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 11619 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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Does AI Assistance Enhance or Erode Expertise? Evidence from a Three-Month Field Experiment in Patent Drafting
David Autor
Tanya Rodchenko
Joshua Martin
Zanna Iscenko
Scott Strand
David Pearl
Melissa Ferere
NBER (2026)
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Whether AI assistance builds or erodes professional expertise is unsettled. In a pre-registered three-month randomized controlled trial, we gave 133 practicing patent lawyers at eleven U.S. intellectual property law firms access to a custom AI drafting assistant and measured both their performance while using AI and their professional judgment afterward without it. All work was scored by blinded expert patent attorneys. Paralleling findings from other white-collar domains, AI access raised the quality of work delivered on benchmark patent drafting tasks at 10 days (0.34 SD, p = 0.03) and 90 days (0.38 SD, p = 0.01), with larger gains among junior lawyers. After three months, all subjects redlined an existing patent application without AI, a core task of patent practice requiring expert judgment. Treated lawyers outperformed controls by 0.32 SD (p = 0.04), but this advantage was concentrated entirely among senior lawyers (0.45 SD, p = 0.02). Junior lawyers showed no average gain; their scores instead bifurcated, with sharply fewer mediocre scores offset by more poor and more good ones. The largest gains from AI thus accrued to the lawyers who retained the least. Foundational expertise may be a prerequisite for extracting durable skill from AI-assisted practice.
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Looking to the brain to improve energy efficiency of AI
Taro Toyoizumi
Hakwan Lau
Michał Klincewicz
Seng Bum Michael Yoo
Megan Peters
taylor.w.webb@gmail.com
Current Biology (2026)
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Modern artificial intelligence (AI) systems have achieved remarkable capabilities, but at an extraordinary energy cost. Training and running large-scale models can consume vast resources, posing environmental, economic, and social challenges. In contrast, biological brains perform lifelong learning, adaptive control, and flexible reasoning using orders of magnitude less energy for learning and adaptation over a lifetime. What accounts for this difference -- and how can it guide future AI development? In this article, we identify key biological principles that support energy-efficient capacities in biological brains, and consider how they might inform the design of more sustainable artificial systems. We organize our analysis around three domains: architectural constraints, signaling strategies, and learning algorithms. In each domain, we discuss concrete observations from biology -- from cell to circuit to cognitive level -- and describe how current and emerging AI systems mirror or diverge from these motifs. One striking feature of biological energy optimization is often overlooked: that brains are remarkably stable in their energy usage across heterogeneous modes, suggesting they may minimize energy needs during active environmental processing through maximizing the utility of “rest-like” background processes. Overall, rather than advocating for biomimicry for its own sake, we argue for biologically informed engineering. Understanding how natural systems minimize energetic cost while maximizing flexibility may help us build AI that is not only powerful, but also efficient, equitable, and environmentally responsible.
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Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action-Spaces
Haitong Ma
Ofir Nabati
Bo Dai
Na Li
Shie Mannor
Guy Tennenholtz
Proceedings of the 43rd International Conference on Machine Learning (ICML-26), Seoul, South Korea (2026)
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Reinforcement learning (RL) algorithms have achieved superhuman performance
on many sequential decision-making tasks, but often struggle in domains with
large, combinatorial action spaces. To address this, we introduce a practical and
stable algorithm for training discrete diffusion models to represent policies in
such environments. We formulate a policy mirror descent algorithm that enhances
training stability by reframing policy optimization as an inference problem, which
naturally aligns with the learning objective of discrete diffusion models. Through
extensive experiments on a suite of challenging benchmark tasks, we demonstrate
that our approach achieves significant improvements over existing methods in both
performance and sample efficiency. This work opens a promising new direction
for applying discrete diffusion models in RL to tackle long-standing challenges in
large-scale combinatorial action spaces.
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Block partitioning framework is a core component in any modern video coding standard, as it directly determines the block size used for predictions and transforms. Flexible block partitioning plays a crucial role in the compression efficiency of these standards. This paper provides a technical overview of the block partitioning framework in AV2 video codec, developed by Alliance for Open Media. Partitioning scheme for both coding blocks and transform blocks has been redesigned in AV2. Coding block partitioning is fully recursive with newly designed partitioning options. Also, newly introduced Semi-Decoupled Partitioning (SDP) option provides additional flexibility by allowing luma and chroma components to have decoupled coding block partition trees. On the other hand, the transform block partitioning has been redesigned to use single-level partitioning with more partitioning options. In this paper, we provide a technical overview of the block partitioning framework in AV2 and also provide tool-off test results for several block partitioning aspects.
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We introduce ALPS (Activation-based Length Prediction for Scheduling), a method for predicting LLM generation length from prefill activations before any tokens are generated. Unlike existing approaches that require model fine-tuning or complex entropy-weighted pooling, ALPS uses a simple linear probe on the last-token activation at intermediate layers. We discover that generation length is encoded in prefill representations: a ridge regression probe achieves R-squared > 0.85 across three model families. Validation across Llama-3.1-8B, Gemma-2-9B, and Qwen-2.5-7B demonstrates: (1) intermediate layers generally perform well, with some architectural variation; (2) simple last-token extraction outperforms complex pooling strategies; (3) activations improve substantially over surface-feature baselines (24 percentage points over input length plus lexical features). The best models achieve R-squared = 0.943 (Gemma), R-squared = 0.880 (Llama), and R-squared = 0.857 (Qwen) with MAE of 38-80 tokens. All test prompts terminated naturally (100% EOS), eliminating truncation confounds. While our evaluation uses 200 curated prompts—sufficient for demonstrating the phenomenon but requiring broader validation—cross-validation confirms generalization beyond training data. ALPS enables practical applications including budget-constrained inference, request scheduling, and resource allocation. The probe adds negligible overhead (~16KB direction vector, single dot product), making ALPS practical for production deployment.
