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 11612 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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We study a quantized prefix estimator for inner products that turns a randomly rotated TurboQuant-style representation into a cheap Johnson–Lindenstrauss-like search signal. The idea is simple: rotate the vectors once, keep only a short prefix of coordinates for fast scoring, and quantize the database-side prefix with an unbiased scalar quantizer. We prove that this estimator is unbiased and that its error separates cleanly into two interpretable sources: prefix truncation from using only r coordinates, and quantization error from using b bits per coordinate
This separation is useful in systems because the prefix can be exposed as a lightweight filter without building a separate projection index. In ParlayANN graph search, a 64-coordinate truncated view of existing TQ4 codes can replace a separately stored JL256 filter before full-precision reranking, adding only prefix-scale and query lookup-table bookkeeping. In k-means, the same estimator accelerates the dominant point–centroid assignment kernel while preserving exact centroid norms. Empirically, the truncated-TQ filter tracks the JL recall–throughput frontier across five graph-search datasets while reusing the quantized representation already present in the index.
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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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VidMap: Exploiting Temporal Structure for Video-Based Structure-from-Motion
Zador Pataki
Paul-Edouard Sarlin
Marc Pollefeys
ECCV (2026)
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Accurately recovering the camera's calibration and metric poses for any unconstrained video would unlock large-scale training data for navigation and scene understanding. The dominant approaches to this problem are severely limited: Simultaneous Localization and Mapping (SLAM) is sensitive to initialization and transient failures due to its causal, incremental nature; it is often over-optimized for real-time operation and generally requires known camera calibration; while Structure-from-Motion (SfM) typically forgoes any image ordering, enabling optimal initialization and global optimization, but lacks robustness to visual symmetries and extreme motions. To bridge this gap, we introduce a system that combines the strong sequential constraints of SLAM with the flexibility and global optimization of offline SfM, enabling the metric reconstruction of arbitrary, long, uncalibrated videos. This system leverages recent advances in wide-baseline dense image matching, treats temporal ordering as a first-class citizen for reliable loop closure, and augments global optimization with metric monocular depth priors. As a result, thorough evaluations on diverse, challenging datasets that exhibit extreme motion and visual symmetries reveal that our approach is significantly more robust and accurate than both state-of-the-art SLAM and SfM, classical or learned, with given or unknown camera calibration. The code is publicly available at https://github.com/cvg/vidmap.
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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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LibEvoBench: Probing Temporal Knowledge Startification in Code Generation Models
Daniele Cipollone
Sergey Titov
Egor Bogomolov
Arie van Deursen
Advances in Neural Information Processing Systems (NeurIPS) (2026)
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Large software projects often depend on older versions of libraries, even as APIs continue to evolve across releases. This creates a challenge for LLMs: they must maintain knowledge of multiple API versions, not merely the latest or most common one. However, current LLMs, trained on temporally mixed corpora and lack explicit mechanisms for such version-specific reasoning, leading to anachronistic errors -- calling APIs as they exist in a different library version. To systematically evaluate this phenomenon, we introduce LibEvoBench, a multi-task benchmark spanning multiple versions of widely used Python libraries, along with a new metric, the Software Evolution Understanding Score (SEUS), to measure models' consistency when working with evolving APIs. Our results show that state-of-the-art models are largely version-oblivious: performance degrades for evolving APIs, while for stable APIs it remains the same across versions. Moreover, simply specifying the target version provides no benefit, while relevant documentation significantly boosts models' accuracy. These findings highlight a systematic limitation of current training paradigms and motivate new approaches for temporally grounded knowledge in code generation.
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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
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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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The advent of 3D Gaussian Splatting has revolutionized graphics rendering by offering high visual quality and fast rendering speed. However, training large-scale scenes at high quality remains challenging due to the substantial memory demands required to store Gaussians and optimizer states. To address these limitations, we propose GS-Offload, fast and memory-efficient training system for 3D Gaussian Splatting. GS-Offload stores Gaussians and optimizer states in host memory and selectively transfer only the necessary data to GPU memory on demand, significantly reducing GPU memory usage. With carefully designed software pipelining and CPU-side optimizer acceleration, GS-Offload achieves training speed near that of GPU-only setups, while significantly lowering GPU memory demands.
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HAPS-to-Host: Seamless FPGA Prototyping for Full-Stack System-on-Chip Validation
Merolin Gold
Jyothish Balakrishnan
Ashwin Kailasam
2026
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The Always-On Subsystem (AOS) is a foundational component for modern mobile platforms, integrating processing cores, memory, and peripheral controllers to support low-power audio and sensor processing while managing complex I/O tasks. However, traditional hardware emulation often faces challenges to accurately model and test the analog modules—such as PHYs—that are essential for real-world I/O connectivity in audio systems. This paper proposes a robust FPGA prototyping system for a System-on-Chip’s Always-On Subsystem (AOS) to bridge this gap. Serving as a high-fidelity platform for accelerating pre-silicon software development, this low-power audio and sensor processing hub enables comprehensive design verification and optimizes the full software stack. This methodology facilitates the tuning of production software—including booting Android and testing the Device Audio stack—prior to costly silicon fabrication by achieving higher clock speeds.
The proposed prototyping platform utilizes the Synopsys HAPS-100 system to accurately model the AOS RTL design, encompassing all internal components and interfaces at higher operating speeds. This high-speed environment provides a robust hardware abstraction layer, enabling software execution that mirrors silicon behavior. To support a comprehensive mobile platform environment, the architecture facilitates the following critical connectivity:
Application Processor Communication: A PCIe Gen1 connection to the Host Platform enables the execution of production-level ISA software and supports the development of host-side AOS drivers and Android configurations.
