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)
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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Optical health sensing algorithms, such as SpO2, sleep monitoring, and metabolic health sensing, critically depend on the accurate measurement of optical emission from Light Emitting Diodes (LEDs) transmitted through user tissue and detected by a photodiode (PD). A significant challenge to the reliability of these measurements is the inherent degradation of LED optical emission intensity over time due to device aging. This degradation can confound the physiological changes being monitored. Our work quantifies the impact of LED aging on sensor signal integrity, specifically examining the Current Transfer Ratio (CTR), which is a key metric defining the ratio of received photocurrent to the LED drive current used for transmission in various health sensing algorithms. We investigate the degradation characteristics across LEDs of different wavelengths. Our findings indicate a relative CTR change due to degradation ranging from 1% to 8% within 100 hours of continuous operation which translates to approximately 3.5 to 7 years of device lifetime. Furthermore, we explore the non-linearity of this degradation and the observed initial ”overshoot” phenomenon in the CTR during aging. We discuss how understanding these dynamics could inform the development of robust specifications for different physiological sensing algorithms. Finally, we present several potential solutions to mitigate the effects of LED aging. During the product design phase, integrating a calibrating photodiode or compensating circuitry around the LED can help preemptively address degradation. In the application space, run-time calibration strategies employing two differently degraded optical paths offer a promising approach to maintain measurement accuracy.
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Gaze Target Estimation Anywhere with Concepts
Xu Cao
Houze Yang
Vipin Gunda
Inki Kim
Jim Rehg
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2026)
Preview abstract
Estimating human gaze targets in-the-wild is a formidable challenge. Existing computer vision algorithms rely on brittle, multi-stage pipelines that require explicit inputs like head bounding boxes and human pose, causing initial detection errors to cascade and lead to system failure. To overcome this, we introduce the \textbf{Promptable Gaze Target Estimation (PGE)} task, a new end-to-end, concept-driven paradigm. PGE conditions gaze prediction on flexible user text or visual prompts (e.g., "the boy in the red shirt" or "person in point [0.52, 0.48]") to identify a specific subject's target, which eliminates the rigid dependency on intermediate localization cues. We develop a scalable data engine to generate \textbf{Gaze-Co}, a dataset and benchmark of 120K high-quality, prompt-annotated image pairs. We also propose \textbf{AnyGaze}, the first model designed for PGE. AnyGaze uses a Transformer-based detector to fuse features from frozen encoders and simultaneously solves subject localization, in/out-of-frame presence, and gaze target heatmap estimation. AnyGaze achieves state-of-the-art performance on standard gaze target estimation benchmarks, setting a strong baseline for this new problem even on a difficult out-of-domain, real-world clinical dataset. We will open-source the AnyGaze model and the Gaze-Co benchmark.
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Preview abstract
Online video platforms face an exponential challenge
in detecting and mitigating the flood of AI-generated “slop”
and synthetic spam perpetuated by coordinated malicious actors.
This content is increasingly designed to exploit the limitations
of traditional media forensics, often utilizing generative AI to
produce unique, localized variations of harmful or low-quality
material at scale. Traditional content-centric moderation fails
against this coordinated, adversarial generation strategy.
This paper presents a novel, scalable detection and classification framework designed for online video platforms (OVP) to
identify and triage clusters of coordinated accounts exhibiting
a prevalence of adversarial synthetic content. The approach
leverages a multi-faceted architecture incorporating two core
machine learning components: a robust Coordinated Bot-Net
Detector (via Account Relatedness) and a Synthetic Pattern Classifier. Crucially, we introduce an advanced AI enhancement layer
utilizing Large Language Models (LLMs), specialized via LowRank Adaptation (LoRA) and Automatic Prompt Optimization
(APO), to achieve rapid, high-precision semantic understanding
of emerging synthetic spam trends.
Evaluated across a representative evaluation dataset (N =
16, 250 weekly candidate channels across six major synthetic
abuse verticals), the system demonstrates high precision (FPR <
0.05%) in identifying coordinated synthetic spam networks.
Furthermore, the LLM-driven classification achieves a 74%
automated triage routing rate, saving over 1, 100 operational
review hours per week while reducing investigation turnaround
times by up to 50% (p < 0.001). This work details a critical
system design that provides essential scalability and adversarial
resilience against sophisticated generative attacks.
