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 11616 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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Block partitioning framework is a core component in any modern video coding standard, as it directly determines the block size used for predictions and transforms. Flexible block partitioning plays a crucial role in the compression efficiency of these standards. This paper provides a technical overview of the block partitioning framework in AV2 video codec, developed by Alliance for Open Media. Partitioning scheme for both coding blocks and transform blocks has been redesigned in AV2. Coding block partitioning is fully recursive with newly designed partitioning options. Also, newly introduced Semi-Decoupled Partitioning (SDP) option provides additional flexibility by allowing luma and chroma components to have decoupled coding block partition trees. On the other hand, the transform block partitioning has been redesigned to use single-level partitioning with more partitioning options. In this paper, we provide a technical overview of the block partitioning framework in AV2 and also provide tool-off test results for several block partitioning aspects.
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We present MOSAIC-GS, a novel, fully explicit, and computationally efficient approach for high-fidelity dynamic scene reconstruction from monocular videos.
Monocular reconstruction is inherently ill-posed due to the absence of sufficient multiview constraints,
making accurate recovery of object geometry and temporal coherence particularly challenging.
To address this, we leverage multiple geometric cues, such as depth, optical flow, dynamic object segmentation, and tracking trajectories,
combined with rigidity constraints to estimate preliminary 3D scene dynamics during an advanced initialization stage.
Recovering scene flow prior to the photometric optimization phase reduces reliance on motion inference from visual appearance alone, which is often ambiguous in monocular settings.
To enable compact representations, fast training, and real-time rendering while supporting non-rigid deformations, the scene is decomposed into static and dynamic components,
with dynamic trajectories represented as time-dependent Poly-Fourier curves for parameter-efficient motion encoding.
We demonstrate that MOSAIC-GS achieves substantially faster optimization and rendering compared to existing methods,
while maintaining reconstruction quality on par with state-of-the-art approaches across standard monocular dynamic scene benchmarks.
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Multi-agent cooperation through in-context co-player inference
Rajai Nasser
Alexander Meulemans
Marissa Weis
João Sacramento
Maciej Wołczyk
Rif A. Saurous
2026
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Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Promising recent work has shown that cooperation can be established between ``learning-aware'' agents that explicitly account for and shape the learning dynamics of their co-players. However, existing approaches typically rely on hardcoded, often inconsistent, assumptions about co-player learning rules or enforce a strict separation between ``naive learners'' updating on fast timescales and ``meta-learners'' observing these updates. Here, we demonstrate that the in-context learning capabilities of sequence models allow for co-player learning awareness without requiring hardcoded assumptions or explicit timescale separation. We show that training sequence model agents against a diverse distribution of co-players naturally induces \textit{in-context best-response} strategies, effectively functioning as learning algorithms on the fast intra-episode timescale. We find that the cooperative mechanism identified in prior work—where vulnerability to extortion drives mutual shaping—emerges naturally in this setting: in-context adaptation renders agents vulnerable to extortion, and the resulting mutual pressure to shape the opponent's in-context learning dynamics resolves into the learning of cooperative behavior.
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CSIG: Congestion Signaling for Datacenter Transports
Abhiram Ravi
Brad Karp
MJ Akhbari
Weida Huang
Konstantinos Prasopoulos
2026
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Optimizing burst-heavy datacenter workloads necessitates fine-grained network control and visibility. We introduce CSIG, a protocol that delivers precise, multi-bit bottleneck congestion signals via a fixed-length Ethernet header. The architecture captures 𝜇s-granularity switch metrics, such as available bandwidth, and signals them to end-hosts using in-band, line-rate operations. We propose Fast Ramp-Up, a congestion control primitive that leverages these bottleneck signals to reduce median RPC latency by 20% and unclaimed bandwidth by 60% in production. Beyond transport-level performance, CSIG enables flow-aware observability by embedding 𝜇s-scale metrics into every packet, allowing individual application transfers to pinpoint their bottleneck location, such as the topology tier limiting their performance. CSIG thus transforms network telemetry from post-hoc correlation into a real time, contextaware capability. We demonstrate CSIG’s broad deployability by validating it across five generations of commodity switch hardware (up to 102.4 Tbps), four NIC generations, and five transport stacks. Our design proves that a streamlined Layer 2 approach, focusing exclusively on the principal path bottleneck, provides transport-agnostic gains without requiring forklift hardware upgrades.
