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)
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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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Quantization methods have significantly improved the compute and memory efficiency of Large Language Model (LLM) training. However, existing approaches still rely on accumulating their updates into high precision: concretely, gradient updates must be applied to a high-precision weight buffer, known as \textit{master weights}. This buffer introduces substantial memory overhead, particularly for Sparse Mixture of Experts (SMoE) models, where model parameters and optimizer states dominate memory usage. In this work, we introduce the Error-Compensating Optimizer (ECO), which \textit{for the first time} enables the complete elimination of master weights by directly accumulating updates into quantized parameters, by leveraging existing optimizer states. ECO quantizes the weights after every gradient step and injects the resulting quantization error into the optimizer's momentum buffer, creating an error-feedback loop with zero additional memory overhead for quantization. Beyond its practical efficiency, ECO comes with theoretical guarantees. Specifically, under standard assumptions, naive master weight removal can lead to unbounded drift from the ideal parameter trajectory, whereas ECO provably bounds this drift, ensuring stable convergence. We validate ECO across a range of models, including small transformers (30M--800M), Gemma-3 1B, and an SMoE 2.1B model, using FP8 quantization. In all cases, ECO achieves near-lossless accuracy compared to high-precision baselines. For large SMoE models, ECO reduces memory usage by up to 25\%, establishing a new Pareto frontier for the trade-off between static memory and training loss.
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Silicon-Level Sovereignty: Root of Trust in AI Accelerators (Digital Trust & Policy)
https://www.dotmagazine.online (2026)
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As artificial intelligence (AI) transitions from experimental pilot programs to mission-critical enterprise operations, traditional software-based security frameworks are proving insufficient against sophisticated infrastructure-level threats. This article introduces the concept of Silicon-Level Sovereignty, a first-principles approach to digital trust that anchors security in the physical hardware rather than the software stack.
We examine the technical architecture of Hardware Root of Trust (RoT), specifically focusing on the roles of Trusted Platform Modules (TPMs) and Secure Enclaves in modern AI accelerators such as GPUs and TPUs. By leveraging cryptographic remote attestation, organizations can move from a model of assumed software integrity to one of verifiable hardware-level proof.
The discussion provides a comparative analysis of industry-leading implementations, including NVIDIA’s Hopper architecture [1, 2], Google’s Titan-backed TPU v5p [3, 4], and Microsoft’s Azure Boost Cerberus system [5, 6], alongside the cluster-scale trust challenges presented by ultra-large systems like xAI’s Colossus [7].
The article concludes that Silicon-Level Sovereignty is no longer an optional security feature but a foundational requirement for establishing the integrity, privacy, and multi-tenant isolation necessary for high-stakes AI workloads.
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When “Correct” Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?
Corina Pasareanu
Haizhong Zheng
Beidi Chen
Ravi Mangal
Xinyu Yang
James Song
Yibo Peng
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, San Diego, California, United States (2026), 15514–15546
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Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively on functional correctness. In this paper, we reveal a novel type of threat to real-world code agents: Functionally Correct yet Vulnerable (FCV) patches, which pass all test cases but contain vulnerable code. With our proposed FCV-Attack, which can be deliberately crafted by malicious attackers or implicitly introduced by benign developers, we show that SOTA LLMs (e.g., ChatGPT and Claude) and agent scaffolds (e.g., SWE-agent and OpenHands) are all vulnerable to this FCV threat; across 12 agent-model combinations on SWE-Bench, the attack only requires black-box access and a single query to the code agent to perform the attack. For example, for CWE-538 (information exposure vulnerability), the FCV-Attack attains an attack success rate of 40.7% on GPT-5 Mini + OpenHands. Our results reveal an important security threat overlooked by current evaluation paradigms and urge the development of security-aware defenses for code agents.
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In modern Kubernetes environments, eBPF (Extended Berkeley Packet Filter) has become the de facto standard for high-performance dataplane enforcement. However, this architecture introduces a complex distributed state problem: the asynchronous synchronization between the Kubernetes control plane (Intent) and the kernel-space BPF maps (Reality).
A critical failure mode, termed “Silent Divergence,” occurs when the control plane believes a network policy or identity is applied, but the underlying kernel state is missing or corrupted. In this “Gray Failure” state, standard observability tools—including logs, liveness probes, and agent status checks—report health, while the network silently drops traffic.
This paper introduces eBPF-Auditor, a specialized consistency verification framework. Unlike standard agents that rely on event-based reconciliation, eBPF-Auditor performs a periodic “Two-Way State Audit” that mathematically verifies the intersection of Kubernetes Intent and BPF Reality. We demonstrate through fault injection and benchmarks on 5,000 pods that this approach successfully detects state drift with 100% accuracy and negligible sub-millisecond overhead (ms), making it a viable solution for high-frequency runtime verification in production hyperscale clusters.
