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 11430 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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Cost-effective Agent Test-Time Scaling
Samira Daruki
I-Hung Hsu
Jin Miao
Tengxiao Liu
2026
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Scaling test-time computation improves performance across different tasks on large language models (LLMs), yet mainstream scaling methods remain challenging for tool-augmented LLM agents. Sequential scaling tends to yield shallow tool use and under-exploration, whereas parallel scaling inflates cost through repeated tool calls. The dual costs of tokens and tool calls further complicate cost accounting and hinder fair comparison. In this work, we study the test-time scaling of widely used, tool-reliant search agents under resource constraints, analyzing performance with a unified cost metric that incorporates both tokens and tool calls. To this end, we propose Cost-effective Agent Test-Time Scaling (CATS), a budget-aware framework designed to support more cost-effective scaling by guiding resource allocation between sequential and parallel exploration. Experiments across search-intensive benchmarks show that CATS produces more favorable scaling curves, attaining higher accuracy with fewer tool calls and lower overall cost. Our work introduces a cost-conscious design for agent test-time scaling and contributes empirical insights that enable a more transparent and principled understanding of scaling in tool-augmented agents.
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GUIDE: A Benchmark for User Context Understanding and Assistance in GUI Workflow Videos
Saelyne Yang
Jaesang Yu
Yi-Hao Peng
Kevin Qinghong Lin
Jae Won Cho
Juho Kim
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2026)
Preview abstract
Graphical User Interface (GUI) agents have the potential to assist users in interacting with complex software. While prior research has primarily focused on automating user actions through clicks and keystrokes, this paradigm overlooks human intention, where users value the ability to explore, iterate, and refine their ideas while maintaining agency.To move beyond automation and toward collaboration, GUI agents must understand what users are doing and why. We introduce GUIDE (GUI Understanding, Intent, and Help Decision Evaluation), a benchmark that evaluates AI models on their ability to perceive user behavior, infer intent, and provide assistance in open-ended GUI tasks. GUIDE consists of 67.5 hours of screen recordings from 120 novice user demonstrations with think-aloud narrations that surface user intent, across 10 complex software (e.g., PowerPoint, Photoshop). GUIDE defines three tasks—(i) Behavior State Detection, (ii) Intent Prediction, and (iii) Help Prediction that test a model’s ability to recognize behavior state, reason about goals, and decide when and how to help. Evaluations across eight state-of-the-art multimodal models reveal that all models struggled with the tasks, achieving only 44.6% and 55.0% accuracy on behavior state and help prediction. However, providing user context such as behavioral state and intent significantly improved the performance, raising help prediction by up to 50.2%. These results highlight the critical role of structured user understanding in effective assistance.Our benchmark provides a path toward GUI agents that go beyond automation to become truly user-aware collaborators.
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Analyzing Bytes: Pre-Disassembly Static Binary Analysis
Soumyakant Priyadarshan
ChenCheng Jiang
R. Sekar
Proceedings of the ACM on Programming Languages, Association for Computing Machinery (2026), pp. 1127-1151
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Binary code analysis plays a central role in numerous applications in software security, performance optimization, reverse engineering, and so on. Existing techniques need to first disassemble binaries into functions in assembly code before an analysis can be performed. However, disassembly and function identification have proven to be major challenges for complex variable-length instruction sets such as the x86. A recent trend has been to use static analysis to improve the accuracy of these tasks. This raises a chicken-and-egg problem: a disassembly is needed for static analysis, but a static analysis is needed for accurate disassembly! We overcome this problem by developing a novel static analysis approach that can operate before committing to a disassembly. Our analysis operates on the output of exhaustive disassembly that considers each possible offset in a binary as an instruction, and constructs what is known as a super-set control-flow graph (CFG). The central technical challenge in analyzing this CFG is that it mixes legitimate instructions with unintended ones, causing analysis results from invalid code paths to pollute legitimate ones. To overcome this challenge, we begin with a key new insight that if we focus on backward analyses, we can ensure accuracy of analysis results at intended instructions even though we have no idea where these intended instructions are! Moreover, our analysis operates in time that is linear in the size of the binary. Specifically, in O(n) total time, it yields analysis results for every one of the n offsets in an n-byte binary. For this task, it is orders of magnitude faster than previous techniques, as the previous techniques typically need to repeat the analysis many times.
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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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Ideation, i. e., generating, capturing, organizing, and evaluating ideas, is foundational to creative practices; yet little is known about how blind and low vision ( BLV) creators engage in ideation. We conducted interviews with 20 BLV creators working in different domains including poetry, music production, tactile graphics, digital drawing, and mixed- media arts. Our findings reveal how participants established accessible creative workflows under social and institutional constraints, drew inspiration from diverse physical and digital sources, captured and organized ideas across text, audio, visual, and physical modalities, and evaluated their work through human and AI feedback. We discuss how ableist norms and accessibility breakdowns cascade across ideation pathways, progressively narrowing BLV individuals’ creative possibilities. Finally, we offer design considerations for building accessible ideation support tools that amplify BLV creators’ distinctive practices.
