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 11624 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)
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.
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TCO-driven Storage Provisioning for Exascale Data Centers
Timothy Kim
Prashant Nema
Jai Menon
Rashmi Vinayak
Gregory R. Ganger
2026
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Recent changes in data temperatures and storage device characteristics, both mechanical disk drives (HDDs) and solidstate drives (SSDs), expand the set of deployment options for exascale storage. Until recently, exascale storage systems followed a pattern of placing most data on HDDs with smaller amounts of SSD storage used for caching and performance-critical workloads. Exascale storage provisioning and dataset placement trade-offs have now changed.
This paper describes a total cost of ownership (TCO) model that captures primary aspects of modern deployments and uses it to explore the new trade-off space. Using capacity and performance telemetry information for 43 production datasets+workloads at two large hyperscalers, we show significant changes from prior analyses of workloads and storage placement decisions across a multitude of storage device types. We also introduce a storage cluster TCO optimizer that identifies the lowest-TCO grouping and assignment of datasets to device types, exposing a number of insights that can help guide future deployments. For example, our analysis shows that the highest-density SSDs are particularly favorable for clusters with heavy AI/ML workloads but are only cost-effective at exascale when combined with high-density HDDs. Finally, we use our framework to evaluate how storage provisioning and overall TCO change as a function of key parameters like device write amplification, cluster power bounds, and the maximum number of device types allowed.
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VIP-MINGLE: A Corpus for Videoconference and In-Person Multimodal Interaction in Group Language Engagement
Abhinay K Bodi
Wenxin Deng
Junrui Huang
Venu G Kadamba
Sumanth B H Karanam
Dhiwahar A Kennady
David Poeppel
Dustin Freeman
Interspeech (2026)
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Group conversations are a fundamental yet complex form of social interaction central to human cognition and telecommunication technology. While understanding and facilitating these interactions has been a long-standing goal, findings are often isolated within specific in-person or videoconferencing settings due to a scarcity of datasets that bridge the two. We introduce VIP-MINGLE, a multimodal dataset comprising 59 hours of recordings (32 groups, 105 participants), featuring paired within-subject sessions in both settings. The dataset includes raw audio/video, psychometric data, processed multimodal features (e.g., diarized speech, facial expressions, transcriptions), and time-resolved human annotations. Our analysis reveals significant behavioral distribution shifts across multiple modalities between settings, reinforcing the need for a cross-setting corpus. VIP-MINGLE serves as a critical resource for developing robust models of group conversations across settings.
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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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Improving reanalysis weather for contrail validation by incorporating satellite observations
Susanne Rohs
Roger Teoh
Journal of Environmentally Compatible Air Transport System (JECATS) (2026)
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Aviation-induced condensation trails (contrails) contribute significantly to anthropogenic radiative forcing. While navigational contrail avoidance has been proposed as a strategy to mitigate this climate impact, the operational viability of such maneuvers relies on the ability to verify their efficacy. Current verification methodologies often employ contrail models driven by reanalysis weather data; however, these assessments are limited by the variable fidelity of the underlying meteorological datasets. In this work, we address this uncertainty by leveraging satellite observations to refine reanalysis estimates for specific contrail events. We demonstrate that this approach significantly improves the agreement between reanalysis data and in-situ measurements obtained from the IAGOS program, thereby offering a more robust framework for evaluating avoidance strategies.
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Preview abstract
Temporally consistent long video generation remains a fundamental challenge. Existing methods suffer from feature drift, where entities and environments gradually change unintentionally, or content collapse, where narratives fail to progress meaningfully. We introduce A${^2}$RD, an agentic autoregressive video generation architecture that decouples creative synthesis from consistency by modeling consistency as a test-time objective. A${^2}$RD features segment-by-segment generation augmented with a novel Multimodal Video Memory (\memory{}) that tracks segment contexts and dynamics and Test-Time Scaling algorithms that verify and refine generation. For each segment, it operates in a Retrieve--Synthesize--Refine--Update (RSRU) loop: the agent retrieves relevant contexts, determines the segment generation mode (extrapolation or interpolation) adaptively, synthesizes boundary frames then video segment with refinements applied at both frame and video levels, and updates \memory{} for subsequent generation. We further develop LVbench-C, a challenging benchmark measuring long-horizon entities and environments evolving in non-linear transitions. Extensive experiments on public and LVbench-C benchmarks across one-, three-, and five-minute video generation demonstrate that A${^2}$RD generates significantly more consistent, meaningful videos than existing baselines. Human evaluations confirm strong consistency in characters, objects, and environments, with smooth motion and meaningful narrative progression.
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ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients"
Kohei Uehara
Haoyu Zhang
Lin Gu
Zheng Xu
Tatsuya Harada
ACL 2026 (2026)
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Prior work synthesizes tool-use LLM datasets by first generating a user query, followed by complex tool-use annotations like depth-first search (DFS). This leads to inevitable annotation failures and low efficiency in data generation. We introduce ToolGrad, an agentic framework that inverts this paradigm. ToolGrad first constructs valid tool-use chains through an iterative process guided by textual "gradients", and then synthesizes corresponding user queries. This "answer-first" approach led to ToolGrad-500, a dataset generated with more complex tool use, lower cost, and almost 100% pass rate. Experiments show that ToolGrad models outperform those trained on expensive baseline datasets and proprietary LLMs.
