation has been done with the use of an off-the-shelf transformation tool implementing the "functional correspondence" between higher-order interpreters and abstract machines, and it yields a simple and concise description of the machine. We prove that the resulting machine conservatively extends the lazy version of Krivine machine for the weak call-by-need strategy, and that it simulates the normal-order strategy in bilinear number of steps.
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Shuffles of Context-Free Languages along Regular Trajectories
Corentin Barloy
Michaël Cadilhac
Kyle Ockerlund
2026
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In single-core processors, concurrency requires that multiple processes be interleaved into a single thread of execution by a scheduler. The language-theoretic operation that corresponds to this is the shuffle of two languages: the set of words obtained by interleaving a word from each language in an arbitrary, letter-wise fashion. It is well known that regular languages are closed under shuffles, while context-free languages (CFLs) are not.
Following an established line of research, this paper considers shuffles according to regular "trajectories," that is, subject to scheduling constraints expressed by an automaton. Unsurprisingly, some trajectories allow for CFLs to be shuffled into CFLs (e.g., simple concatenation of the two words), while others do not. This paper provides a robust toolset to show that a given trajectory would always shuffle two nonregular CFLs into a nonCFL. In the case of deterministic CFLs (DCFLs), a salient trichotomy of trajectories depending on how they shuffle DCFLs is provided.
These results are based on lemmata of independent interest regarding how pushdown automata (PDA) must invoke the stack when accepting a nonregular CFL or DCFL. The latter case relies on a recent result of Jančar and Šíma (MFCS'2021); answering an open question therein, it is demonstrated that said result cannot be generalized to arbitrary CFLs, leading to dedicated machinery for both cases.
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Lessons learned from building an agent to convert the R Forecast Package into a JAX api
Skander Hannachi, Jasmeet Bhatia, Dennis Kashkin, Anna Novakovska - Applied AI Engineering, Google Cloud
{shannachi@ jasmeetbhatia@ kashkin@ anovakovska@}google.com
Accepted at ISF 2026
Abstract:
The Forecast package in R, is one of the most popular and established frameworks for learning and working with statistical (local) time series models. However, over the last decade or so, the language of choice for data analysis and mathematical modeling has become Python, especially, since, unlike R, the latter provides multiple options for running models on dedicated hardware accelerators (TPUs/GPUs), and on distributed compute infrastructure.
In this study, we implement an LLM agent based code conversion pipeline that automatically converts the code from the Forecast package (R, C++), into JAX/PAX, a more recent modeling framework dedicated specifically for running compute heavy modeling tasks on TPUs and GPUs. We run our specialized code conversion agent against three core models of the Forecast package: TBATs, auto.arima(), and ETS(), with an aim to provide the exact same user experience as the original R API, but with the underlying model selection, model fitting, and forecast generation running all using JAX and PAX operations, running in a Python environment.
We report the results of our experiments and discuss the challenges we observed while running it. We compile these into a skills markdown file, which can be used by other agents intended to perform similar experiments. We provide the JAX based implementations, along with the skills file in an accompanying open source repo.
This is not the first attempt at converting the Forecast package into Python. Those other efforts however are significantly labor intensive, especially when it comes to ensuring parity with the source modeling APIs and quality control in general. Moreover, such projects rely heavily on the long term commitment of both community members and institutional contributors to the conversion effort.
The purpose of our effort is to show how this agent based process can be applied to automate any data science package upgrade or language conversion process with minimal contributors required outside of the core package maintainer team. Especially since the concept of agent skills files makes the process inherently self-improving, both within the scope of a single conversion effort, as well as across multiple long term conversion efforts. For example, the same approach can be applied to a future effort for upgrading the Forecast package to work with Julia, an even more recent and promising modeling language, while benefitting from the R-2JAX lessons learned.
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Can AI systems be aligned to human values? The popularization of large language models (LLMs) and multi-modal foundation models
has seen a commensurate rise in ways these models cause harm, spanning areas from toxic speech and hallucinations to AI agents
executing unauthorized actions. Given that these models are probabilistic and general-purpose by nature, it is impossible to enumerate
all possible uses and outputs of the model to reach a fully aligned end state. Within the field of AI safety, these harmful instances are
often framed as “the alignment problem,” of models being “misaligned” with human values. Researchers have responded by pursuing
applied and theoretical AI “value alignment” efforts, often without specifying what they mean by human values. How does the field of
AI value alignment conceive of human values? How are these conceptions of values technically operationalized and evaluated? What
does the emergent theory of value from this field signify for the future of AI?
