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 11056 publications
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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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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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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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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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This tutorial examines the progress and scaling limitations of IM-DD based optical technologies and explores how datacenter use cases optimized coherent technology, including a newly proposed polarization-folding, time-diversity approach and a novel single-sideband coherent detection technology—can address some of these challenges
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SYMBIOSIS: Systems Thinking and Machine Intelligence for Better Outcomes in Society
Sameer Sethi
Donald Martin
Emmanuel Klu
Preprint (2025)
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This paper presents SYMBIOSIS, an AI-powered framework to make Systems Thinking accessible for addressing societal challenges and unlock paths for leveraging systems thinking framework to improve AI systems. The platform establishes a centralized, open-source repository of systems thinking/system dynamics models categorized by Sustainable Development Goals (SDGs) and societal topics using topic modeling and classification techniques. Systems Thinking resources, though critical for articulating causal theories in complex problem spaces, are often locked behind specialized tools and intricate notations, creating high barriers to entry. To address this, we developed a generative co-pilot that translates complex systems representations - such as causal loops and stock-flow diagrams - into natural language (and vice-versa), allowing users to explore and build models without extensive technical training.
Rooted in community-based system dynamics (CBSD) and informed by community-driven insights on societal context, we aim to bridge the problem understanding chasm. This gap, driven by epistemic uncertainty, often limits ML developers who lack the community-specific knowledge essential for problem understanding and formulation, often leading to misaligned causal theories and reduced intervention effectiveness. Recent research identifies causal and abductive reasoning as crucial frontiers for AI, and Systems Thinking provides a naturally compatible framework for both. By making Systems Thinking frameworks more accessible and user-friendly, we aim to serve as a foundational step to unlock future research into Responsible and society-centered AI that better integrates societal context leveraging systems thinking framework and models. Our work underscores the need for ongoing research into AI's capacity essential system dynamics such as feedback processes and time delays, paving the way for more socially attuned, effective AI systems.
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We study the allocation problem in the Massively Parallel Computation (MPC) model. This problem is a special case of b-matching in which the input is a bipartite graph with capacities greater than 1 in only one part of the bipartition. We give a (1 + ϵ) approximate algorithm for the problem, which runs in Õ(√log λ) MPC rounds, using sublinear space per machine and Õ(λn) total space, where λ is the arboricity of the input graph. Our result is obtained by providing a new analysis of a LOCAL algorithm by Agrawal, Zadimoghaddam, and Mirrokni [ICML 2018], which improves its round complexity from O(log n) to O(log λ). Prior to our work, no o(log n) round algorithm for constant-approximate allocation was known in either LOCAL or sublinear space MPC models for graphs with low arboricity.
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Synthesize Privacy-Preserving High-Resolution Images via Private Text Mediators
Haoxiang Wang
Zinan Lin
Da Yu
Huishuai Zhang
2025
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A new framework for generating high-resolution, differentially private (DP) synthetic images. The method summarizes each private image using image-to-text models, applies a modified Private Evolution algorithm to generate DP text, and then reconstructs images via text-to-image models.
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Transformers, while powerful, suffer from quadratic computational complexity and the ever-growing Key-Value (KV) cache of the attention mechanism. This paper introduces Trellis, a novel Transformer architecture with bounded memory that learns how to compress its key-value memory dynamically at test time. Trellis replaces the standard KV cache with a fixed-size memory and train a two-pass recurrent compression mechanism to store new keys and values into memory. To achieve this, it leverages an online gradient descent procedure with a forget gate, enabling the compressed memory to be updated recursively while learning to retain important contextual information from incoming tokens at test time. Extensive experiments on language modeling, common-sense reasoning, recall-intensive tasks, and time series show that the proposed architecture outperforms strong baselines. Notably, its performance gains increase as the sequence length increases, highlighting its potential for long-context applications.
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The application of quantum computers to machine learning tasks is an exciting potential direction to explore in search of quantum advantage. In the absence of large quantum computers to empirically evaluate performance, theoretical frameworks such as the quantum probably approximately correct (PAC) and quantum statistical query (QSQ) models have been proposed to study quantum algorithms for learning classical functions.
Despite numerous works investigating quantum advntage in these models, we nevertheless only understand it at two extremes: either exponential quantum advantages for uniform input distributions or no advantage for potentially adversarial distributions.
