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 11522 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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A 3D Scene Graphs Survey: Open Challenges and Future Directions
Dennis Rotondi
Francesco Argenziano
Sebastian Koch
Nathan Hughes
Martin Büchner
Johanna Wald
Lukas Schmid
Daniele Nardi
Abhinav Valada
Liam Paul
Luca Carlone
Kai Arras
Annual Review of Control, Robotics, and Autonomous Systems (ARCRAS), 10 (2027) (to appear)
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3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Their expressiveness has made them relevant to a broad range of problems in robotics and computer vision, including mapping, task and motion planning, scene understanding, and many others. However, the field remains fragmented: different communities adopt distinct formulations, construction pipelines, and evaluation protocols, making it difficult to compare methods, identify common assumptions, and assess remaining challenges for robust real- world deployment. This survey provides a unified and critical review of 3DSGs, with particular emphasis on open challenges and future directions. We first formalize 3DSGs under a common definition and analyze the principal modeling choices that characterize existing formulations, including node and edge attributes, hierarchical structure, dynamic scene representations, and affordance-aware extensions. We then review how 3DSGs are constructed from raw sensory observations, covering both learning-oriented and construction-oriented systems. Finally, we examine downstream applications and evaluation strategies, from intrinsic graph quality to task-level performance. To support the community, we also provide a dedicated website that organizes and extends the surveyed works.
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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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Evaluating Contextual Illegality: AI Compliance in Corporate Law Scenarios
Hilal Aka
Joe Kwon
Noam Kolt
Forty-third International Conference on Machine Learning (2026)
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AI models readily refuse explicitly unlawful requests, but real-world illegality often depends on context. We evaluate frontier models on contextual illegality across four corporate law domains in which routine actions—editing documents, trading stock, requesting payment, approving communications—become unlawful due to triggers such as pending investigations or bankruptcy filings. We study both chat and agentic settings and compare results to a human baseline. The best-performing models achieved near-zero compliance with illegal requests while maintaining high compliance with legal ones, though performance varied sharply by domain. We also identify distinct failure modes such as excessive refusal of legal requests and find improved performance from reasoning models and agentic environments. By utilizing the structure of contextual illegality to create controlled evaluations, our methodology provides empirical grounding for emerging research on law-following AI and extends naturally to additional legal domains.
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Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT
Michelangelo Conserva
Alex Wilson
Anna Michalak
Phil Brodrick
Andrew Thorpe
Proceedings of the National Academy of Sciences (2026)
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Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system inefficiencies. Global tracking with imaging spectroscopy is just becoming feasible, but still largely relies on manual tracking. Here we present the Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) model, an end-to-end vision transformer framework that advances the state of the practice by directly utilizing the complete radiance spectrum from the Earth Surface Mineral Dust Source Investigation (EMIT) instrument to jointly retrieve methane enhancements across all pixels within a scene. This approach joins spectral information content - where the methane signature resides - with essential spatial context to significantly lower detection limits. MAPL-EMIT simultaneously supports quantification, plume delineation, and source localization, even for multiple overlapping plumes. The model was trained on 3.6 million physics-based synthetic plumes injected into global EMIT radiance data. Evaluation against synthetic observations confirms the model’s ability to identify plumes with high recall and precision and to capture weaker plumes relative to existing matched-filter approaches. On real-world benchmarks, MAPL-EMIT captures 79% of known hand-annotated NASA L2B plume complexes across a test set of 1084 EMIT granules, while identifying twice as many plausible plumes than identified by human analysts. Further validation against coincident airborne data, top-emitting landfills, and controlled release experiments confirms the models efficacy at identifying previously uncaptured sources. By incorporating model-generated metrics such as spectral fit scores and estimated noise levels, the framework can further limit false-positive rates. Overall, MAPL-EMIT enables high-throughput implementation on the full EMIT data catalog, shifting methane monitoring from labor-intensive workflows to a rapid, scalable paradigm for facility-level accountability.
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In Search of "Weird Corners": Diagnosing the Limits of Convergent AI in Professional Creative Practice
Mark Williams
2026
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Generative AI assistants typically employ convergent interaction paradigms to resolve ambiguity. While effective for technical tasks, this risks premature convergence in creative domains, constraining output variance. Evaluating a convergent AI probe with expert creatives (N=9) indicates an interactional paradox: structural linearity provides "ignition" utility for early ideation, but misaligns with organic workflows, often inducing "aesthetic sanitization" that standardizes individualized nuance. Prioritizing constructive friction over default agreement, the experts requested active, lateral collaborators. In response, we reframe output convergence as a "full-stack" UI challenge, advocating for Generative frameworks that operationalize the Double Diamond via fluid role-shifting and productive tension.
