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 11206 publications
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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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ARM MTE Performance in Practice
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Taehyun Noh
Yingchen Wang
Tal Garfinkel
Mahesh Madhav
Mattan Erez
Shravan Narayan
Usenix Security (2026)
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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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Peeking Ahead of the Field Study: Exploring VLM Personas as Support Tools for Embodied Studies in HCI
Xinyue Gui
Ding Xia
Mark Colley
Yuan Li
Vishal Chauhan
Anubhav Anubhav
Ehsan Javanmardi
Stela Hanbyeol Seo
Chia-Ming Chang
Manabu Tsukada
Takeo Igarashi
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI 26)
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Field studies are irreplaceable but costly, time-consuming, and error-prone, which need careful preparation. Inspired by rapid-prototyping in manufacturing, we propose a fast, low-cost evaluation method using Vision-Language Model (VLM) personas to simulate outcomes comparable to field results. While LLMs show human-like reasoning and language capabilities, autonomous vehicle (AV)-pedestrian interaction requires spatial awareness, emotional empathy, and behavioral generation. This raises our research question: To what extent can VLM personas mimic human responses in field studies? We conducted parallel studies: 1) one real-world study with 20 participants, and 2) one video-study using 20 VLM personas, both on a street-crossing task. We compared their responses and interviewed five HCI researchers on potential applications. Results show that VLM personas mimic human response patterns (e.g., average crossing times of 5.25 s vs. 5.07 s) lack the behavioral variability and depth. They show promise for formative studies, field study preparation, and human data augmentation.
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Type-Aware Ranking of Urban Similarity from Aerial Imagery
Idan Kligvasser
Yotam Intrator
Yuval Desheh
Aviad Barzilai
Niv Efron
Ehud Rivlin
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops (2026), pp. 821-829
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Estimating and ranking cross-city similarity from aerial imagery is a fundamental challenge in remote sensing and geospatial representation learning. Urban environments differ widely in road layout, marking conventions, and infrastructure design, yet standard visual representations often struggle to disentangle these meaningful structural variations from superficial appearances. In this work, we propose a type-aware contrastive learning framework that measures urban similarity by explicitly modeling distinct infrastructure elements. Leveraging open-vocabulary retrieval, we construct a globally diverse dataset of road-related features, such as intersections, crosswalks, and bus lanes, and train a type-conditioned Vision Transformer that fuses visual features with CLIP-derived semantic embeddings. Crucially, we introduce an adaptive per-type contrastive loss that dynamically emphasizes infrastructure categories with high discriminative power while down-weighting less informative types. To quantify city-level similarity, we aggregate per-type cosine similarities via a lightweight classifier to generate a global city-to-city similarity matrix. Experiments demonstrate that this type-aware approach significantly improves clustering quality and successfully generalizes to unseen cities, establishing a scalable, interpretable foundation for comparative urban analysis.
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Expert evaluation of LLM world models: A high-Tc superconductivity case study
Haoyu Guo
Maria Tikhanovskaya
Paul Raccuglia
Alexey Vlaskin
Chris Co
Scott Ellsworth
Matthew Abraham
Lizzie Dorfman
Peter Armitage
Chunhan Feng
Antoine Georges
Olivier Gingras
Dominik Kiese
Steve Kivelson
Vadim Oganesyan
Brad Ramshaw
Subir Sachdev
Senthil Todadri
John Tranquada
Eun-Ah Kim
Proceedings of the National Academy of Sciences (2026)
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Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. This work evaluates the performance of six different LLM-based systems for answering scientific literature questions, including commercially available closed models and a custom retrieval-augmented generation (RAG) system capable of retrieving images alongside text. We conduct a rigorous expert evaluation of the systems in the domain of high-temperature cuprate superconductors, a research area that involves material science, experimental physics, computation, and theoretical physics. We use an expert-curated database of 1726 scientific papers and a set of 67 expert-formulated questions. The evaluation employs a multi-faceted rubric assessing balanced perspectives, factual comprehensiveness, succinctness, evidentiary support, and image relevance. Our results demonstrate that RAG-based systems, powered by curated data and multimodal retrieval, outperform existing closed models across key metrics, particularly in providing comprehensive and well-supported answers, and in retrieving relevant visual information. This study provides valuable insights into designing and evaluating specialized scientific literature understanding systems, particularly with expert involvement, while also highlighting the importance of rich, domain-specific data in such systems.