The study of human values has long been part of many academic disciplines outside of computer science, yet these disciplines
are seldom consulted in AI alignment. Building on the theoretical insights of Zhi-Xuan’s (2024) "preferentist paradigm" critique, we
conduct a review of influential AI alignment literature. We also draw from conceptions of human values from philosophy, anthropology,
and sociology, to create an analytical schema. We annotated 94 AI value alignment research papers to discern their implicit theory of
values in AI. The majority do not define values, relying heavily on “preferences” as a stand-in that runs the risk of reducing complex,
culturally situated concepts down to binary choices. As researchers dispense with using human annotators for model training and
evaluation, turning instead to synthetic data and LLM-as-a-judge approaches to aligning and evaluating models, we identify the
potential to close off alternative methods for contesting and enacting values in foundation models. Overall, value alignment is often
reduced to an exercise in utility maximization, which we argue abstracts human values away from their lived context. In making AI
value alignment’s philosophical commitments explicit, we seek to bring greater specificity and under-explored perspectives into the
debate on whether and how AI can address human values
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Deep-learning methods have boosted the analytical power of Raman spectroscopy, yet they still require large, task-specific, labeled datasets and often fail to transfer across application domains. The study explores pre-trained encoders as a solution. Pre-trained encoders have significantly impacted Natural Language Processing and Computer Vision with their ability to learn transferable representations that can be applied to a variety of datasets, significantly reducing the amount of time and data required to create capable models. The following work puts forward a new approach that applies these benefits to Raman Spectroscopy. The proposed approach, RSPTE (Raman Spectroscopy Pre-Trained Encoder), is designed to learn generalizable spectral representations without labels. RSPTE employs a novel domain adaptation strategy using unsupervised Barlow Twins decorrelation objectives to learn fundamental spectral patterns from multi-domain Raman Spectroscopy datasets containing samples from medicine, biology, and mineralogy. Transferability is demonstrated through evaluation on several models created by fine-tuning RSPTE for different application domains: Medicine (detection of Melanoma and COVID), Biology (Pathogen Identification), and Agriculture. As an example, using only 20% of the dataset, models trained with RSPTE achieve accuracies ranging 50%–86% (depending on the dataset used) while without RSPTE the range is 9%–57%. Using the full dataset, accuracies with RSPTE range 81%–97%, and without pretraining 51%–97%. Current methods and state-of-the-art models in Raman Spectroscopy are compared to RSPTE for context, and RSPTE exhibits competitive results, especially with less data as well. These results provide evidence that the proposed RSPTE model can effectively learn and transfer generalizable spectral features across different domains, achieving accurate results with less data in less time (both data collection time and training time).
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Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair
José Cambronero
Renyao Wei
Grant Uy
34th ACM International Conference on the Foundations of Software Engineering (FSE) (2026)
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Bug Reproduction Tests (BRTs) have been used in many Automated Program Repair (APR) systems, primarily for validating fixes and aiding fix generation. In practice, when developers submit a patch, they often implement the BRT alongside the fix. Our experience deploying agentic APR reveals that developers desire a BRT within AI-generated patches to increase their confidence. However, canonical APR systems tend to generate BRTs and fixes separately, and focus on producing only the fix in the final patch. In this paper, we study agentic APR in the context of cogeneration, where the APR agent is instructed to generate both a fix and a BRT in the same patch. We evaluate the effectiveness of different cogeneration strategies on 120 human-reported bugs at Google and characterize different cogeneration strategies by their influence on APR agent behavior. We develop and evaluate patch selectors that account for test change to select patches with plausible fixes (and plausible BRTs). Finally, we analyze the root causes of failed cogeneration trajectories. We show that cogeneration allows the APR agent to generate BRTs for at least as many bugs as a dedicated BRT agent, without compromising the generation rate of plausible fixes, thereby reducing engineering effort in maintaining and coordinating separate generation pipelines for fix and BRT at scale.
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Differential Validator: Ensuring SoC ROM to Hierarchical Blocks Node Voltage Integrity
Prateek Pendyala
Mathew Kaipanatu
2026
Preview abstract
SoC Flat IR/EM signoff is generally done for multiple cycles thus generally mandating 2+ days to cover a single scenario- not only is the coverage limited but also expensive since even small ECO fixes trigger full analysis repeat (of same resources). Additionally, designs which have multiple hierarchical block level instantiations - this is massively computationally redundant.
Reduced Order Model (ROM) Flow: Hierarchical Abstraction for IR/EM Signoff:
ROM eliminates computational redundancy by using abstract representations of pre-verified blocks. It leverages tweaked SoC flat analysis to have appropriate block level details to enable 10-20× faster SoC turnaround and broader scenario coverage. Below is its mechanism:
The Common Connection Layer (CCL) acts as the electrical boundary between block & SoC top. ROM preserves full detail only at the CCL and CCL-1, while the lower metal layers (M0 to CCL-2) are rolled up into equivalent impedance model to maintain signoff accuracy.
Designers use a mix of detailed instances for same critical block with reduced instances to optimize resource usage as shown in Fig 1.
The Validation Problem with ROM- Trust Gap:
Context Mismatch: ROMs are generated in standalone conditions, failing to account for top-level grid impedance and adjacent block coupling.
Fidelity & Coverage Loss: Abstracting 12-14 layers can mask local voltage violations; current manual spot-checks are insufficient since these fail to quantify if CCL node voltages in all ROM instances match their power-domain & scenario specific simulation values
Objective of this work:
Systematic validation across all ROM instances & all power domains in a quick (wall time ~mins for SoC) else it would offset ROM runtime benefits.
Quantitative fidelity metrics with low violation thresholds & spatial coverage for debug to understand root cause of localised errors.
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