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The Synthetic Gap: Automating Forensic Investigation of "AI Slop" with the Scaled Abuse Forensics Examiner (SAFE)
Vahid Jalali
Longling Wang
Geethik Narayana Kamineni
Utkarsh Chaudhary
Crystal Zhao
Lucas Liu
2026
Preview abstract
Generative AI capabilities have enabled malicious actors to flood online platforms with "AI slop"—mass-produced, low-quality synthetic media designed to overwhelm traditional integrity systems. These adversarial campaigns often utilize coordinated networks to distribute unique, localized variations of synthetic content, rendering static detection methods ineffective. The signals to detect coordination often have recall gaps. The content is not exactly duplicative to be in the same repetitive video cluster. The abusers however show similar patterns of behavior which need forensics. Manual forensic investigations cannot scale to match the velocity of these generative attacks.
To address this, we present SAFE (Scaled Abuse Forensics Examiner), an automated multi-agent architecture designed for the scalable forensics of adversarial synthetic media. The system decomposes the investigation process into specialized agents: a Cluster Understanding Agent specialized in analyzing the relations between channels in a cluster, a Behavior Understanding Agent that identifies inorganic spatiotemporal patterns, and a Content Understanding Agent that utilizes LoRA-adapted Large Language Models (LLMs) and few-shot learning to detect existing policy violations and spirit of the policy violations respectively . A Root Agent synthesizes these multimodal signals to render a final verdict. Early deployment results indicate that SAFE significantly accelerates the identification of novel synthetic threats, reducing forensic investigation time compared to human-in-the-loop workflows.
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Shuffles of Context-Free Languages along Regular Trajectories
Corentin Barloy
Michaël Cadilhac
Kyle Ockerlund
2026
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In single-core processors, concurrency requires that multiple processes be interleaved into a single thread of execution by a scheduler. The language-theoretic operation that corresponds to this is the shuffle of two languages: the set of words obtained by interleaving a word from each language in an arbitrary, letter-wise fashion. It is well known that regular languages are closed under shuffles, while context-free languages (CFLs) are not.
Following an established line of research, this paper considers shuffles according to regular "trajectories," that is, subject to scheduling constraints expressed by an automaton. Unsurprisingly, some trajectories allow for CFLs to be shuffled into CFLs (e.g., simple concatenation of the two words), while others do not. This paper provides a robust toolset to show that a given trajectory would always shuffle two nonregular CFLs into a nonCFL. In the case of deterministic CFLs (DCFLs), a salient trichotomy of trajectories depending on how they shuffle DCFLs is provided.
These results are based on lemmata of independent interest regarding how pushdown automata (PDA) must invoke the stack when accepting a nonregular CFL or DCFL. The latter case relies on a recent result of Jančar and Šíma (MFCS'2021); answering an open question therein, it is demonstrated that said result cannot be generalized to arbitrary CFLs, leading to dedicated machinery for both cases.
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Uncovering Relationships between Android Developers, User Privacy, and Developer Willingness to Reduce Fingerprinting Risks
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26) (2026)
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The major mobile platforms, Android and iOS, have introduced changes that restrict user tracking to improve user privacy, yet apps continue to covertly track users via device fingerprinting. We study the opportunity to improve this dynamic with a case study on mobile fingerprinting that evaluates developers’ perceptions of how well platforms protect user privacy and how developers perceive platform privacy interventions. Specifically, we study developers’ willingness to make changes to protect users from fingerprinting and how developers consider trade-offs between user privacy and developer effort. We do this via a survey of 246 Android developers, presented with a hypothetical Android change that protects users from fingerprinting at the cost of additional developer effort.
We find developers overwhelmingly (89%) support this change, even when they anticipate significant effort, yet prefer the change be optional versus required. Surprisingly, developers who use fingerprinting are six times more likely to support the change, despite being most impacted by it. We also find developers are most concerned about compliance and enforcement. In addition, our results show that while most rank iOS above Android for protecting user privacy, this distinction significantly reduces among developers very familiar with fingerprinting. Thus there is an important opportunity for platforms and developers to collaboratively build privacy protections, and we present actionable ways platforms can facilitate this.
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Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models
Adel Javanmard
Baharan Mirzasoleiman
arXiv preprint arXiv:2603.01293 (to appear in ICML 2026) (2026)
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We will study a theoretical model (one layer linear self attention with in-context linear task) to rigorously explain several observations (already reported in previous empirical work in the literature) about the characteristics of pretraining and post-training data that are relevant for good performance. Our analysis explain the importance of having a balanced diverse data in pretraining, and hard small set of examples for SFT, and large volume data fro RL. The experiments are on synthetic toy settings and on GPT-2 with synthetic linear regression data.