Real-World I/O: A dedicated daughter card interfaces external components to the AOS via the HT3 connector, allowing for the integration of physical microphones, speakers, and sensors for realistic scenario simulation.
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Every team building Large Language Models (LLMs) faces a core challenge: offline benchmarks show performance gains and user engagement rises post deployment, but isolating cause from effect remains difficult. Simultaneous marketing, media coverage, and seasonal demand obscure whether model updates truly drive engagement gains. This paper presents a novel causal estimation approach that leverages non uniform quality improvements across capabilities within a single model version. Because capabilities improve unevenly (e.g., strong gains in coding versus modest gains in writing), users experience varied quality depending on their task distribution. This variation in experienced quality provides causal signal for estimation. We validate this approach on synthetic data with known ground truth. The raw estimator recovers 81% to 88% of the true effect, with the remainder lost to measurement error in usage estimates. Applying an errors in variables disattenuation adjustment (using a test retest reliability ratio mean correlation of 0.861) corrects the estimate to 1.017 (bootstrapped 95% CI: 0.915 to 1.109). By contrast, naive methods fail significantly, recovering only 63% without version controls and 62% using real time rather than frozen usage patterns. Permutation tests confirm the framework distinguishes true causal effects from noise. Sensitivity analysis indicates recovery improves monotonically from 78% to 87% as pre period length increases from 3 to 7 weeks, highlighting a clear tradeoff between sample duration and estimator precision. Seed sensitivity tests further confirm stability across random draws.
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**Agentic Engineering** is the rigorous discipline of treating Large Language Models as semi-autonomous systems that execute complex, multi-step workflows (trajectories) based on verifiable specifications, rather than using them as simple autocomplete engines.
Here is a brief summary of its core principles:
* **Main Goals:** It aims to maximize the agent's autonomous run-time, multiply a single engineer's impact by running parallel tasks, and offload tedious boilerplate coding.
* **The "Harness":** A raw model is virtually useless without heavy investment in a harness—comprising tools, system prompts, and strict guardrails—to reliably guide the model and enforce coding policies.
* **Loss of Micro-Control:** Engineers must surrender idiosyncratic stylistic preferences; if the agent's code passes automated linters and tests, it is accepted.
* **Meta-Debugging:** When failures occur, engineers no longer fix code syntax. Instead, they debug the workflow itself—adjusting the agent's tools, search queries, or prompt constraints to ensure repeatable success.
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Correct-by-Construction Framework for Robustness Against Divergent Voltage Drops and High Clock Divergence
Prateek Pendyala
Mathew Kaipanatu
2026
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Limitations in Sign-off Methodology:
Traditional STA corner selection 10% lower STA corner from PMIC voltage is selected- design is constantly optimised for 10% lower voltage, thereby failing to build margin against differential drop.
IR aware STA does not account timing path’s geometric imbalances (logic depths), net dominated interconnect skews (net delays & metal layer variation) - all dominant in advanced process nodes. Furthermore, this is workload dependent: fixing IR STA violations does not build margins on unseen vectors.
Frequent Silicon issues due to these gaps:
Low voltage mode scan shift Vmin jumps need to meet slack on paths which become exponentially sensitive to voltage gradients. Even small differential IR drops (capture & launch traversing through contrasting IR hotspot & cool regions) cause catastrophic slack loss
High divergence paths with structural imbalances-where clock paths are net-dominated & are operated at high speeds often fail to meet hold timing, despite good pre-silicon margins due to high interlayer metal-sheet & via resistances in lower process nodes.
Additionally, there is considerable PPA impact -higher dynamic & leakage power in clock & data paths respectively in divergent paths. Our proposed solution aims to address above gaps.
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Spectral amplification for ground-state energy estimation of electronic structure in first quantization
Alicja Dutkiewicz
Alec White
Guang Hao Low
Albert Eugene DePrince III
Marika Kieferova
Dominic Berry
arXiv:2607.15358 (2026)
Preview abstract
We demonstrate an asymptotic gate complexity improvement in first-quantized ground-state energy estimation of electronic structure Hamiltonians in a plane wave basis by employing the sum-of-squares spectral gap amplification protocol. The improvement relies on identifying a sum-of-squares representation of the Hamiltonian which provides a lower bound certificate and low cost block encoding that leads to a provably lower quantum phase estimation gate cost. This is achieved by using a sum-of-squares operator generated by the total charge density operator resulting in a block encoding normalization improvement of $\lambda = \mathcal{O}\left(\eta\Delta^{-1.5}+\eta^{1.5}\Delta^{-1} \right)$ compared to prior work $\lambda = \mathcal{O}(\eta\Delta^{-2}+\eta^2\Delta^{-1})$ where $\eta$ is the number of electrons and $\Delta$ is the simulation grid spacing. The asymptotic reduction in block encoding normalization and similar block encoding costs to prior work is demonstrated to reduce resource estimates for materials and chemical systems by a factor of $2 - 44\times$ corresponding to the lowest cost estimates for \textit{ab initio} materials simulation.
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Inference Perf: A Benchmarking Tool for GenAI Inference
Sachin Varghese
Jason Kramberger
Brendan Slabe
Chen Wang
Yuan Tang
Journal of Open Source Software (2026)
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Inference Perf is a generative AI (GenAI) inference performance benchmarking tool aimed at benchmarking and analyzing the performance of inference deployments. It is designed to be model-server agnostic, allowing for apples-to-apples comparisons across different model servers and serving stacks. As part of the inference benchmarking and metrics standardization effort in the Kubernetes wg-serving, it seeks to standardize tooling and metrics for measuring inference performance across the Kubernetes and model server communities.
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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.
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