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 10961 publications
Preview abstract
Semantic data models express high-level business concepts and metrics, capturing the business logic needed to query a database correctly. Most data modeling solutions are built as layers above SQL query engines, with bespoke query languages or APIs. The layered approach means that semantic models can’t be used directly in SQL queries. This paper focuses on an open problem in this space – can we define semantic models in SQL, and make them naturally queryable in SQL?
In parallel, graph query is becoming increasingly popular, including in SQL. SQL/PGQ extends SQL with an embedded subset of the GQL graph query language, adding property graph views and making graph traversal queries easy.
We explore a surprising connection: semantic data models are graphs, and defining graphs is a data modeling problem. In both domains, users start by defining a graph model, and need query language support to easily traverse edges in the graph, which means doing joins in the underlying data.
We propose some useful SQL extensions that make it easier to use higher-level data model abstractions in queries. Users can define a “semantic data graph” view of their data, encapsulating the complex business logic required to query the underlying tables correctly. Then they can query that semantic graph model easily with SQL.
Our SQL extensions are useful independently, simplifying many queries – particularly, queries with joins. We make declared foreign key relationships usable for joins at query time – a feature that seems obvious but is notably missing in standard SQL.
In combination, these extensions provide a practical approach to extend SQL incrementally, bringing semantic modeling and graph query together with the relational model and SQL.
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AI coding assistants are rapidly becoming integral to modern software development. A key challenge in this space is the continual need to migrate and modernize codebases in response to evolving software ecosystems. Traditionally, such migrations have relied on rule-based systems and human intervention. With the advent of powerful large language models (LLMs), AI-driven agentic frameworks offer a promising alternative—but their effectiveness remains underexplored. In this paper, we introduce FreshBrew, a novel benchmark for evaluating AI-based agentic frameworks on project-level Java migrations. We benchmark several such frameworks, powered by state-of-the-art LLMs, and compare their performance against established rule-based tools. Our evaluation of AI agents on this benchmark of 228 repositories shows that the top-performing model, Gemini 2.5 Flash, can successfully migrate 56.5% of projects to JDK 17. Our empirical analysis reveals novel insights into the critical strengths and limitations of current agentic approaches, offering actionable insights into their real-world applicability. By releasing FreshBrew publicly upon acceptance, we aim to facilitate rigorous, reproducible evaluation and catalyze progress in AI-driven codebase modernization.
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CrossCheck: Input Validation for WAN Control Systems
Rishabh Iyer
Isaac Keslassy
Sylvia Ratnasamy
Networked Systems Design and Implementation (NSDI) (2026) (to appear)
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We present CrossCheck, a system that validates inputs to the Software-Defined Networking (SDN) controller in a Wide Area Network (WAN). By detecting incorrect inputs—often stemming from bugs in the SDN control infrastructure—CrossCheck alerts operators before they trigger network outages.
Our analysis at a large-scale WAN operator identifies invalid inputs as a leading cause of major outages, and we show how CrossCheck would have prevented those incidents. We deployed CrossCheck as a shadow validation system for four weeks in a production WAN, during which it accurately detected the single incident of invalid inputs that occurred while sustaining a 0% false positive rate under normal operation, hence imposing little additional burden on operators. In addition, we show through simulation that CrossCheck reliably detects a wide range of invalid inputs (e.g., detecting demand perturbations as small as 5% with 100% accuracy) and maintains a near-zero false positive rate for realistic levels of noisy, missing, or buggy telemetry data (e.g., sustaining zero false positives with up to 30% of corrupted telemetry data).
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How many T gates are needed to approximate an arbitrary n-qubit quantum state to within
a given precision ϵ? Improving prior work of Low, Kliuchnikov and Schaeffer, we show that the
optimal asymptotic scaling is Θ(sqrt{2^n log(1/ε)} + log(1/ε)) if we allow an unlimited number of ancilla qubits. We also show that this is the optimal T-count for implementing an arbitrary
diagonal n-qubit unitary to within error ϵ. We describe an application to batched synthesis of
single-qubit unitaries: we can approximate a tensor product of m = O(log log(1/ϵ)) arbitrary
single-qubit unitaries to within error ϵ with the same asymptotic T-count as is required to
approximate just one single-qubit unitary.