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In some multi-stage software build pipelines, downstream compiler errors may be reported against ephemeral, machine-generated intermediate artifacts rather than original, human-written source code, which can make remediation challenging. A system and method may address this by intercepting a downstream error, mapping its location back to the original source file, and programmatically injecting a dormant suppression tag into the original source code. During a subsequent build, an intermediate transpiler can propagate this tag into a newly generated intermediate artifact. In the intermediate file, the tag may become active and be recognized by the downstream compiler as a directive to suppress the specific error. This approach can facilitate an automated remediation process for certain build failures that avoids direct modification of ephemeral files and uses the original source code as a record for suppression.
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Online video platforms face an exponential challenge
in detecting and mitigating the flood of AI-generated “slop”
and synthetic spam perpetuated by coordinated malicious actors.
This content is increasingly designed to exploit the limitations
of traditional media forensics, often utilizing generative AI to
produce unique, localized variations of harmful or low-quality
material at scale. Traditional content-centric moderation fails
against this coordinated, adversarial generation strategy.
This paper presents a novel, scalable detection and classifi-
cation framework designed for online video platforms (OVP) to
identify and triage clusters of coordinated accounts exhibiting
a prevalence of adversarial synthetic content. The approach
leverages a multi-faceted architecture incorporating two core
machine learning components: a robust Coordinated Bot-Net
Detector (via Account Relatedness) and a Synthetic Pattern Clas-
sifier. Crucially, we introduce an advanced AI enhancement layer
utilizing Large Language Models (LLMs), specialized via Low-
Rank Adaptation (LoRA) and Automatic Prompt Optimization
(APO), to achieve rapid, high-precision semantic understanding
of emerging synthetic spam trends.
Evaluated across a representative evaluation dataset (N =
16, 250 weekly candidate channels across six major synthetic
abuse verticals), the system demonstrates high precision (FPR <
0.05%) in identifying coordinated synthetic spam networks.
Furthermore, the LLM-driven classification achieves a 74%
automated triage routing rate, saving over 1, 100 operational
review hours per week while reducing investigation turnaround
times by up to 50% (p < 0.001). This work details a critical
system design that provides essential scalability and adversarial
resilience against sophisticated generative attacks.
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Here’s a thought experiment. Say I wave a magic wand across a codebase and an entire class of technical debt, poof, goes away and immediately evaporates if introduced in the future. For example, maybe I make it so that dead feature flags are simply no longer a problem: they just delete themselves as soon as the engineer wills it. Or maybe large-scale migrations just migrate themselves. Maybe we magically have 100% test coverage, without an engineer lifting a finger.
What will happen to developer productivity?
Surely, developer productivity increases overall.
But will the productivity metrics that we all use as a proxy for “developer productivity” move up and to the right. Let’s explore this idea.
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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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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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While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterative error correction. Furthermore, standard single-stream prompting proves brittle when models encounter novel abstractions or rigorous domain constraints. We introduce PoTRE (Poly-Topological Reasoning Ensembles), a heterogeneous framework that decouples inference into four agents: (1) Adversarial Refinement Agent, (2) Hierarchical strategic Planning Agent, (3) Spectrum Search Agent, and (4) Direct Chain Agent. A final Task-Adaptive Aggregation Layer dynamically reconciles these perspectives -- via final candidate selection, semantic synthesis, or neuro-symbolic verification -- to produce a robust global solution. We evaluate PoTRE on three frontier benchmarks: ARC-AGI-2, Humanity's Last Exam (HLE), and PRBench Finance. PoTRE achieves state-of-the-art accuracy of 49.92% on HLE, surpassing the previous best official score. We demonstrate that this architectural heterogeneity achieves improved reasoning performance using similar or fewer inference tokens compared to heavily scaled homogeneous baselines.
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Learning Conditional Averages
Marco Bressan
Nataly Brukhim
Nicolo Cesa-Bianchi
Emmanuel Esposito
Shay Moran
Maximilian Thiessen
COLT (2026)
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We introduce the problem of learning \emph{conditional averages} in the PAC framework.
The learner receives a sample labeled by an unknown target concept from a known concept class, as in standard PAC learning. However, instead of learning the target concept itself, the goal is to predict, for each instance, the average label over its \emph{neighborhood}---an arbitrary subset of points that contains the instance.
In the degenerate case where all neighborhoods are singletons, the problem reduces exactly to classic PAC learning. More generally, it extends PAC learning to a setting that captures learning tasks arising in several domains, including explainability, fairness, and recommendation systems. %including explainability, fairness, and recommendation systems.
Our main contribution is a complete characterization of when conditional averages are learnable, together with sample complexity bounds that are tight up to logarithmic factors. The characterization hinges on the joint finiteness of two novel combinatorial parameters, which depend on both the concept class and the neighborhood system, and are closely related to the independence number of the associated neighborhood graph.
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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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