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Rolling Shutter Relative Pose Estimation Made Practical
Daniel Barath
European Conference on Computer Vision (ECCV) (2026)
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Rolling shutter (RS) cameras equip virtually all consumer devices, yet RS-aware relative pose estimation has remained impractical: the state-of-the-art solver requires a minimum of 20 point correspondences, making RANSAC-based robust estimation prohibitively expensive due to the exponential dependence of the iteration count on the sample size. We make RS relative pose estimation practical by introducing affine correspondences (ACs) into the RS two-view geometry. We derive novel \emph{RS-corrected affine constraints} that account for the coupling between point perturbations and the row-dependent essential matrix, providing two equations per correspondence beyond the standard epipolar constraint. Building on these constraints, we develop a linearized algebraic solver that estimates pose and RS motion from only 7 ACs. The solver exploits the physical smallness of RS parameters to linearize the constraints, eliminates the 12 RS unknowns via null-space projection, and solves the remaining degree-20 system via action matrices in 1.2\,ms. On the TUM RS benchmark, our method achieves the best pose and RS parameter accuracy among all tested methods and, uniquely among RS solvers, provides accurate translational velocity estimates -- which are poorly conditioned from point correspondences alone due to a $\vec{v}$-$\vec{t}$ coupling. On the global-shutter EuRoC MAV dataset, the solver achieves comparable accuracy to the standard 5-point algorithm, demonstrating that it generalizes well to the GS setting. Code will be made public.
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Human-AI Interaction for Time-Critical Sensemaking in Missing Persons Investigations
Pola Labedzka
Miri Zilka
Dorian Peters
John Dudley
2026
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Every year an estimated 200,000 people go missing in the UK alone. Missing persons investigations involve challenging time-critical sensemaking tasks based on fragmented data sources. This paper describes a mixed-methods participatory study evaluating data science and AI-driven techniques (summarisation, fact extraction, and data visualisation) for supporting these investigations as part of a human-centered workflow. A series of human-AI interfaces were iteratively designed and tested with search officers and domain experts at Police Scotland. Based on findings, we describe: (1) user and information needs for missing persons investigations; (2) Insights on the benefits and challenges of applying LLM-based techniques in high-risk contexts; and (3) lessons for integrating AI for information and sensemaking tasks in policing more broadly. We highlight that in high-stakes contexts, where accuracy and context-sensitivity are paramount, AI techniques must be balanced with other approaches and designed in close partnership with end-users.
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The Perfection Paradox: From Architect to Curator in AI-Assisted API Design
JJ Geewax
David R Karger
Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26), ACM, Barcelona, Spain, TBD
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Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement Proposals(AIPs). Through a controlled study with 16 industry experts, we compared AI-generated API specifications against human-authored ones. While quantitative results indicated AI superiority in 10 of 11 usability dimensions and an 87% reduction in authoring time, qualitative analysis revealed a paradox: experts frequently misidentified AI work as human (19% accuracy) yet described the designs as unsettlingly “perfect.” We characterize this as a “Perfection Paradox”—where hyper-consistency signals a lack of pragmatic human judgment. We discuss the implications of this perfection paradox, proposing a shift in the human designer’s role from the “drafter” of specifications to the “curator” of AI-generated patterns.
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Editing Everything Everywhere All at Once
Fabio Quattrini
Carmine Zaccagnino
Enis Simsar
Marta Tintore Gazulla
Rita Cucchiara
Silvia Cascianelli
2026
Preview abstract
Editing multiple elements of an image in a single forward pass has recently emerged as a practical alternative to multi-turn image manipulation, offering improved efficiency. However, when several instructions target different regions, semantic interference often leads to attribute leakage and poor edit disentanglement, especially as the number of edits increases. In this work, we propose MICE (Multi-Instance Concurrent Editing), a training-free strategy for scalable multi-instance image editing with Multimodal Diffusion Transformers. MICE modifies the additive bias of joint attention to regulate interactions between instance-specific text, latent, and context tokens identified via user-provided segmentation masks. Specifically, MICE allows intra-instance attention, penalizes interactions between neighboring region tokens, and suppresses unrelated cross-instance attention. As a result, our method enforces attribute binding while preserving global visual consistency. We evaluate MICE on LoMOE-Bench and introduce MICE-Bench, a more challenging benchmark with an average of 8.5 concurrent edits per image. The experiments demonstrate that our approach outperforms strong baselines and recent competitors in terms of the number of attempted edits and faithfulness to the textual editing instruction.
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Modeling multi-chiplet architectures for TPU co-design
Pritha Doddahosahally Narayanappa
Hung-Ming Hsu
Khai Tran
Hardie Cate
Narges Shahidi
Zhijie Deng
Avinash Lingamneni
Thejasvi Vijayaraj
Aditya Yanamandra
Sameer Kumar
Ming Liu
Lluis-Miquel Munguia
2026
Preview abstract
We present an experience report on modeling multi-chiplet hardware accelerators for TPU co-design, and argue that detailed chiplet modeling is necessary to identify bottlenecks and optimizations for future hardware design. Our chiplet modeling tool (CMT) shows that at lower target serving latencies, serving queries/second/chip (QPS/chip) can be up to $2.2\times$ different with chiplet modeling compared to modeling with aggregated compute and memory resources. With this tool, we identify interdependencies between chiplet modeling and the rest of the system: we highlight an example where cost savings from prefetching weights from HBM to local SRAM need to be incorporated in the selection heuristic for chiplet sharding strategies. Using CMT, we explore performance sensitivity to chiplet properties in a design space exploration that highlights considerations for high-performance serving. We use an open-source MoE model (OSS-MoE) served on Ironwood-like TPU architectures as a case study. We conclude by discussing the need to establish best sharding practices to narrow this large design search space.
