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.
Sort By
1 - 15 of 11635 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)
Preview abstract
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.
View details
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.
View details
Beyond Tsybakov: Model Margin Noise and H-Consistency Bounds
The Nineteenth International Symposium on Artificial Intelligence and Mathematics (ISAIM 2026)
Preview abstract
We introduce a new low-noise condition for classification, the *Model Margin Noise (MM noise)* assumption, and derive enhanced $H$-consistency bounds under this condition. MM noise is *weaker* than Tsybakov noise condition: it is implied by Tsybakov noise condition but can hold even when Tsybakov fails, because it depends on the discrepancy between a given hypothesis and the Bayes-classifier rather than on the intrinsic distributional minimal margin (see Figure 1 for an illustration of an explicit example). This hypothesis-dependent assumption yields enhanced $H$-consistency bounds for both binary and multi-class classification. Our results extend the enhanced $H$-consistency bounds of Mao, Mohri, and Zhong (2025a) with the same favorable exponents but under a weaker assumption than the Tsybakov noise condition; they interpolate smoothly between linear and square-root regimes for intermediate noise levels. We also instantiate these bounds for common surrogate loss families and provide illustrative tables.
View details
Agentic AI Infrastructure in Practice: Learn These Key Hurdles to Deploy Production AI Agents Efficiently
https://swisscognitive.ch/ (2026)
Preview abstract
The emergence of Agentic AI—autonomous systems capable of reasoning, decision-making, and multi-step execution—represents a paradigm shift in enterprise technology. Moving beyond simple generative tasks, these agents offer the potential to solve long-standing industry pain points, with over 90% of enterprises planning integration within the next three years. However, the transition from successful proof-of-concept (PoC) to a resilient, production-grade system presents significant hurdles.
This article categorizes these challenges into three primary domains:
Technical and Engineering Hurdles: Issues such as "entangled workflows" that complicate debugging, the struggle to maintain output quality and mitigate hallucinations, and the unpredictability caused by shifting underlying models or data sources.
People, Process, and Ecosystem Hurdles: The high operational costs and unclear ROI of large models, the necessity of a new "Agent Ops" skillset, the complexity of integrating agents with disparate enterprise systems, and a rapidly evolving regulatory landscape.
The Pace of Change and Security risks: The technical debt incurred by shifting software frameworks and the expanded attack surface created by autonomous agents.
The article concludes that successful deployment requires a shift from informal "vibe-testing" to rigorous engineering discipline. By adopting code-first frameworks, establishing robust evaluation metrics (KPIs), and prioritizing functional deployment over theoretical optimization, organizations can effectively manage the lifecycle of Agentic AI and realize its transformative business value.
View details
Protocol-Sensitive Evaluation of Log Anomaly Detection: Component Costs and Target-Access Sensitivity on HDFS and BGL
Hang Xiao
Janet Sung
Zhaoyi Li
Iris Qian
Chuhong Xu
2026
Preview abstract
Published results for log anomaly detection often
depend on how the data are split and which preprocessing
artifacts are available at test time. We examine this depen-
dence by running six pipelines on the Hadoop Distributed
File System (HDFS) and Blue Gene/L (BGL) logs under
several split, parser, temporal, and target-access settings.
The pipelines include three count classifiers, a compact
sequence model, and two classifiers that share a semantic
representation. We also measure parsing, representation
fitting, classifier training, artifact size, and classifier-only
inference as separate components. Random splitting gives
near-ceiling scores on both datasets. When HDFS count-
vector groups are held out intact, or when BGL is split
at a chronological cutoff, the scores fall and the model
ordering changes. In cross-system evaluation, giving the
semantic representation access to an unlabeled target cat-
alog raises XGBoost mean average precision from 0.191
to 0.325 for HDFS-to-BGL and from 0.089 to 0.142 for
BGL-to-HDFS. At a five-percent review budget, the corre-
sponding mean precision and recall are 0.318 and 0.157 in
the first direction and 0.129 and 0.221 in the second. The
study quantifies how protocol and cost boundaries shape
conclusions drawn from these two benchmarks. Code,
split indices, predictions, sensitivity runs, and regenera-
tion checks accompany the submission.
View details
`It’s still abuse’: Community attitudes and perceptions on AI-generated image-based sexual abuse
Nicola Henry
Gemma Beard
Lisa Given
Information, Communication, & Society (2026)
Preview abstract
There are growing concerns about AI-generated image-based sexual abuse (AI-IBSA), also known as nonconsensual sexualized ′deepfakes.′ Empirical research on AI-IBSA, however, remains very limited. This study surveyed 7231 respondents across Australia, the United Kingdom, and the United States to investigate community attitudes and perceptions on AI-IBSA. Through a vignette study, we explored the relationship between public familiarity with AI-IBSA, normative concerns about consent, and context-dependent judgments that vary based on the target's identity relational status, and how the content was used. Our findings reveal strong condemnation of AI-IBSA, yet respondents demonstrated low familiarity with the technology and their views varied depending on particular contexts. AI-IBSA targeting intimate partners was viewed as more unacceptable than targeting celebrities, and content created solely for personal use was seen as less unacceptable than content intended for distribution. The study highlights the need for approaches that go beyond technical fixes and punitive measures, advocating for a multifaceted response that integrates ethical data governance, digital sexual literacy, and restorative justice approaches.
