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 11644 publications
Preview abstract
Modern transformers have been scaled up in size, data, training-time compute, and more recently test-time compute based on Chain-of-Thought (CoT), but the consequences of trading off these parameters are unknown. We construct explicit tradeoffs between two of these aspects: the number of layers in a transformer and the number of CoT tokens. Our first result is general: with mild assumptions and a single-token prompt, for any integer k, a transformer using soft attention and SwiGLU MLP layers with L layers and n CoT tokens can be converted to a transformer with at most ckL layers and ⌈n/k⌉ CoT tokens, and vice versa, although with an increase to the size of other parameters. We then a multi-token prompt path following task, finding that additional preprocessing from the multi-token prompt changes the depth-CoT relationship in this case. We construct an attention-only network with L layers and 2^(L−2) heads that can traverse 2^(L−1) − 1 edges along a path with a single CoT token, demonstrating at least an exponential relationship between increases in depth and reductions in the number of CoT tokens needed to traverse the entire path. This relationship for the path following task holds empirically as well, as demonstrated by training networks with up to 5 layers, with the tradeoff between depth and the maximum number of edge traversals in a single CoT token exactly matching our theoretical constructions.
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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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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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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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A common problem in private data analysis is the partition selection problem, where each user holds a set of partitions (e.g. keys in a GROUP BY operation) from a possibly unbounded set. The challenge here is in maximizing the set of released partitions while respecting a differential privacy constraint. Previous work [DVGM21] presented an optimal (ε, δ)-DP algorithm when each user submits only a single partition. We generalize this approach to find the optimal algorithm under δ-approximate (α, ε)-R´enyi differential privacy (RDP), which allows much tighter analysis under composition. Motivated by the non-existence of a general optimality result in the case where users submit multiple partitions each, we present a simple extension of our optimal algorithm tuned for L2 bounded weighted partition selection which can be used as a drop-in improvement over the Gaussian mechanism any time the partition frequency is not also needed. We show that our primitive can be easily plugged into state of the art partition selection algorithms (PolicyGaussian from [GGK+20] and MAD2R from [CCAEZ25]), improving performance both for parallel and sequential algorithms. Finally, we show that there is an inherent cost to algorithm which do support releasing the frequency as well as the partitions. Specifically, we formulate a basic notion of optimal approximate RDP algorithm for partition selection using additive noise, and show that there is a numerical separation between additive and non-additive noise mechanisms for this problem.
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Preview abstract
Systems for escalating interactions from automated agents to human agents can create inefficiencies, for example, by transferring unstructured transcripts. An intermediary system can employ a generative artificial intelligence synthesis engine to process the context of an automated interaction upon an escalation trigger. The engine may analyze the dialogue transcript, user metadata, and the automated agent's internal state to perform semantic abstraction, diagnose potential failure points, and infer a possible resolution. The system can then generate a structured briefing for the human agent, which could include a concise summary, a failure diagnosis, or a recommended next action presented as an interactive element. This process may facilitate a more efficient handoff and contribute to an improved escalation workflow by providing the human agent with synthesized, contextual information.
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Efficacy of Scalable Airline-led Contrail Avoidance
Thomas Dean
Tristan Abbott
Jill Blickstein
Alejandra Martín Frías
Mark Galyen
Rebecca Grenham
Paul Hodgson
Alan Pechman
Tyler Robarge
Dinesh Sanekommu
Aarón Sonabend
Marc E.J. Stettler
Raimund Zopp
Journal of Environmentally Compatible Air Transport System (JECATS) (2026) (to appear)
Preview abstract
Contrails account for a large portion of aviation's contribution to anthropogenic climate change. Navigational contrail avoidance is a promising solution to mitigate the warming caused by contrails. Prior trials testing navigational contrail avoidance have relied on bespoke integrations of contrail forecasts into airline operations. Here, we use a randomized control trial to test the feasibility of dispatcher-led contrail avoidance integrated into standard flight planning operations using a workflow which scales to an airline's entire network. Using satellite imagery and an automated flight-contrail attribution algorithm, we observed an 11.6% reduction in contrail formation rate for the 1232 flights marked as eligible for contrail avoidance (intent-to-treat) relative to the flights in the control group (p = 0.0109). In the 112 flights which flew contrail avoidance as planned (per-protocol flights), we observed a 62.0% lower contrail formation rate relative to the flights in the control group (p < 0.001). No statistically significant difference in fuel usage was observed between the two groups.