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Enterprise service delivery platforms, while vital for HR operations, create significant challenges in managing the risks of Personally Identifiable Information (PII) exposure. The integration of Generative AI offers new efficiencies but also amplifies these risks. Existing solutions—ranging from manual redaction and rule-based Data Loss Prevention (DLP) to inflexible data masking—fail to provide a nuanced, integrated approach. This paper introduces the Dual-Mode Privacy Guard (DMPG), a conceptual framework that establishes a model for Augmented Compliance. The framework provides a "defense-in-depth" strategy built on three pillars: (1) a Zero-Trust AI Foundation leveraging a verifiable, non-retention API gateway to ensure data privacy; (2) a proactive "Guardrail" that uses AI to detect and flag potential PII for human-in-the-loop review; and (3) an on-demand "Tool" that allows users to create securely anonymized data assets. By differentiating between proactive monitoring and reactive utility, the DMPG shifts the compliance paradigm from a manual burden to an AI-assisted process that enhances, rather than replaces, human oversight. This paper details the framework’s platform-agnostic architecture, using Salesforce as a reference implementation, and argues for its novelty as a model for operationalizing privacy principles within modern enterprise systems.
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This article delves into how Google Site Reliability Engineers (SREs) leverage Gemini 3 and the Gemini CLI to aggressively reduce Mean Time to Mitigation (MTTM) during real-world outages. By focusing on the SRE motto of "Eliminate Toil," the article walks through a simulated incident, demonstrating how an agentic CLI acts as a human-in-the-loop copilot across the entire incident lifecycle: from initial paging and investigation, through safe, tool-driven mitigation and root cause analysis, to automated postmortem generation and action item filing. This direct integration of Gemini's reasoning capabilities with operational data and internal tools creates a virtuous cycle where past incident learnings continuously inform and improve future solutions.
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Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning
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Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices interpretability while introducing significant resource and operational overhead. To address these limitations, we introduce Prompt-Level Distillation (PLD). We extract explicit reasoning patterns from a Teacher model and organize them into a structured list of expressive instructions for the Student model's System Prompt. Evaluated on the StereoSet and Contract-NLI datasets using Gemma-3 4B, PLD improved Macro F1 scores from 57\% to 90.0\% and 67\% to 83\% respectively, enabling this compact model to match frontier performance with negligible latency overhead. These expressive instructions render the decision-making process transparent, allowing for full human verification of logic, making this approach ideal for regulated industries such as law, finance, and content moderation, as well as high-volume use cases and edge devices.
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Reasoning-Driven Synthetic Data Generation and Evaluation
Tim R. Davidson
Benoit Seguin
Transactions on Machine Learning Research (2026)
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Although many AI applications of interest require specialized multi-modal models, relevant data to train such models is inherently scarce or inaccessible. Filling these gaps with human annotators is prohibitively expensive, error-prone, and time-consuming, leading model builders to increasingly consider synthetic data as a scalable alternative. However, existing synthetic data generation methods often rely on manual prompts, evolutionary algorithms, or extensive seed data from the target distribution — limiting their scalability, explainability, and control. In this paper, we introduce Simula: a novel reasoning-driven framework for data generation and evaluation. It employs a seedless, agentic approach to generate synthetic datasets at scale, allowing users to define desired dataset characteristics through an explainable and controllable process that enables fine-grained resource allocation. We show the efficacy of our approach on a variety of datasets, rigorously testing both intrinsic and downstream properties. Our work (1) offers guidelines for synthetic data mechanism design, (2) provides insights into generating and evaluating synthetic data at scale, and (3) unlocks new opportunities for developing and deploying AI in domains where data scarcity or privacy concerns are paramount.
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Generative AI is reshaping software development, yet its psychological impact remains under-researched. During May and August 2025 we conducted reflexive thematic analysis of interviews with 12 senior engineers (≥5 years experience) recruited from Western technology hubs to explore shifts in professional identity. We identify a central transition from "coder to conductor," where AI acts as a cognitive partner. Key findings include: (1) a re-architecting of focus from implementation to strategy; (2) a shift in productivity metrics from output to impact; and (3) a dual-impact on agency, where AI empowers autonomy but threatens competence through de-skilling anxieties. These findings suggest that as implementation becomes commoditised, organisational training and career progression must prioritise architectural mastery and metacognitive oversight to ensure sustained developer motivation and system integrity.