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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
Distributed federated SQL query engines frequently
materialize query results into files in data lakes. Optimizing the
sizes of these files (e.g., balancing their sizes) is crucial for the
efficiency of not only the materialization queries themselves, but
also subsequent queries that read these files. Existing techniques
to manage file size include specifying output targets (e.g., number
of partitions), using cardinality estimation for materialized data
volume, performing full shuffles prior to materialization to obtain
accurate statistics, and/or post (background) compactions to
merge small files. All of these solutions have limitations in
practice; they typically do not provide any strong guarantees or
can be prohibitively expensive when they do (e.g., they require
background compactions to merge small files, which requires
additional I/O and CPU cost). Most of the existing techniques
can produce many small files during materialization and are
sensitive to data skew.
In this paper, we propose a novel method that streamlines
materialization within the same query execution and provides
guarantees on the file sizes using statistics at runtime. Our
approach does not require a full shuffle before the materialization
operator in order to get accurate statistics, making it attractive
and robust in practice. To show the effectiveness of our approach,
we present production metrics from F1 Query at Google that has
been running this functionality for the majority of its production
workload over many quarters. Our approach reduces the number
of files produced by a factor of 100 or more, and as a result,
it avoids creating tens of billions of files per day, saving storage
and computation cost.
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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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Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT
Michelangelo Conserva
Alex Wilson
Anna Michalak
Philip Brodrick
Andrew Thorpe
Proceedings of the National Academy of Sciences (PNAS) (2026)
Preview abstract
Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system inefficiencies. Global tracking with imaging spectroscopy is just becoming feasible, but still largely relies on manual tracking. Here we present the Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) model, an end-to-end vision transformer framework that advances the state of the practice by directly utilizing the complete radiance spectrum from the Earth Surface Mineral Dust Source Investigation (EMIT) instrument to jointly retrieve methane enhancements across all pixels within a scene. This approach joins spectral information content - where the methane signature resides - with essential spatial context to significantly lower detection limits. MAPL-EMIT simultaneously supports quantification, plume delineation, and source localization, even for multiple overlapping plumes. The model was trained on 3.6 million physics-based synthetic plumes injected into global EMIT radiance data. Evaluation against synthetic observations confirms the model’s ability to identify plumes with high recall and precision and to capture weaker plumes relative to existing matched-filter approaches. On real-world benchmarks, MAPL-EMIT captures 79% of known hand-annotated NASA L2B plume complexes across a test set of 1084 EMIT granules, while identifying twice as many plausible plumes than identified by human analysts. Further validation against coincident airborne data, top-emitting landfills, and controlled release experiments confirms the models efficacy at identifying previously uncaptured sources. By incorporating model-generated metrics such as spectral fit scores and estimated noise levels, the framework can further limit false-positive rates. Overall, MAPL-EMIT enables high-throughput implementation on the full EMIT data catalog, shifting methane monitoring from labor-intensive workflows to a rapid, scalable paradigm for facility-level accountability.
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This framework manages AI agents by establishing behavioral boundaries and a persistent identity. It uses a multi-layered stack, combining safety rules with brand guidelines, to shape an agent's reasoning. Features include authority decay to limit power if confidence drops and memory segmentation to prevent data tampering. Centralized oversight ensures these digital representatives remain aligned with company policies through continuous monitoring and testing.
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Neural general circulation models for modeling precipitation
Stephan Hoyer
Dmitrii Kochkov
Janni Yuval
Ian Langmore
Science Advances (2026)
Preview abstract
Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. While hybrid models combining machine learning and physics have emerged with the premise of improving precipitation simulations, none have proven sufficiently skillful or stable enough to outperform existing models in simulating precipitation.
Here, we present the first hybrid model that is trained directly on precipitation observations. The model runs at 2.8 degrees resolution and is built on the differentiable NeuralGCM framework. This model is stable for decadal simulations and demonstrates significant improvements over existing GCMs, ERA5 reanalysis, and a Global Cloud-Resolving Model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle. Furthermore, it outperforms the ECMWF ensemble for mid-range weather forecasting.
This advance paves the way for more reliable simulations of current climate and for the ability to fully utilize the abundance of existing observations to further improve GCMs.
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Enterprise service centers, particularly in domains like People Operations, are critical hubs of organizational knowledge work. They face a persistent difficulty in disseminating the tacit, case-specific expertise of senior agents, which can lead to inconsistent service and slower onboarding for new hires. While existing Knowledge Management (KM) and Case-Based Reasoning (CBR) systems have improved the retrieval of historically similar cases, they inadvertently shift the cognitive burden of synthesizing this information to the time-constrained agent. This paper introduces the Dynamic Case Precedent (DCP) architecture, a novel socio-technical framework designed to address this gap. The DCP architecture moves beyond simple precedent recommendation to automated precedent synthesis. It achieves this by integrating a semantic retrieval model with the large-context reasoning capabilities of a generative Large Language Model (LLM). We propose a three-pillar framework—(1) Contextual Similarity Indexing, (2) Generative Insight Synthesis, and (3) Human-in-the-Loop Refinement. By analyzing multiple relevant historical cases to generate a concise summary of resolution patterns, the DCP architecture aims to reduce agent cognitive load, accelerate proficiency, and improve service consistency. This conceptual framework offers a new model for human-AI collaboration, framing the AI not as a mere information tool, but as an active partner in sensemaking.