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Preview abstract
For many practical applications of quantum computing, the slowest and most costly steps involve coherently accessing classical data. We help address this challenge by applying mass production techniques, which can sometimes allow us to perform operations many times in parallel for a cost that is comparable to a single execution[1-3]. We combine existing mass-production results with modern approaches for loading classical data using ``quantum read-only memory.'' We show that quantum mass production techniques offer no benefit when we consider a cost model that focuses purely on the number of non-Clifford gates. However, analyzing the constant factors in a more nuanced cost model, we find that it may be possible to obtain a reduction in cost of an order or magnitude or more for a variety reasonably-sized fault-tolerant quantum algorithms. We present several applications of quantum mass-production techniques beyond naive parallelization, including a strategy for reducing the cost of serial calls to the same data loading step.
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I know what I don't know: improving model cascades through confidence tuning
Stephan Rabanser
Nathalie Rauschmayr
Petra Poklukar
Congchao Wang
2025
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Large-scale machine learning models deliver strong performance across a wide range of tasks but come with significant computational and resource constraints. To mitigate these challenges, local smaller models are often deployed alongside larger models, relying on routing and deferral mechanisms to offload complex tasks. However, existing approaches inadequately balance the capabilities of these models, often resulting in unnecessary deferrals or sub-optimal resource usage. In this work we introduce a novel loss function called Gatekeeper for calibrating smaller models in cascade setups. Our approach fine-tunes the smaller model to confidently handle tasks it can perform correctly while deferring complex tasks to the larger model. Moreover, it incorporates a mechanism for managing the trade-off between model performance and deferral accuracy, and is broadly applicable across various tasks and domains without any architectural changes. We evaluated our method on encoder-only, decoder-only, and encoder-decoder architectures. Experiments across image classification, language modeling, and vision-language tasks show that our approach substantially improves deferral performance.
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Audio description (AD) narrates important visual details which are played during dialogue gaps in video soundtracks, making them accessible to blind and low vision (BLV) audiences. AD professionals (producers, writers, narrators, mixers, and quality control specialists) possess expert knowledge of AD development and the constraints that affect their work. However, their perspectives remain largely absent in AD research. We present interviews with 17 AD professionals (8 BLV), detailing their workflows to produce AD for recorded media and live theater. We additionally explore their perspectives on recent changes impacting their work, revealing tensions between advocacy for culturally-competent AD and the rise of automations--some beneficial, others with concerning implications for AD quality. Highlighting these tensions, we offer research directions to support AD professionals, and we pose guiding questions for AD and AI innovators on preserving the high-quality human touch professionals consider fundamental to the accessibility provision.
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Browser fingerprinting enables persistent cross-site user tracking via subtle techniques that often evade conventional defenses or cause website breakage when script-level blocking countermeasures are applied. Addressing these challenges requires detection methods offering both function-level precision to minimize breakage and inherent robustness against code obfuscation and URL manipulation.
We introduce ByteDefender, the first system leveraging V8 engine bytecode to detect fingerprinting operations specifically at the JavaScript function level. A Transformer-based classifier, trained offline on bytecode sequences, accurately identifies functions exhibiting fingerprinting behavior. We develop and evaluate lightweight signatures derived from this model to enable low-overhead, on-device matching against function bytecode during compilation but prior to execution, which only adds a 4% (average) latency to the page load time. This mechanism facilitates targeted, real-time prevention of fingerprinting function execution, thereby preserving legitimate script functionality. Operating directly on bytecode ensures inherent resilience against common code obfuscation and URL-based evasion. Our evaluation on the top 100k websites demonstrates high detection accuracy at both function- and script-level, with substantial improvements over state-of-the-art AST-based methods, particularly in robustness against obfuscation. ByteDefender offers a practical framework for effective, precise, and robust fingerprinting mitigation.