View details
Preview abstract
Latent Diffusion Models (LDM) face three compounding efficiency challenges in practical deployment: i) the temporal latency of iterative sampling; ii) the architectural overhead of heavy backbone parameter counts; and iii) the spatial cost of high-dimensional latent grids. While recent acceleration methods have made substantial progress on temporal distillation and architectural compression, the spatial axis is often inherited from the teacher tokenizer and treated as fixed. In this work, we recast latent resolution as an optimizable efficiency axis and introduce a unified framework that optimizes all three dimensions simultaneously. We introduce a novel strategy of Score-Compatible Tokenizer Distillation (SCTD), which leverages score-matching principles to align a spatially compact latent space with the induced distribution of a frozen, powerful teacher model, distilling the teacher's generative prior into a compressed, lower-dimensional compatible manifold. With flexibility provided by SCTD, we can surrogate a computationally heavy teacher backbone with a lightweight student architecture operating strictly within this new compressed space. Finally, we apply temporal distillation to collapse the sampling trajectory, producing a one-step generator that operates at peak efficiency. Our method yields a student generator outperforming existing single-axis acceleration methods in efficiency and throughput, while maintaining competitive generation quality. With reduced peak memory usage and latency, our method enables resource-constrained deployment and high-volume serving of high-fidelity LDM.
View details
GroundSet: A Cadastral-Grounded Dataset for Spatial Understanding with Vector Data
Roger Ferrod
Maël Lecene
Krishna Sapkota
Vered Silverman
Sylvain Lobry
Computer Vision -- ECCV 2026, Springer, Cham, pp. 93-108
Preview abstract
Precise spatial understanding in Earth Observation is essential for translating raw aerial imagery into actionable insights for critical applications like urban planning, environmental monitoring and disaster management. However, Multimodal Large Language Models exhibit critical deficiencies in fine-grained spatial understanding within Remote Sensing, primarily due to a reliance on limited or repurposed legacy datasets.
To bridge this gap, we introduce a large-scale dataset grounded in verifiable cadastral vector data, comprising 3.8 million annotated objects across 510k high-resolution images with 135 granular semantic categories. We validate this resource through a comprehensive instruction-tuning benchmark spanning seven spatial grounding tasks.
Our evaluation establishes a robust baseline using a standard LLaVA architecture. We show that while current RS-specialized and commercial models (e.g., Gemini) struggle in zero-shot settings, high-fidelity supervision effectively bridges this gap, enabling standard architectures to master fine-grained spatial grounding without complex architectural modifications.
Data, pretrained model and code are available at: https://huggingface.co/datasets/RogerFerrod/GroundSet
View details
Mull-Tokens: Modality-Agnostic Latent Thinking
Arijit Ray
Chengzhi Mao
Bryan A. Plummer
Kate Saenko
Ranjay Krishna
Leonidas Guibas
Vincent Chu
IEEE/CVF Conference on Computer Vision and Pattern Recognition (Findings) (2026) (to appear)
Preview abstract
Reasoning goes beyond language; the real world requires reasoning about space, time, affordances, and much more that words alone cannot convey. Existing multimodal models exploring the potential of reasoning with images are brittle and do not scale. They rely on calling specialist tools, costly generation of images, or handcrafted reasoning data to switch between text and image thoughts. Instead, we offer a simpler alternative -- Mull-Tokens -- modality-agnostic latent tokens pre-trained to hold intermediate information in either image or text modalities to let the model think free-form towards the correct answer. We investigate best practices to train Mull-Tokens inspired by latent reasoning frameworks. We first train Mull-Tokens using supervision from interleaved text-image traces, and then fine-tune without any supervision by only using the final answers. Across four challenging spatial reasoning benchmarks involving tasks such as solving puzzles and taking different perspectives, we demonstrate that Mull-Tokens improve upon several baselines utilizing text-only reasoning or interleaved image-text reasoning, achieving a +3% average improvement and up to +16% on a puzzle solving reasoning-heavy split compared to our strongest baseline. Adding to conversations around challenges in grounding textual and visual reasoning, Mull-Tokens offers a simple solution to abstractly think in multiple modalities.
View details
Meta-Initialized Warm Pool Manager: Predictive Cold Start Mitigation for Serverless Functions via Adaptive Meta-Learning
Praveen Baskar
Om Narayan
Ramkinker Singh
2026
Preview abstract
Serverless computing platforms commonly suffer
from cold-start latency because of Cold-start problems dynamic
invocation patterns, but with current warm pool management
strategies face difficulty in effectively adjusting to changes in
workload. We propose the Meta-Initialized Warm Pool Manager
(MI-WPM), a new component which combines meta-learning and
adaptive control to address the problem of cold start efficiently.