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How Tech Workers Contend with Hazards of Humanlikeness in Generative AI
Eric Corbett
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, ACM (2026), pp. 1-18
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Generative AI’s humanlike qualities are driving its rapid adoption in professional domains. However, this anthropomorphic appeal raises concerns from HCI and responsible AI scholars about potential hazards and harms, such as overtrust in system outputs. To investigate how technology workers navigate these humanlike qualities and anticipate emergent harms, we conducted focus groups with 30 professionals across six job functions (ML engineering, product policy, UX research and design, product management, technology writing, and communications). Our findings reveal an unsettled knowledge environment surrounding humanlike generative AI, where workers’ varying perspectives illuminate a range of potential risks for individuals, knowledge work fields, and society. We argue that workers require comprehensive support, including clearer conceptions of “humanlikeness” to effectively mitigate these risks. To aid in mitigation strategies, we provide a conceptual map articulating the identified hazards and their connection to conflated notions of “humanlikeness.”
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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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Claim-Gated Source-Risk Auditing for Generative Search
Mark Zhou
Chuhong Xu
Iris Qian
Zhaoyi Li
2026
Preview abstract
A generative search answer can cite a supported
passage yet omit a source relationship that changes its interpretation. We specify a claim-gated audit of the query–source–
answer tuple. An omission is resolved only when relationship
evidence, answer adoption, materiality, and disclosure are all
observed; incomplete evidence remains unresolved rather than
being treated as independence. The specification separates this
endpoint from citation support and review priority, and binds
decisions to versioned evidence spans. A reference checker makes
the record contract executable. On an exhaustive synthetic
suite, it reproduces all 81 three-state predicate combinations
and rejects 192 deliberately malformed records. Common-guard
baselines and predicate ablations isolate endpoint logic from
missing-evidence handling, while controlled transitions check
support separation and evidence removal. These are finite
contract-conformance results, not detector accuracy or evidence
of improved user outcomes. We define the independent annotation,
held-out evaluation, and paired utility tests still required to
establish semantic validity and deployment benefit.
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Hybrid Reputation Routing for Decentralized Networks: A Blockchain-Enabled Secure and Scalable Protocol with On-Chain/Off-Chain Partitioning
Praveen Baskar
Om Narayan
Ramkinker Singh
2026
Preview abstract
Our suggestion is a hybrid reputation-based routing
protocol in the context of decentralized routing. networks which
partitions trust measurements to on-chain immutable logs and
off-chain dynamic computations to attain security and scalability.
The protocol incorporates a layer of sharded blockchain to store
critical. trust data and local reputation model to do real-time
updates, enabling routing choices that cannot be tampered with.
performance. The trust ledger which operated on-chain kept
the records of the impartiality of. Historical performance and
node identities, whereas the off-chain reputation engine uses
graph neural network to calculate dynamic. real-time scores like
packet delivery rate to be trusted. and latency. Additionally, it
uses rollup-based batches of synchronization off-chain. optimistic
updates to zero-knowledge proofs, making on-chain efficient.
checking with minimum overhead. The suggested approach
replaces conventional network discovery and forwarding modules
using trust-based adjacency lists and trust-based pathing, and
thus enhancing routing consistency over adversarial settings.
Moreover, the structure integrates Hyperledger Fabric with.
Graph Attention Network-based high-throughput sharded ledger
operations. to update reputation in a privacy-preserving manner,
proving to be linear. network size scalability. The experimental
findings indicate that the system supports 10,000 transactions per
shard and produces. Under 100 ms per ZK-Rollup proof, which
is appropriate to large-scale IoT. and DeFi deployments. This
publication fills the gap between pure on-chain. and off-chain
reputation systems, which provides a viable solution to scalable
and secure decentralized routing.