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SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling
Xinyun Chen
Transactions on Machine Learning Research (TMLR) (2025)
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Recent advancements in Large Language Models (LLMs) have created new opportunities to enhance performance on complex reasoning tasks by leveraging test-time computation. However, existing scaling methods have key limitations: parallel methods like repeated sampling are often inefficient and quickly saturate, while sequential methods like SELF-REFINE struggle to improve after a few rounds. Although combining these approaches shows promise, current methods require fine-tuned reward and revision models. This paper proposes Self-Enhanced Test-Time Scaling (SETS), a simple yet effective approach that overcomes these limitations by strategically combining parallel and sequential techniques and fully leveraging LLMs' self-improvement abilities. SETS exploits the inherent self-verification and self-correction capabilities of LLMs, unifying sampling, verification, and correction within a single framework. This facilitates efficient and scalable test-time computation for enhanced performance on complex tasks without any model training. Our comprehensive experimental results on challenging benchmarks spanning planning, reasoning, math, and coding demonstrate that SETS achieves significant performance improvements and more advantageous test-time scaling behavior than the alternatives.
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GOALIE (GOAL oriented IntErventions) Proactive Multimodal Agent to Assist Augmented Reality
Saptarashmi Bandyopadhyay
Vikas Bahirwani
Lavisha Aggarwal
Bhanu Guda
Lin Li
Qin Liu
Tom Goldstein
John Dickerson
Andrea Colaco
2025
Preview abstract
Multimodal AI Agents are helpful to assist and guide users in completing real-time tasks like cooking, robotics, manufacturing. An emerging form of multimodal communication is Augmented Reality (AR), where an AI Agent can enhance user experience with step-by-step guidance of tasks by observing the user's vision and language inputs. Current LLM or VLM based agents are reactive, waiting for an user query before responding. Proactive AI Agents in AR focus on detecting when the AI Agent should autonomously intervene to fix mistakes or followup any instruction. Our GOALIE (GOAL-oriented IntErvention) Agent is the first multimodal proactive AR agent which guides the user step-by-step on its own. We build an innovative Zero-Shot Prompting framework PSoS (Proactive Sequence of Steps) with the context of abstract past user actions, the agent's previous responses, and the user's granular goals and actions before it is detected that the AI Agent should intervene. We use PSoS for Supervised Finetuning (SFT), Direct Preference Optimization (DPO) and Group-Relative Policy Optimization (GRPO) finetuning of our AI agent to improve the quality of the agent's proactive intervention. We also propose a new algorithmic framework, Bagged group Relative Policy Optimization (BRPO), to reduce the variance in rewards of generation groups, to adapt the finetuning algorithm for multimodal proactive interventions by the AI Agent and to enable real-time finetuning of the AI model. We compare the step-by-step intervention quality and efficiency of the GOALIE Agent with Gemma-3 models along with other VLMs for task execution with human expert labels. We conduct human evaluation of the proactive interventions, demonstrating user satisfaction with the GOALIE Agent's proactive interventions. We will release the code, model and human evaluation data.
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Generative Quantum Advantage for Classical and Quantum Problems
Robert Huang
Michael Broughton
Norhan Eassa
arXiv:2509.09033 (2025)
Preview abstract
Recent breakthroughs in generative machine learning, powered by massive computational resources, have demonstrated unprecedented human-like capabilities. While beyond-classical quantum experiments can generate samples from classically intractable distributions, their complexity has thwarted all efforts toward efficient learning. This challenge has hindered demonstrations of generative quantum advantage: the ability of quantum computers to learn and generate desired outputs substantially better than classical computers. We resolve this challenge by introducing families of generative quantum models that are hard to simulate classically, are efficiently trainable, exhibit no barren plateaus or proliferating local minima, and can learn to generate distributions beyond the reach of classical computers. Using a 68-qubit superconducting quantum processor, we demonstrate these capabilities in two scenarios: learning classically intractable probability distributions and learning quantum circuits for accelerated physical simulation. Our results establish that both learning and sampling can be performed efficiently in the beyond-classical regime, opening new possibilities for quantum-enhanced generative models with provable advantage.