In this work, we study the gap between these two regimes by designing an efficient quantum algorithm for learning periodic neurons in the QSQ model over a broad range of non-uniform distributions, which includes Gaussian, generalized Gaussian, and logistic distributions.
To our knowledge, our work is also the first result in quantum learning theory for classical functions that explicitly considers real-valued functions.
Recent advances in classical learning theory prove that learning periodic neurons is hard for any classical gradient-based algorithm, giving us an exponential quantum advantage over such algorithms, which are the standard workhorses of machine learning.
Moreover, in some parameter regimes, the problem remains hard for classical statistical query algorithms and even general classical algorithms learning under small amounts of noise.
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In recent years, deep learning has made remarkable progress in a wide range of domains, with a
particularly notable impact on natural language
processing tasks. One of the challenges associated
with training deep neural networks is the need
for large amounts of computational resources and
time. In this paper, we present Deep Fusion, an efficient approach to network training that leverages
pre-trained initializations of smaller networks.
We show that Deep Fusion accelerates the training process, reduces computational requirements,
and leads to improved generalization performance
on a variety of NLP tasks and T5 model sizes.
Our experiments demonstrate that Deep Fusion
is a practical and effective approach to reduce
the training time and resource consumption while
maintaining, or even surpassing, the performance
of traditional training methods.
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In Julia, JuMP is the go-to modelling package for mathematical optimisation. As of this writing, Google's award-winning solvers have not been accessible through JuMP; which offers Julia's ease of use. ORTools.jl is changing this. Julia users will now have access to Google's Glop, CP-SAT, and PDLP solvers through JuMP as provided by the ORTools.jl package.
This talk offers an introduction to the features of the package and an overview of the difficulties we encountered.
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MaRVL-QA: A Benchmark for Mathematical Reasoning over Visual Landscapes
Nilay Pande
Sahiti Yerramilli
Jayant Tamarapalli
Rynaa Grover
arXiv preprint arXiv:2508.17180 (2025)
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A key frontier for Multimodal Large Language Models (MLLMs) is the ability to perform deep mathematical and spatial reasoning directly from images, moving beyond their established success in semantic description. Mathematical surface plots provide a rigorous testbed for this capability, as they isolate the task of reasoning from the semantic noise common in natural images. To measure progress on this frontier, we introduce MaRVL (Mathematical Reasoning over Visual Landscapes), a new benchmark designed to quantitatively evaluate these core reasoning skills. The benchmark comprises two novel tasks: Topological Counting, identifying and enumerating features like local maxima; and Transformation Recognition, recognizing applied geometric transformations. Generated from a curated library of functions with rigorous ambiguity filtering, our evaluation on MaRVL reveals that even state-of-the-art MLLMs struggle significantly, often resorting to superficial heuristics instead of robust spatial reasoning. MaRVL provides a challenging new tool for the research community to measure progress, expose model limitations, and guide the development of MLLMs with more profound reasoning abilities.
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Computer use agents (CUAs) need to plan long-horizon task workflows grounded in diverse, ever-changing applications and environments, but learning is hindered by the scarcity of large-scale, high-quality training data. Existing datasets are small, domain-specific, and costly to annotate, while current synthetic data generation methods often yield brittle, simplistic, or misaligned task demonstrations.
We introduce Watch & Learn (W&L), a framework that transforms human demonstration videos available in the Internet into executable UI trajectories at scale. Inspired by robotics, we train an inverse dynamics model that accurately predicts user actions from consecutive screens, bypassing the need for complex heuristics. To scale to the web, we curate a large state-transition corpus and design a retrieval framework that identifies relevant video tutorials, enabling automatic conversion of raw videos into structured UI trajectories without requiring manual annotations. Beyond training data, we show that the generated UI trajectories can also serve as in-context exemplars, providing CUAs with long-horizon priors and domain-specific knowledge at inference time.
On the challenging OSWorld and Mind2Web benchmarks, UI trajectories extracted with W&L consistently improve both general-purpose and state-of-the-art frameworks when used in-context, and delivers stronger gains for open-source models when used in training. These results highlight web-scale human demonstration videos as a practical and scalable foundation for advancing CUAs towards real-world deployment.
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