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As the ECMAScript specification evolves, industrial-scale JavaScript compilers face the challenge of supporting modern language syntax while maintaining compatibility for diverse execution environments. Traditionally, compilers solve this by running transpilation passes in a monolithic pipeline, where the transpilation passes are chosen to execute strictly based on a target language level. This results in significant computational waste, as compilers perform expensive Abstract Syntax Tree (AST) traversals to lower features that may not exist in the actual input source code. We present a static analysis improvement that conditionally executes transpiler passes based on accurately tracking and dynamically maintaining the exact set of language features seen in the compilation unit throughout the transpilation process. It is implemented in the production Google Closure Compiler. By populating and maintaining a FeatureSet at every JavaScript script-level, it dynamically skips running the unnecessary lowering passes. We detail the architectural safeguards - including strategic pass ordering and dynamic validation of the transpiled code for feature-correctness. Evaluation of this improvement on large-scale production applications produced a considerable reduction in compilation time and saved compute and memory usage.
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Multimodal large language models (LLMs) integrate and process information from multiple modalities such as text, images, audio, and video, enabling complex tasks such as audio translation and visual question answering. While powerful, this complexity introduces novel vulnerabilities to sophisticated adversarial attacks. This survey paper provides a comprehensive overview of this rapidly expanding field, systematically categorizing attacks that range from manipulations of single modalities (e.g., perturbed images or audio) to those exploiting cross-modal interactions. We overview how these attacks exploit weaknesses in model fusion, attention mechanisms, and representation learning and provided analyses on their potential for real-world consequences.
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Large language models (LLMs) are trained on web-scale corpora that exhibit steep power-law distributions, in which the distribution of knowledge is highly long-tailed, with most appearing infrequently. While scaling has improved average-case performance, persistent failures on low-frequency, domain-specific, cultural, and temporal knowledge remain poorly characterized. This paper develops a structured taxonomy and analysis of long-tail knowledge in large language models, synthesizing prior work across technical and sociotechnical perspectives.
We organize the literature along four complementary axes: how long-tail knowledge is defined, the mechanisms by which it is lost or distorted during training and inference, the technical interventions proposed to mitigate these failures, and the implications of these failures for fairness, accountability, transparency, and user trust. We further examine how existing evaluation practices obscure tail behavior and complicate accountability for rare but consequential failures. The paper concludes by identifying open challenges related to privacy, sustainability, and governance that constrain long-tail knowledge representation. Taken together, this paper provides a unifying conceptual framework for understanding how long-tail knowledge is defined, lost, evaluated, and manifested in deployed language model systems.
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Bridging the Dimensionality Gap: A Taxonomy and Survey of 2D Vision Model Adaptation for 3D Analysis
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The remarkable success of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in 2D computer vision has catalyzed significant research into their adaptation for the complex domain of 3D analysis. However, a fundamental dichotomy exists between the regular, dense grid of 2D images and the irregular, sparse nature of 3D data formats such as point clouds and meshes. This paper provides a comprehensive survey and a novel intellectual framework for navigating this burgeoning field. Our core contribution is a new taxonomy that organizes adaptation strategies into three distinct families: (1) Data-centric methods, which project 3D data into 2D formats to leverage off-the-shelf 2D models; (2) Architecture-centric methods, which design intrinsic network modules to directly process 3D data; and (3) Hybrid methods, which synergistically combine pre-trained 2D features with 3D modeling processing pipelines to benefit from both rich visual priors and explicit geometric reasoning. Through this taxonomic lens, we conduct a systematic review and qualitative synthesis of the field. We illuminate the fundamental trade-offs between these families concerning computational complexity, reliance on large-scale pre-training, and the preservation of geometric inductive biases. Based on this analysis, we identify and discuss critical open challenges and chart promising future research directions, including the development of 3D foundation models, advancements in self-supervised learning for geometric data, and the deeper integration of multi-modal signals. This survey serves as an essential resource and roadmap for researchers seeking to understand and advance the state-of-the-art in 3D computer vision.
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