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Enhancing Remote Sensing Representations through Mixed-Modality Masked Autoencoding
Ori Linial
Yochai Blau
Nadav Sherman
Yotam Gigi
Wojciech Sirko
Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops (2025), pp. 507-516
Preview abstract
This paper presents an innovative approach to pre-training models for remote sensing by integrating optical and radar data from Sentinel-2 and Sentinel-1 satellites. Using a novel variation on the masked autoencoder (MAE) framework, our model incorporates a dual-task setup: reconstructing masked Sentinel-2 images and predicting corresponding Sentinel-1 images. This multi-task design enables the encoder to capture both spectral and structural features across diverse environmental conditions. Additionally, we introduce a "mixing" strategy in the pretraining phase, combining patches from both image sources, which mitigates spatial misalignment errors and enhances model robustness. Evaluation on segmentation and classification tasks, including Sen1Floods11 and BigEarthNet, demonstrates significant improvements in adaptability and generalizability across varied downstream remote sensing applications. Our findings highlight the advantages of leveraging complementary modalities for more resilient and versatile land cover analysis.
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Scaling Laws for Downstream Task Performance in Machine Translation
Natalia Ponomareva
Hussein Hazimeh
Sanmi Koyejo
International Conference on Learning Representations (ICLR) (2025) (to appear)
Preview abstract
Scaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on studying scaling laws for pretraining (upstream) loss. However, in transfer learning settings, in which LLMs are pretrained on an unsupervised dataset and then finetuned on a downstream task, we often also care about the downstream performance. In this work, we study the scaling behavior in a transfer learning setting, where LLMs are finetuned for machine translation tasks. Specifically, we investigate how the choice of the \emph{pretraining} data and its size affect downstream performance (translation quality) as judged by: downstream cross-entropy and translation quality metrics such as BLEU and COMET scores. Our experiments indicate that the size of the finetuning dataset and the distribution alignment between the pretraining and downstream data significantly influence the scaling behavior. With sufficient alignment, both downstream cross-entropy and translation quality scores improve monotonically with more pretraining data. In such cases, we show that it is possible to predict the downstream translation quality metrics with good accuracy using a log-law. However, there are cases where moderate misalignment causes the downstream translation scores to fluctuate or get worse with more pretraining, whereas downstream cross-entropy monotonically improves. By analyzing these, we provide new practical insights for choosing appropriate pretraining data.
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We propose moving from Software Defined Networks (SDN) to Software Managed Networks (SMN) where all information for managing the life cycle of a network (from deployment to operations to upgrades), across all layers (from Layer 1 through 7) is stored in a central repository. Crucially, a SMN also has a generalized control plane that, unlike SDN, controls all aspects of the cloud including traffic management (e.g., capacity planning) and reliability (e.g., incident routing) at both short (minutes) and large (years) time scales. Just as SDN allows better routing, a SMN improves visibility and enables cross-layer optimizations for faster response to failures and better network planning and operations. Implemented naively, SMN for planetary scale networks requires orders of magnitude larger and more heterogeneous data (e.g., alerts, logs) than SDN. We address this using coarsening - mapping complex data to a more compact abstract representation that has approximately the same effect, and is more scalable, maintainable, and learnable. We show examples including Coarse Bandwidth Logs for capacity planning and Coarse Dependency Graphs for incident routing. Coarse Dependency Graphs improve an incident routing metric from 45% to 78% while for a distributed approach like Scouts the same metric was 22%. We end by discussing how to realize SMN, and suggest cross-layer optimizations and coarsenings for other operational and planning problems in networks.
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LightLab: Controlling Light Sources in Images with Diffusion Models
Nadav Magar
Amir Hertz
Eric Tabellion
Alex Rav Acha
Yedid Hoshen
Arik Shamir
SIGGRAPH Conference Papers '25 (2025)
Preview abstract
We present a simple, yet effective diffusion-based method for fine-grained, parametric control over light sources in an image. Existing relighting methods either rely on multiple input views to perform inverse rendering at inference time, or fail to provide explicit control over light changes. Our method fine-tunes a diffusion model on a small set of real raw photograph pairs, supplemented by synthetically rendered images at scale, to elicit its photorealistic prior for the relighting task. We leverage the linearity of light to synthesize image pairs depicting controlled light changes of either a target light source or ambient illumination. Using this data and an appropriate fine-tuning scheme, we train a model for precise illumination changes with explicit control over light intensity and color. Lastly, we show how our method can achieve compelling light editing results, and outperforms existing methods based on user preference.
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