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Securing Elliptic Curve Cryptocurrencies against Quantum Vulnerabilities: Resource Estimates and Mitigations
Michael Broughton
Thiago Bergamaschi
Justin Drake
Dan Boneh
arXiv:2603.28846 (2026)
Preview abstract
This whitepaper seeks to elucidate implications that the capabilities of developing quantum architectures have on blockchain vulnerabilities and mitigation strategies. First, we provide new resource estimates for breaking the 256-bit Elliptic Curve Discrete Logarithm Problem, the core of modern blockchain cryptography. We demonstrate that Shor's algorithm for this problem can execute with either <1200 logical qubits and <90 million Toffoli gates or <1450 logical qubits and <70 million Toffoli gates. In the interest of responsible disclosure, we use a zero-knowledge proof to validate these results without disclosing attack vectors. On superconducting architectures with 1e-3 physical error rates and planar connectivity, those circuits can execute in minutes using fewer than half a million physical qubits. We introduce a critical distinction between fast-clock (such as superconducting and photonic) and slow-clock (such as neutral atom and ion trap) architectures. Our analysis reveals that the first fast-clock CRQCs would enable on-spend attacks on public mempool transactions of some cryptocurrencies. We survey major cryptocurrency vulnerabilities through this lens, identifying systemic risks associated with advanced features in some blockchains such as smart contracts, Proof-of-Stake consensus, and Data Availability Sampling, as well as the enduring concern of abandoned assets. We argue that technical solutions would benefit from accompanying public policy and discuss various frameworks of digital salvage to regulate the recovery or destruction of dormant assets while preventing adversarial seizure. We also discuss implications for other digital assets and tokenization as well as challenges and successful examples of the ongoing transition to Post-Quantum Cryptography (PQC). Finally, we urge all vulnerable cryptocurrency communities to join the ongoing migration to PQC without delay.
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TDXRay: Microarchitectural Side-Channel Analysis of Intel TDX for Real-World Workloads
Tristan Hornetz
Hosein Yavarzadeh
Albert Cheu
Adria Gascon
Lukas Gerlach
Michael Schwarz
Ruiyi Zhang
IEEE Security & Privacy (S&P) (2026)
Preview abstract
Confidential computing with VM-based trusted execution environments (TEEs) promises to protect code and data from a privileged cloud operator, enabling privacy-preserving workloads ranging from medical analytics to AI inference. However, most deployments exclude microarchitectural side channels from their threat model, shifting the burden to application developers who lack practical, general-purpose tools to assess (let alone mitigate) leakage. This gap is problematic: host-observable effects such as page-fault patterns, shared-cache contention, performance-counter surrogates (where available), and fine-grained timing primitives (e.g., MWAIT) can still reveal high-level secrets even when memory remains encrypted.
We present TDXRay, an open-source framework that systematizes the evaluation of side-channel risk for confidential VMs in Intel TDX. TDXRay exposes unified interfaces to exercise and measure several attack primitives—including controlled-channel attacks via page tables, cache-based contention/occupancy probes, performance-counter–derived signals, and timing channels—against unmodified guest workloads. Using TDXRay, we build two end-to-end case studies: (1) a classic AES T-table attack in which a malicious hypervisor recovers the secret key from access-pattern leakage, and (2) an LLaMA inference attack in which the host infers user prompts by monitoring memory accesses during tokenization and embedding lookups. Across both, we show that a host with no direct access to guest memory can reconstruct sensitive information by observing only externalized microarchitectural signals.
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Source-to-source compilers may perform inefficiently by executing transpilation passes on scripts that do not contain the specific language features a pass is designed to transform, potentially leading to redundant processing. A compiler can analyze a script to generate a per-script feature map, for example, by identifying language features in its abstract syntax tree (AST). Before executing a transpilation pass, the compiler can check this map and may bypass the pass for that script if the specific feature targeted by the pass is not present. This feature map can also be dynamically updated throughout the compilation process as other passes transform the code. This method of conditional pass execution based on content-aware analysis may reduce redundant AST traversals, which could decrease overall compilation time and computational resource consumption.
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