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Preview abstract
High-fidelity removal of eyeglasses from video is a major challenge in facial attribute editing, as the underlying facial geometry is often obscured by complex refractive distortions and view-dependent specular reflections. While large-scale generative priors have shown promise in static image inpainting, they often lack the structural constraints necessary to maintain identity, expression, and pose, leading to visible “identity drift” in both static images and dynamic sequences. In this paper, we propose a novel distillation framework that addresses the stochastic nature of generative priors. Our pipeline first extracts high-fidelity outputs from Gemini, regularizes them via dense geometric landmark constraints to preserve identity, expression, and pose, and finally applies physically-based simulation of lens optics to generate realistic paired training data. This process transfers Gemini’s photo-realistic, multi-view knowledge into a specialized restoration architecture, JFSnet (Joint Feature-Spatial network). JFSnet integrates DINOv2-based semantic features with a convolutional decoder for spatial reconstruction, leveraging equivariance constraints to improve high-frequency detail preservation and stability. Evaluations show that our approach significantly outperforms existing diffusion and GAN-based baselines, achieving the lowest FID scores and ranking highest in user preference studies across visual fidelity, identity preservation, and temporal consistency.
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DAC: Hierarchical Functional Fault Grading with Auto-Identification of Observation Points for Enhanced DFT Coverage in Complex SoC Designs
Kasi Chunduri
Rajesh Gottumukkala
Karthikeyan Sundaramahalingam
Veerabhadra Rao Vasa
2026
Preview abstract
As semiconductor designs increase in complexity, achieving near-zero Defective Parts Per Million (DPPM) has become a critical challenge that traditional scan-based Design-for-Test (DFT) methods often difficult to meet. Conventional structural tests often leave quality gaps in custom circuits and hard-to-detect logic, leading to costly field returns (RMA). While functional verification patterns can bridge these coverage gaps, their integration is typically hindered by unoptimized stimuli, redundant simulations, and extreme memory demands that can make "flat" SoC fault grading unfeasible.
This paper presents a transformative hierarchical fault grading methodology that overcomes these bottlenecks through two core innovations: automated identification of optimal observation points and hierarchical partitioning for parallel simulation. Our approach utilizes a proprietary algorithm to automatically pinpoint internal nodes that maximize fault detection, significantly reducing the manual effort required for final coverage closure. By breaking complex SoCs into modular sub-scopes (chiplets), our framework enables parallelized fault grading that slashes RAM requirements—reducing them from over higher compute in traditional runs to standard server levels—and converts week-long simulation cycles into a predictable workflows.
Experimental results demonstrate the high scalability of this approach: where simulating just four faults at the SoC level was previously memory-prohibitive, our hierarchical method successfully processed 10,000 faults with consistent accuracy and a multi-fold reduction in total runtime. By leveraging functional test suites to detect structural defects that traditional DFT misses, this framework ensures earlier coverage closure and eliminates late-cycle engineering changes, ultimately accelerating time-to-market for high-quality, complex SoC designs.
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Feed-forward foundation models for multi-view 3-dimensional(3D) reconstruction have been trained on large-scale datasets of perspective images; when tested on wide field-of-view images, e.g., from a fisheye camera, their performance degrades. This degradation arises from changes in spatial arrangements of pixels induced by the non-linear projection model that maps 3D points onto the 2D image plane. While one may surmise that training on fisheye images would resolve this problem, there are far fewer fisheye images with ground truth than perspective images, which limits generalization. To enable inference on imagery exhibiting high radial distortion, we propose Fisheye3R, a novel adaptation framework that extends these multi-view 3D reconstruction foundation models to natively accommodate fisheye inputs without performance regression on perspective images. To address the scarcity of fisheye images and ground truth, we introduce flexible learning schemes that support self-supervised adaptation using only unlabeled perspective images and supervised adaptation without any fisheye training data. Extensive experiments across three foundation models, including VGGT, π3 , and MapAnything, demonstrate that our approach consistently improves camera pose, depth, point map, and field-of-view estimation on fisheye images.Code is available at https://github.com/android-xr/fisheye3r.
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We propose a benchmark to evaluate the extent to which LLMs can uncover hidden environments through interaction. In our setup, an agent should uncover a hidden deterministic finite automaton (DFA) by interacting with an oracle through (1) membership queries (``Does this string belong to the target language?'') and (2) equivalence queries (``Is this the target DFA?''). This yields a scalable testbed with controlled task complexity, measurable interaction efficiency, and strong baselines (classic automata-learning algorithms). Evaluating state-of-the-art LLMs, we find that performance drops sharply as DFA size increases. Reasoning models are markedly stronger than non-reasoning models, yet trajectory analyses reveal recurring failures in query planning, evidence integration, and hypothesis construction. Overall, our results show that current LLMs can sometimes perform non-trivial interactive discovery, but remain far less robust and efficient than classic algorithms for the task.
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