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Can AI systems be aligned to human values? The popularization of large language models (LLMs) and multi-modal foundation models
has seen a commensurate rise in ways these models cause harm, spanning areas from toxic speech and hallucinations to AI agents
executing unauthorized actions. Given that these models are probabilistic and general-purpose by nature, it is impossible to enumerate
all possible uses and outputs of the model to reach a fully aligned end state. Within the field of AI safety, these harmful instances are
often framed as “the alignment problem,” of models being “misaligned” with human values. Researchers have responded by pursuing
applied and theoretical AI “value alignment” efforts, often without specifying what they mean by human values. How does the field of
AI value alignment conceive of human values? How are these conceptions of values technically operationalized and evaluated? What
does the emergent theory of value from this field signify for the future of AI?
The study of human values has long been part of many academic disciplines outside of computer science, yet these disciplines
are seldom consulted in AI alignment. Building on the theoretical insights of Zhi-Xuan’s (2024) "preferentist paradigm" critique, we
conduct a review of influential AI alignment literature. We also draw from conceptions of human values from philosophy, anthropology,
and sociology, to create an analytical schema. We annotated 94 AI value alignment research papers to discern their implicit theory of
values in AI. The majority do not define values, relying heavily on “preferences” as a stand-in that runs the risk of reducing complex,
culturally situated concepts down to binary choices. As researchers dispense with using human annotators for model training and
evaluation, turning instead to synthetic data and LLM-as-a-judge approaches to aligning and evaluating models, we identify the
potential to close off alternative methods for contesting and enacting values in foundation models. Overall, value alignment is often
reduced to an exercise in utility maximization, which we argue abstracts human values away from their lived context. In making AI
value alignment’s philosophical commitments explicit, we seek to bring greater specificity and under-explored perspectives into the
debate on whether and how AI can address human values
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We revisit the distributed counting problem, where a server must continuously approximate the total number of events occurring across $k$ sites while minimizing communication. The communication complexity of this problem is known to be $\Theta(\frac{k}{\eps}\log N)$ for deterministic protocols. Huang, Yi, and Zhang (2012) showed that randomization can reduce this to $\Theta(\frac{\sqrt{k}}{\eps}\log N)$, but their analysis is restricted to the oblivious setting, where the stream of events is independent of the protocol's outputs.
Xiong, Zhu, and Huang (2023) presented a robust protocol for distributed counting that removes the oblivious assumption. However, their communication complexity is suboptimal by a polylog(k) factor and their protocol is substantially more complex than the oblivious protocol of Huang et al. (2012). This left open a natural question: could it be that the simple protocol of Huang et al. (2012) is already robust?
We resolve this question with two main contributions. First, we show that the protocol of Huang et al. (2012) is itself not robust by constructing an explicit adaptive attack that forces it to lose its accuracy. Second, we present a new, surprisingly simple, robust protocol for distributed counting that achieves the optimal communication complexity of $O(\frac{\sqrt{k}}{\eps} \log N)$. Our protocol is simpler than that of Xiong et al. (2023), perhaps even simpler than that of Huang et al. (2012), and is the first to match the optimal oblivious complexity in the adaptive setting.
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Ten Insights from Other Domains That Inform Responsible AI Frameworks
Allison Woodruff
Angela McKay
Dunstan Allison-Hope
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (2026), 104–115
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The rapid growth of AI systems is being accompanied by new guidelines, principles, standards, regulations, and best practices (hereafter “frameworks”) that seek to ensure the responsible design, development, deployment, and use of AI systems.
Our premise is that the substance, implementation, and evolution of these AI frameworks can be informed by the practical experience of pursuing similar desired outcomes in other relevant domains (e.g., content moderation, human rights, climate change). This will help ensure that mistakes are not repeated and more rapid progress is made.
We used a “repetition test” to generate the following ten insights from other domains. Insights passing the “repetition test” are those that experts with thousands of hours of practical experience often repeat when describing the best practices that have emerged from their domain.
AI frameworks can draw from these ten insights, rather than invent entirely new approaches.
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A Computer Vision Problem in Flatland
Erin Connelly
Annalisa Crannell
Timothy Duff
Rekha R. Thomas
SIAM Journal on Applied Algebra and Geometry, 10 (2026), pp. 14-45
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When is it possible to project two sets of labeled points of equal cardinality lying in a pair of projective planes to the same image on a projective line? We give a complete answer to this question, obtaining the following results. We first show that such a pair of projections exist if and only if the two point sets are themselves images of a common point set in projective space. Moreover, we find that for generic pairs of point sets, a common projection exists if and only if their cardinality is at most seven. In these cases, we give an explicit description of the loci of projection centers that enable a common image.
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