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 11435 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 the fair allocation of indivisible goods with variable groups. In this model, the goal is to partition the agents into groups of given sizes and allocate the goods to the groups in a fair manner. We show that for any number of groups and corresponding sizes, there always exists an envy-free up to one good (EF1) outcome, thereby generalizing an important result from the individual setting. Our result holds for arbitrary monotonic utilities and comes with an efficient algorithm. We also prove that the EF1 existence can be guaranteed even when the goods lie on a path and each group must receive a connected bundle. In addition, we consider a probabilistic model where the utilities are additive and drawn randomly from a distribution. We show that if there are n agents and the number of goods m is divisible by the number of groups k, then an envy-free outcome exists with high probability if m = ω(log n), and this bound is tight. On the other hand, if m is not divisible by k, then an envy-free outcome is unlikely to exist as long as m = o(√n).
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An AI system to help scientists write expert-level empirical software
Eser Aygün
Anastasiya Belyaeva
Gheorghe Comanici
Hao Cui
Renee Johnston
Zahra Shamsi
David Smalling
James Thompson
Sarah Martinson
Lai Wei
Yuchen Zhou
Qian-Ze Zhu
Matthew Abraham
Erica Brand
Anna Bulanova
Jeffrey Cardille
Chris Co
Scott Ellsworth
Grace Joseph
Malcolm Kane
Ryan Krueger
Johan Kartiwa
Jackson Cui
Paul Raccuglia
Julie Wang
Kat Chou
James Manyika
Lizzie Dorfman
Shibl Mourad
Nature (2026)
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The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. To address this, we present Empirical Research Assistance (ERA), an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. ERA achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a diverse range of tasks. In bioinformatics, ERA discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, ERA generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. ERA also produced expert-level software for geospatial analysis, neural activity prediction in zebrafish, and numerical solution of integrals, and a novel rule-based construction for time series forecasting. By devising and implementing novel solutions to diverse tasks, ERA represents a significant step towards accelerating scientific progress.
Keywords: Tree Search, Generative AI, Scorable Scientific Tasks, Empirical Software
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The Abkhaz-Adyghe and Nakh-Daghestanian language families encompass 35 living languages that possess arguably the most complex modern Cyrillic orthographies due to their very sophisticated phonology. The relevant online data displays idiosyncratic patterns among which the use of confusable characters in input methods is the most prevalent. This work studies one such character---letter \emph{palochka}---that is shared by most writing systems in question. We investigate whether the patterns including variants of this character alone can act as language data markers when mining these languages in a large-scale web-crawled data. Using a wide-coverage off-the-shelf LID model (GlotLID) we further investigate the data mined using such patterns and estimate the effects of confusable character normalization on quality of paragraph-level LID predictions in 14 supported languages. According to GlotLID, the normalization significantly increases the recall (discovery of new language data) for some languages while degrading it for others. However, manual evaluation reveals that only 41\% of wins and 46\% of losses are accurate due to GlotLID prediction errors. We argue that despite finding useful signal higher precision LID approaches tailored to these long-tail languages are needed to improve the quality of mined data.
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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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Systems for escalating interactions from automated agents to human agents can create inefficiencies, for example, by transferring unstructured transcripts. An intermediary system can employ a generative artificial intelligence synthesis engine to process the context of an automated interaction upon an escalation trigger. The engine may analyze the dialogue transcript, user metadata, and the automated agent's internal state to perform semantic abstraction, diagnose potential failure points, and infer a possible resolution. The system can then generate a structured briefing for the human agent, which could include a concise summary, a failure diagnosis, or a recommended next action presented as an interactive element. This process may facilitate a more efficient handoff and contribute to an improved escalation workflow by providing the human agent with synthesized, contextual information.
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A Dynamic Numerical Model for Real-Time Estimation of Latent Cognitive States Using Oculomotor Metrics
Diako Mardanbegi
ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 22087-22091
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Estimating internal cognitive states from oculomotor data is fundamentally challenging due to their context-dependency and the complex relationship between various metrics. This paper proposes a dynamic numerical framework to model a task-specific behavioral signature and monitor deviations from it in real-time. The model integrates key oculomotor and motion metrics into a differential equation, yielding a continuous score that serves as an estimate of a latent cognitive state. By using tunable, heuristic parameters, our approach offers a transparent and adaptable alternative to opaque machine learning models. The framework’s strength lies in its ability to pinpoint objective changes in behavior, providing a potential tool for interpreting events like the onset of fatigue, distraction, or cognitive load.
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We study the problem of generating synthetic data under differential privacy. We establish fixed-parameter tractability (FPT) for this problem where the parameter is the treewidth of the query family’s incidence graph. Our algorithms attain optimal error rates across all regimes and are realized by two different approaches: the first is based on linear programming (LP) and the FPT of the separation problem for the LP dual; the second is based on a subsampled private multiplicative weights method, where we obtain FPT for sampling from Gibbs distributions. Both approaches are unified by a dynamic programming framework over a tree decomposition.