The innovation is based on a meta predictor trained on diverse
invocation scenarios, which offers a generalized Initialization for
rapid fine-tuning based on detection of workload drift. Contrary
to traditional schemes based upon static heuristics or expensive
“retraining, MI-WPM adapts its warm pool sizes by combining
“ meta-initialized predictions with lightweight statistical drift
detection. The architecture uses a temporal fusion transformer
for modeling invocation patterns, Kolmogorov-Smirnov test for
monitoring drift, and feedback-controlled controller for opti-
mizing resource distribution. The experimental results show the
reduction in the Cold Start problem using MI-WPM. occurring
by 47% compared to state-of-the-art approaches while keeping
provisioning costs below 5%. Moreover, the Meta learning frame-
work supports sub-minute adaptation on unknown invocation
patterns, filling a gap in serverless computing. The proposed
approach is developed as a modular extension of the existing
serverless architectures without any infrastructure modifications.
This work fills the gap between offline-trained models and
reactive scaling, providing a principled approach to cold start
mitigation in production environments.
View details
CAST: Modeling Visual State Transitions for Consistent Video Retrieval
Yanqing Liu
Yingcheng Liu
Fanghong Dong
Budianto Budianto
Cihang Xie
Yan Jiao
2026
Preview abstract
As video content creation shifts towards long-form narratives, retrieving and composing short clips into coherent storylines becomes a critical challenge.
Standard retrieval formulations, however, perform context-agnostic retrieval, prioritizing local semantic alignment while neglecting procedural state and identity consistency across the narrative flow.
To address this, we introduce the task of Consistent Video Retrieval (CVR) and establish a benchmark designed to diagnose such inconsistencies via semantic hard negatives.
We propose CAST (Context-Aware State Transition), a lightweight adapter that models procedural progression as state-conditioned transitions.
Conditioned on visual history, CAST predicts a gated residual vector ($\Delta$) to selectively update the state embedding, ensuring procedural coherence while preserving identity.
Extensive experiments demonstrate that CAST significantly outperforms standard retrieval baselines on our CVR benchmark.
Furthermore, we show its potential as a plug-and-play consistency verifier, guiding black-box generation models (e.g., Veo) toward coherent video continuations within long-form narratives.
View details
Preview abstract
Object-Counting for remote-sensing (RS) imagery is raising increasing research interest due to its crucial role in a wide and diverse set of applications. While several promising methods for RS object-counting have been proposed, existing methods focus on a closed, pre-defined set of object classes. This limitation necessitates costly re-annotation and model re-training to adapt current approaches for counting of novel objects that have not been seen during training, and severely inhibits their application in dynamic, real-world monitoring scenarios.
To address this gap, in this work we propose RS-OVC - an adaptation of existing work for Open Vocabulary Counting (OVC) approach from general computer vision to the RS domain. We show that our model is capable of accurate counting of novel object classes, that are unseen during training, based solely on textual and/or visual conditioning.
View details
Hybrid Consensus with Quantum Sybil Resistance
Dar Gilboa
Sid Jain
Or Sattath
2026
Preview abstract
Sybil resistance is a key requirement of decentralized consensus protocols. It is achieved by introducing a scarce resource (such as computational power, monetary stake, disk space, etc.), which prevents participants from costlessly creating multiple fake identities and hijacking the protocol. Quantum states are generically uncloneable, which suggests that they may serve naturally as an unconditionally scarce resource. In particular, uncloneability underlies quantum position based-cryptography, which is unachievable classically. We design a consensus protocol that combines classical hybrid consensus protocols with quantum position verification as the Sybil resistance mechanism, providing security in the standard model, and achieving improved energy efficiency compared to hybrid protocols based on Proof-of-Work. Our protocol inherits the benefits of other hybrid protocols, namely the faster confirmation times compared to pure Proof-of-Work protocols, and resilience against the compounding wealth issue that plagues protocols based on Proof-of-Stake Sybil resistance. We additionally propose a spam prevention mechanism for our protocol in the Random Oracle model.
View details
Preview abstract
Advancements in large language models (LLMs) have made them increasingly useful for complex reasoning tasks which previously required domain experts. One such task is quality evaluation of query responses produced by a search engine. Evaluation generates metrics necessary to study the quality, impact, and usefulness of product changes and features. Typically, to compute evaluation metrics, human experts are asked to rate various attributes of search responses. This process is generally quite expensive and requires several days to complete. As an alternative, LLMs are now being used to perform rating tasks with lower costs and latency. In addition, many new metrics are being developed to evaluate Google's new AI-based offerings, which require ratings too. As a result, there is much higher demand for LLM rating prediction tasks in comparison with the allocated TPU (Tensor Processing Unit) budget. A larger portion of the company's TPU resources are reserved for serving live user traffic. In this paper, we present the AI Rater Service (AIRS), an inference pipeline that employs several software engineering techniques to generate AI ratings with high reliability and low latency. AIRS maximizes LLM inference throughput by optimizing TPU resource utilization across various evaluation workflows, while minimizing latency for higher priority tasks.
View details