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Twenty years of Bigtable
Fabio Baltieri
Bora Beran
Igor Bernstein
Aimee Borda
Adrian Chan
Mark D'Andrea
Artak Dashyan
Ramesh Dharan
Gabor Dinnyes
Mike Dominguez
dorland .
Jose Duenas
Gary Elliott
Bruno Furtado
Madison Garcia
Marçal Garolera Huguet
Brendan Gleason
Alexis Hawkins
Anoshak Irani
Rohit Jog
Sudarshan Kadambi
Vikram Khemka
Sailesh Krishnamurthy
Maxim Krivokon
Bruce Lee
Tom Magrino
Matt Maly
Mark Mangrich
Douglas McErlean
Pablo Montes
Li Moore
Eduardo Morales
Greg Morris
Steve Niemitz
Gaurav Prabhu Gaonkar
Jim Rutherford
Stephen Ryan
Sho Saha
Kanoj Sarcar
Cristina Schmidt
Andrii Shyshkalov
Pratibha Suryadevara
Nick Suttle
Anvit Tawar
John Tobin
Justin Uang
Phaneendhar Vemuru
Harendra Verma
Shitanshu Verma
Jinghang (Frank) Wang
Michal Wegorek
Simon Yau
Andrius Ziukas
SIGMOD Companion '26: Companion of the International Conference on Management of Data, ACM (2026), pp. 188-200
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Bigtable is a pioneering and influential non-relational database system. The original Bigtable paper has been widely cited and it inspired and influenced many other systems such as HBase and Cassandra. Since then, Bigtable has continued to grow and has become one of the largest database systems inside Google. In this paper, we tell the journey of Bigtable inside Google for the last twenty years. We present new features added and improvements made to Bigtable, and we share our experience of running this storage system at scale, continually improving all aspects to accommodate the ever-growing demands of users.
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VISTA: A Test-Time Self-Improving Video Generation Agent
Xuan Long Do
Hootan Nakhost
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (to appear) (2026)
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Despite rapid advances in text-to-video (T2V) synthesis, generated video quality remains critically dependent on precise user prompts. Existing test-time optimization methods, successful in other domains, struggle with the multi-faceted nature of video. To address this, we introduce VISTA, a novel multi-agent system that autonomously refines prompts to improve video generation. VISTA operates in an iterative loop, first decomposing a user's idea into a structured temporal plan. After generation, the best video is identified through a robust pairwise tournament. This winning video is then critiqued by a trio of specialized agents focusing on visual, audio, and contextual fidelity. Finally, a reasoning agent synthesizes this feedback to introspectively rewrite and enhance the prompt for the next generation cycle. To rigorously evaluate our proposed approach, we introduce MovieGen-Bench, a new benchmark of diverse single- and multi-scene video generation tasks. Experiments show that while prior methods yield inconsistent gains, VISTA consistently improves video quality, achieving up to 60% pairwise win rate against state-of-the-art baselines. Human evaluators concur, preferring VISTA's outputs in 68% of comparisons.
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Performance and user response of Android's smartphone-based alerts in the 2025 Marmara Ereğlisi Earthquake
Patrick Robertson
Richard Allen
Alexei Barski
Robert Bosch
Nivetha Thiruverahan
Youngmin Cho
Tajinder Gadh
Steve Malkos
Boone Spooner
Greg Wimpey
Edward Shi
Marc Stogaitis
Nature Communications (2026)
Preview abstract
This study presents a comprehensive evaluation of Google’s Android Earthquake Alert (AEA) system during the Mw 6.2 Marmara Ereğlisi, Türkiye earthquake. AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users. Warning times for weak shaking (MMI III) reached up to 150 seconds, with a median of 56 seconds. While near-source warning windows were shorter, the system achieved 90% true positives and 99% precision overall. The high density of the phone network enabled faster detection than traditional stations, even for this offshore epicenter. Feedback data shows AEA recipients were highly likely to take protective actions, such as "drop, cover, and hold on," or warn others. Timely alerts substantially increased user engagement, perceived usefulness, and future trust. These results highlight how crowd-sourced technology and behavioral insights can effectively enhance seismic resilience on a massive scale.
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Chain-of-Thought (CoT) improves LLM reasoning but amplifies decoding latency and memory usage. While latent reasoning attempts to shift this computation to internal hidden states, it is hindered by the parallel nature of the Transformer prefill—which blocks sequential deep-to-shallow information flow—and the high training costs of recurrent optimization. We propose a supervised latent reasoning approach that enables efficient sequential computation during prefill. Our method augments an LLM with a recurrent pathway to propagate intermediate "thinking" tokens from deep to shallow layers without expanding the KV-cache. Unlike prior unsupervised methods, we utilize structured supervision to train this mechanism via teacher forcing, eliminating the need for Backpropagation Through Time (BPTT). Evaluations on state-tracking and reasoning benchmarks demonstrate that our approach outperforms existing latent baselines and approaches CoT performance, effectively combining the reasoning power of CoT with the inference efficiency of standard models.
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