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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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Abstain and Validate: A Dual-LLM Policy for Reducing Noise in Agentic Program Repair
José Cambronero
Renyao Wei
Grant Uy
Chin-Jung Liu
Shiying Pan
Satish Chandra
ICSE-SEIP '26: Proceedings of the IEEE/ACM 48th International Conference on Software Engineering: Software Engineering in Practice, ACM (Association for Computing Machinery), New York, NY, USA (2026), 118 – 129
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Agentic automated program repair (APR) is increasingly being used to tackle complex, repository-level bugs in both academic and industry settings. However, changes made by the agent still need to be reviewed by a human before committing them to the codebase to ensure the change correctly fixes the bug. Showing unlikely patches to developers can lead to substantial noise, wasting valuable developer time and decreasing trust in all automated code changes. We introduce two complementary LLM-based policies to reduce such noise: bug abstention and patch critic policies. Bug abstention allows us to exclude bugs that the agentic APR system is unlikely to fix and the patch critic rejects patches that are unlikely to be a good fix for the given bug. We evaluate both policies on three sets of bugs collected from Google’s codebase and agent trajectories generated by an agentic APR system in use at Google. We introduce filtered success rates, where we compute performance after removing trajectories rejected by our policies. On a set of 174 human-reported bugs, abstention and critic can raise filtered success rates up to 13 percentage points and 15 percentage points, respectively, and up to 29 percentage points in combination. For null pointer exceptions and sanitizer-reported bugs with machine-generated bug reports or known reproduction tests, we show the same critic policy can be used to improve average single-sample success rates.
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From Correctness to Collaboration: A Human-Centered Taxonomy of AI Agent Behavior in Software Engineering
Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26), ACM, New York, NY, USA (2026)
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The ongoing transition of Large Language Models in software engineering from code generators into autonomous agents requires a shift in how we define and measure success. While models are becoming more capable, the industry lacks a clear understanding of the behavioral norms that make an agent effective in collaborative software development in the enterprise. This work addresses this gap by presenting a taxonomy of desirable agent behaviors, synthesized from 91 sets of user-defined rules for coding agents. We identify four core expectations: Adhere to Standards and Processes, Ensure Code Quality and Reliability, Solve Problems Effectively, and Collaborate with the User. These findings offer a concrete vocabulary for agent behavior, enabling researchers to move beyond correctness-only benchmarks and design evaluations that reflect the realities of professional software development in large enterprises.
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Using generative artificial intelligence with sensitive data may present challenges, as transmitting personally identifiable information or protected health information to third-party providers can introduce security risks, and some data masking techniques can reduce reasoning capabilities. A described system uses a proxy, masking layer that can intercept data within an enterprise's secure perimeter. This layer can substitute sensitive strings with persistent, structured semantic tokens that may be enriched with non-sensitive metadata hints to help preserve context. An external artificial intelligence can perform reasoning on this abstracted data, and its tokenized response can be re-hydrated into readable text on a client device (e.g., a smartphone, computer, or wearable device). This approach may allow third-party models to reason on proprietary information without direct access to the underlying plaintext data, which can assist organizations in managing data sovereignty while maintaining functional utility.
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Nudging Developers Toward Privacy: Evaluating the Impact of Personalized App Review Reports
Omer Akgul
Michelle L. Mazurek
USENIX Symposium on Usable Privacy and Security (SOUPS) (2026)
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Mobile application developers often struggle to create accurate privacy notices or implement robust privacy practices due to limited expertise or resources. While users share unsolicited privacy feedback in app reviews, and prior research has characterized this privacy feedback, uncovering developer reactions to this feedback remains unexplored. This study explores whether personalized privacy review reports---summarizing real user feedback for a developer's own app---can effectively nudge them toward planning privacy improvements. We surveyed 42 app developers, presenting them with reports containing privacy themes, temporal trends, peer benchmarks, and emotion distributions derived from their apps' reviews. Our findings indicate that these privacy report interventions proved highly effective, with 76% (32 of 42) of participants finding at least one section of the report useful. Furthermore, exposure to the report increased the participants' intent to pursue privacy-relevant actions -- such as reorganizing the UI, enhancing privacy communications, or adding/removing features -- with 69% (29 of 42) of participants indicating an increased intent to do so. Almost all developers expressed a desire to receive such privacy reports periodically or on demand. These results indicate that making this style of report broadly available across the industry could foster a more privacy-conscious mobile ecosystem.
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A Theoretical Framework for Modular Learning of Robust Generative Models
Proceedings of the 43rd International Conference on Machine Learning (ICML 2026)
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Training large-scale generative models is resource-intensive and relies heavily on heuristic dataset weighting. We address two fundamental questions: Can we train Large Language Models (LLMs) modularly, combining small, domain-specific experts to match monolithic performance, and can we do so robustly for any data mixture, eliminating heuristic tuning? We present a theoretical framework for modular generative modeling where a set of pre-trained experts are combined via a gating mechanism. We define the space of normalized gating functions $\mathcal{G}_{1}$ and formulate the problem as a minimax game to find a single robust gate that minimizes divergence to the worst-case data mixture. We prove the existence of such a robust gate using Kakutani's fixed-point theorem and show that modularity acts as a strong regularizer, with generalization bounds scaling with the lightweight gate's complexity. Furthermore, we prove that this modular approach can theoretically outperform models retrained on aggregate data, with the gap characterized by the Jensen-Shannon Divergence. Finally, we introduce a scalable Stochastic Primal-Dual algorithm and a Structural Distillation method for efficient inference. Empirical results on synthetic and real-world datasets confirm that our modular architecture effectively mitigates gradient conflict and can robustly outperform monolithic baselines.
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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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