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SpatialStack: Layered Geometry-Language Fusion for 3D VLM Spatial Reasoning
Jian Zhang
Bangya Liu
Achuta Kadambi
Zhiwen Fan
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2026)
Preview abstract
Large vision-language models (VLMs) still struggle with reliable 3D spatial reasoning, a core capability for embodied and physical AI systems. This limitation arises from their inability to capture fine-grained 3D geometry and spatial relationships. While recent efforts have introduced multi-view geometry transformers into VLMs, they typically fuse only the deep-layer features from vision and geometry encoders, discarding rich hierarchical signals and creating a fundamental bottleneck for spatial understanding. To overcome this, we propose SpatialStack, a general hierarchical fusion framework that progressively aligns vision, geometry, and language representations across the model hierarchy. Moving beyond conventional late-stage vision-geometry fusion, SpatialStack stacks and synchronizes multi-level geometric features with the language backbone, enabling the model to capture both local geometric precision and global contextual semantics. Building upon this framework, we develop VLM-SpatialStack, a model that achieves state-of-the-art performance on multiple 3D spatial reasoning benchmarks. Extensive experiments and ablations demonstrate that our multi-level fusion strategy consistently enhances 3D understanding and generalizes robustly across diverse spatial reasoning tasks, establishing SpatialStack as an effective and extensible design paradigm for vision-language-geometry integration in next-generation multimodal physical AI systems.
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As AI agents increasingly operate with system-level privileges across cloud environments, they face significant security vulnerabilities—most notably prompt injection and unauthorized command execution. With 72% of agent deployments experiencing security incidents within 90 days, there is a critical need for robust, defense-in-depth strategies. This article introduces "Bottom-Up AI Agent Security," a comprehensive 14-layer framework designed to secure AI agents from the foundation up. The strategy covers five essential domains: code-level security, container hardening, cloud IAM, runtime monitoring, and human oversight. By providing actionable implementation templates (including Java, YAML, and GitHub Actions) and a structured 4-week deployment roadmap, this framework enables cloud architects to transform vulnerable AI deployments into hardened, production-ready systems with measurable reductions in risk and operational costs.
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Shuffles of Context-Free Languages along Regular Trajectories
Corentin Barloy
Michaël Cadilhac
Kyle Ockerlund
2026
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In single-core processors, concurrency requires that multiple processes be interleaved into a single thread of execution by a scheduler. The language-theoretic operation that corresponds to this is the shuffle of two languages: the set of words obtained by interleaving a word from each language in an arbitrary, letter-wise fashion. It is well known that regular languages are closed under shuffles, while context-free languages (CFLs) are not.
Following an established line of research, this paper considers shuffles according to regular "trajectories," that is, subject to scheduling constraints expressed by an automaton. Unsurprisingly, some trajectories allow for CFLs to be shuffled into CFLs (e.g., simple concatenation of the two words), while others do not. This paper provides a robust toolset to show that a given trajectory would always shuffle two nonregular CFLs into a nonCFL. In the case of deterministic CFLs (DCFLs), a salient trichotomy of trajectories depending on how they shuffle DCFLs is provided.
These results are based on lemmata of independent interest regarding how pushdown automata (PDA) must invoke the stack when accepting a nonregular CFL or DCFL. The latter case relies on a recent result of Jančar and Šíma (MFCS'2021); answering an open question therein, it is demonstrated that said result cannot be generalized to arbitrary CFLs, leading to dedicated machinery for both cases.
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SAC133 - SSAC Comments on Proposed Root KSK Algorithm Rollover
Wes Hardaker
Internet Corporation for Assigned Names and Numbers (ICANN), ICANN Security and Stability Advisory Committee (SSAC) Reports and Advisories (2026), pp. 9
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The SSAC supports the transition from RSA with SHA-256 (Algorithm 8) to ECDSA P-256
with SHA-256 (Algorithm 13) as the cryptographic algorithm for the RootKSK. The root zone
has relied on RSA-based algorithms since DNSSEC signing began in 2010. The algorithm did
not change during the first KSK rollover in 2018 or during the second rollover currently
underway and scheduled to complete in October 2026. Establishing a clear and predictable
process for algorithm transitions is essential to the long-term security of the root zone, and the
SSAC observes that the proposal addresses the Recommendation 23 of the SSR2 Review
accordingly.
The SSAC notes that the proposal builds upon the Root Zone DNSSEC Algorithm Rollover
Study published by ICANN in May 2024, which assessed resolver and authoritative server
support for alternative algorithms, analyzed rollover methodologies, and evaluated operational
risks. The SSAC finds that the proposal implements the study’s recommendations. The SSAC also notes that this proposal is consistent with the SSAC’s prior work on DNSSEC key rollover,
including SAC063, SAC073, SAC102, and SAC108.
The SSAC encourages ICANN to proceed with this rollover. Specific comments on the
proposal’s methodology, timeline, and operational readiness follow
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We introduce a new Bayesian perspective on the concept of data reconstruction, and leverage this viewpoint to propose a new security definition that, in certain settings, provably prevents reconstruction attacks. We use our paradigm to shed new light on one of the most notorious attacks in the privacy and memorization literature - fingerprinting code attacks (FPC). We argue that these attacks are really a form of membership inference attacks, rather than reconstruction attacks. Furthermore, we show that if the goal is solely to prevent reconstruction (but not membership inference), then in some cases the impossibility results derived from FPC no longer apply.