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PaperBanana: Automating Academic Illustration for AI Scientists
Dawei Zhu
Sujian Li
International Conference on Machine Learning (2026)
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While AI scientists increasingly automate research tasks through advanced language models, generating publication-ready illustrations remains a labor-intensive bottleneck in the scientific workflow. To lift this burden, we introduce PaperBanana, an agentic framework for automated generation of publication-ready academic diagrams. Powered by Nano-Banana-Pro and Gemini-3-Pro, PaperBanana orchestrates a team of specialized agents to retrieve reference examples, devise detailed plans for content and style, render the image, and perform iterative refinement based on self-critique. To rigorously evaluate our framework and address the absence of dedicated benchmarks for automated academic illustration, we introduce PaperBananaBench, comprising 292 test cases for methodology diagrams curated from NeurIPS 2025 publications.
Comprehensive experiments demonstrate that PaperBanana consistently outperforms vanilla Nano-Banana-Pro across all four dimensions—faithfulness, conciseness, readability, and aesthetics—achieving human-level performance. We further show that PaperBanana seamlessly extends to statistical plots through targeted adaptations. Collectively, PaperBanana enables AI scientists to fully automate the generation of publication-ready academic illustrations.
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Usability Hasn’t Peaked: Exploring How Expressive Design Overcomes the Usability Plateau
Alyssa Sheehan
Bianca Gallardo
Ying Wang
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26), April 13–17, 2026, Barcelona, Spain (2026)
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Critics have argued that mobile usability has largely been optimized, and that only incremental gains are possible. We set out to explore if the newest generation of design systems, which promote greater flexibility and a return to design basics, could produce substantially more usable designs while maintaining or increasing aesthetic judgments. Through a study with 48 diverse participants completing tasks in 10 different applications, we found that in designs created following Material 3 Expressive guidelines, users fixated on the correct screen element for a task 33% faster, completed tasks 20% faster, and rated experiences more positively compared to versions designed using the previous Material design system. These improvements in performance and aesthetic ratings challenge the premise of a usability plateau and show that mobile usability has not peaked. We illustrate specific opportunities to make mobile experiences more usable by returning to design fundamentals while highlighting risks of added flexibility.
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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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Understanding long visual documents, where information is distributed across extensive pages of text and graphics, remains a critical challenge for modern Vision-Language Models (VLMs). This difficulty is rooted in two fundamental obstacles: poor evidence localization and a high tendency for model hallucination. To address these issues, we propose DocLens, a multi-agent framework that decomposes the task into two specialized stages.
First, a Lens Module leverages document parsing tools for fine-grained, hierarchical evidence localization at both the page and element level. Second, a Reasoning Module employs a sampling-adjudication mechanism to systematically analyze the localized evidence, mitigating hallucination and synthesizing reliable answers.
Paired with Gemini-2.5-Pro, DocLens achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, even surpassing human experts. Furthermore, our framework offers a highly cost-effective variant that delivers comparable performance to strong baselines at a five-fold reduction in cost.
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Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action-Spaces
Haitong Ma
Ofir Nabati
Bo Dai
Na Li
Shie Mannor
Guy Tennenholtz
Proceedings of the 43rd International Conference on Machine Learning (ICML-26), Seoul, South Korea (2026)
Preview abstract
Reinforcement learning (RL) algorithms have achieved superhuman performance
on many sequential decision-making tasks, but often struggle in domains with
large, combinatorial action spaces. To address this, we introduce a practical and
stable algorithm for training discrete diffusion models to represent policies in
such environments. We formulate a policy mirror descent algorithm that enhances
training stability by reframing policy optimization as an inference problem, which
naturally aligns with the learning objective of discrete diffusion models. Through
extensive experiments on a suite of challenging benchmark tasks, we demonstrate
that our approach achieves significant improvements over existing methods in both
performance and sample efficiency. This work opens a promising new direction
for applying discrete diffusion models in RL to tackle long-standing challenges in
large-scale combinatorial action spaces.
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Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents
Pengfei He
Ash Fox
Stefan Friedli
Daniel Fabian
Burak Gokturk
Jiliang Tang
ICML 2026
Preview abstract
Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation due to limited interaction, weak execution grounding, and a lack of experience reuse. We propose Code-RedTeam, a security-aware multi-agent framework designed to mirror real-world red-teaming workflows by integrating security-domain knowledge, code-aware analysis, execution-grounded iterative reasoning, and long-term memory. Code-RedTeam decomposes vulnerability analysis into coordinated discovery and exploitation stages, enabling agents to plan, execute, validate, and refine actions based on real execution feedback while learning from prior trajectories. Extensive evaluations on challenging security benchmarks demonstrate that Code-RedTeam consistently outperforms strong baselines across diverse backbone models, achieving over 60% attack success rate in vulnerability exploitation and up to 10% absolute improvement in vulnerability detection. Ablation and iteration studies further confirm the critical role of execution feedback, structured interaction, and memory for building robust and generalizable cybersecurity agents.
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GGN: Experiences in Designing and Deploying Next-Generation Google Global Network
Richard Alimi
Arda Balkanay
Nachikethas Jagadeesan
Bikash Koley
Warren Martins
Arjun Muralidharan
Namrata Pralhad Kadam
Aman Shaikh
Sankalp Singh
Charith Wickramaarachchi
Min Zhu
2026
Preview abstract
Cloud and AI/ML workloads are posing unprecedented new requirements on the wide-area network: it must combine strict availability, massive growth, and feature agility. It became increasingly clear that traditional WAN designs were ill-equipped to adapt to these requirements.
We present Google's Global Network (GGN), a major architectural redesign of our WAN that evolves B2 and B4 into a single, modular, and highly available software-defined network. The architecture is designed around three pillars: (1) A modular design of functional domains with well-defined APIs; (2) a physically sharded and regionalized core for fault isolation and horizontal scaling; (3) a vendor-agnostic hardware strategy based on open standards. We share the multi-year deployment journey of GGN, including a safe, host-steered migration strategy, and demonstrate its ability to improve network availability and reaction time to failures, setting a foundation for a planet-scale modern WAN.
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