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The rapid expansion of the Internet of Things (IoT) and smart home ecosystems has led to a fragmented landscape of user data management across consumer electronics (CE) such as Smart TVs, gaming consoles, and set-top boxes. Current onboarding processes on these devices are characterized by high friction due to manual data entry and opaque data-sharing practices. This paper introduces the User Data Sharing System (UDSS), a platform-agnostic framework designed to facilitate secure, privacy-first PII (Personally Identifiable Information) exchange between device platforms and third-party applications. Our system implements a Contextual Scope Enforcement (CSE) mechanism that programmatically restricts data exposure based on user intent—specifically distinguishing between Sign-In and Sign-Up workflows. Unlike cloud-anchored identity standards such as FIDO2/WebAuthn, UDSS is designed for shared, device-centric CE environments where persistent user-to-device bind-ing cannot be assumed. We further propose a tiered access model that balances developer needs with regulatory compliance (GDPR/CCPA). A proof-of-concept implementation on a reference ARMv8 Linux-based middleware demonstrates that UDSS reduces user onboarding latency by 65% and measurably reduces PII over-exposure risk through protocol-enforced data minimization. This framework provides a standardized approach to identity management in the heterogeneous CE market.
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Beyond Vector Similarity: Hierarchical Context-Aware Graph RAG vs Standard RAG in Enterprise Code Migration
Saurabh Garg
Arjit Shukla
Divya Malhotra
Suddhasatwa Bhaumik
Nilesh Jaiswal
Aniket Agrawal
Google Cloud Consulting, Google Cloud Consulting, Google, S. No, AP81, 83, N Main Rd, near Hard Rock Cafe, Koregaon Park Annexe, Mundhwa, Pune, Maharashtra 411036 (2026) (2026)
Preview abstract
As enterprises modernize legacy systems (e.g., monolithic Java architectures to Python microservices), Large Language Models (LLMs) have become instrumental in automated code translation.
However, traditional vector-based Retrieval-Augmented Generation (Standard RAG) struggles
with topological relationships, fetching isolated text chunks that frequently sever inheritance chains and lead to high compilation failure rates.
This paper presents a comparative analysis between Standard RAG and a novel Hierarchical
Context-Resident Graph (HCRG) methodology. Our pipeline utilizes tree-sitter for polyglot
Abstract Syntax Tree (AST) extraction, mapping architectural edges into a Google Cloud Spanner Property Graph, and serializing this structure into a Gemini (on Vertex AI) Context Cache to enable topological, parent-first code translation.
By shifting evaluation from naive text-overlap to a custom 7-metric framework measuring Software Engineering (SE) utility, empirical evaluations on the spring-petclinic-genai repository
demonstrate significant structural improvements. Graph RAG decisively mitigates dependency loss, dropping the API hallucination rate from 56.4% to 16.2%.
Furthermore, it improves Dependency Resolution Quality (DRQ) from 34.8% to
65.9% and enhances Parent-Child Consistency (PCC) from 26.7% to 45.5%. Interestingly, traditional lexical metrics fail to capture this divergence; both methodologies achieved
an identical 91% average CodeBLEU score, effectively masking Standard RAG’s structural
failures behind syntactically plausible but broken code.
However, the results indicate that Graph RAG is not strictly superior across all dimensions. Providing the LLM with dense, global structural context introduces new vulnerabilities: Graph RAG suffers a severe degradation in Cyclomatic Complexity Consistency
(dropping from Standard RAG’s 71.6% to 46.7%) due to defensive over-engineering
by the LLM, alongside a slight drop in Docstring Preservation (67.0% down to 61.0%)
caused by prompt attention dilution.
Ultimately, this research validates that while Graph RAG trades an increase in code complexity
for critical reductions in API hallucinations, it offers a substantially more viable and architecturally sound path for automated enterprise codebase modernisation.
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Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
Han Zhou
Shariq Iqbal
Ivan Vulić
Anna Korhonen
International Conference on Learning Representations (ICLR) (2026)
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Large language models (LLMs), employed as multiple agents that interact and collaborate with each other, have excelled at solving complex tasks. The agents are programmed with {prompts} that declare their functionality, along with the {workflows} that orchestrate interactions within a structured flow. Designing prompts and workflows for multi-agent systems is inherently complex, especially when addressing a new task. It often demands expert-level knowledge and involves significant trial and error. Gaining a deep understanding of the factors that contribute to effective multi-agent systems is essential for automating the entire process. Motivated by this, we first conduct an in-depth analysis of the design spaces for multi-agent systems, focusing on the impact of prompts, scaling the number of agents, and common types of agentic modules. Our findings reveal that top-performing systems often emerge from simpler design spaces, where prompts play a critical role in enhancing agent functionality and enabling more effective scaling. Based on the insights, we propose Multi-Agent System Search (MASS), a multi-stage optimization framework that performs the optimization in a pruned design space, with prompts and an influential subset of modules. We show that MASS-optimized multi-agent systems outperform existing alterntives by a substantial margin. Based on the MASS-found systems, we finally propose design principles behind building effective multi-agent systems.
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