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When “Correct” Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?
Corina Pasareanu
Haizhong Zheng
Beidi Chen
Ravi Mangal
Xinyu Yang
James Song
Yibo Peng
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, San Diego, California, United States (2026), 15514–15546
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Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively on functional correctness. In this paper, we reveal a novel type of threat to real-world code agents: Functionally Correct yet Vulnerable (FCV) patches, which pass all test cases but contain vulnerable code. With our proposed FCV-Attack, which can be deliberately crafted by malicious attackers or implicitly introduced by benign developers, we show that SOTA LLMs (e.g., ChatGPT and Claude) and agent scaffolds (e.g., SWE-agent and OpenHands) are all vulnerable to this FCV threat; across 12 agent-model combinations on SWE-Bench, the attack only requires black-box access and a single query to the code agent to perform the attack. For example, for CWE-538 (information exposure vulnerability), the FCV-Attack attains an attack success rate of 40.7% on GPT-5 Mini + OpenHands. Our results reveal an important security threat overlooked by current evaluation paradigms and urge the development of security-aware defenses for code agents.
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“That Experience Still Haunts Me to This Day”: A Multi-Region Survey on AI-Generated Image-Based Sexual Abuse Victimization
Nicola Henry
International Journal of Human-Computer Interaction (2026)
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Image-based sexual abuse (IBSA) refers to the creation, sharing, or threats to share intimate images, whether real or synthetic. Advances in artificial intelligence (AI) now enable the generation of sexually explicit images from non-explicit source material, such as professional headshots and social media profiles. In this study, we found that across three regions (Australia, the United Kingdom, and the United States), 15% of a representative sample reported experiences of IBSA involving digitally altered images, including 6.9% who reported AI-generated IBSA (AI-IBSA) victimization specifically. Higher rates of victimization were observed among respondents under 35, LGBTQ+, and BIPOC participants, people with disabilities, and men. While victimization rates were higher among men, women were more likely to report a broader range of harms and more negative emotional and social impacts. The most common responses to victimization were platform-based actions, including blocking (69%) and reporting (58%). These findings highlight AI-IBSA as a growing and under-recognized form of harm, with important implications for prevention, platform governance, and support for victim-survivors.
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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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Proactive Heterogeneous Interconnect Arbitration via ML-Aware Compiler Hinting
Prakash Viswalingam
2026
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Modern heterogeneous Systems-on-Chip (SoCs) integrate highly diverse intellectual property (IP) blocks, including latency-sensitive CPUs, Throughput-intensive GPUs, real-time streaming display/camera engines, and massive parallel Neural Processing Units (NPUs/TPUs). Arbitrating access to a shared Network-on-Chip (NoC) and memory subsystem under such diverse traffic profiles expose a critical structural bottleneck. Traditional reactive Quality of Service (QoS) and Dynamic Voltage and Frequency Scaling (DVFS) frameworks incur substantial performance and energy penalties due to phase-locked loop (PLL) lag and transient queue saturation. This paper introduces a Compiler-Assisted Interconnect Hinting (CAIH) framework that eliminates reactive latency penalties by exploiting the deterministic nature of deep learning execution graphs. We propose a lightweight Instruction Set Architecture (ISA) extension (ICH) that allows the deep learning compiler to proactively signal upcoming memory phases, operational intensity shifts, and lookahead cycles to a centralized System QoS (SQoS) arbiter via a dedicated physical sideband channel. Experimental results extracted from cycle-accurate gem5/Garnet simulations demonstrate that our proactive framework reduces average CPU memory latency by 22%, completely eliminates display buffer underflows under heavy concurrent workloads, and achieves up to a 15% reduction in LPDDR6 memory subsystem power.
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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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Correct-by-Construction Framework for Robustness Against Divergent Voltage Drops and High Clock Divergence
Prateek Pendyala
Mathew Kaipanatu
2026
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Limitations in Sign-off Methodology:
Traditional STA corner selection 10% lower STA corner from PMIC voltage is selected- design is constantly optimised for 10% lower voltage, thereby failing to build margin against differential drop.
IR aware STA does not account timing path’s geometric imbalances (logic depths), net dominated interconnect skews (net delays & metal layer variation) - all dominant in advanced process nodes. Furthermore, this is workload dependent: fixing IR STA violations does not build margins on unseen vectors.
Frequent Silicon issues due to these gaps:
Low voltage mode scan shift Vmin jumps need to meet slack on paths which become exponentially sensitive to voltage gradients. Even small differential IR drops (capture & launch traversing through contrasting IR hotspot & cool regions) cause catastrophic slack loss
High divergence paths with structural imbalances-where clock paths are net-dominated & are operated at high speeds often fail to meet hold timing, despite good pre-silicon margins due to high interlayer metal-sheet & via resistances in lower process nodes.
Additionally, there is considerable PPA impact -higher dynamic & leakage power in clock & data paths respectively in divergent paths. Our proposed solution aims to address above gaps.
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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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We present MOSAIC-GS, a novel, fully explicit, and computationally efficient approach for high-fidelity dynamic scene reconstruction from monocular videos.
Monocular reconstruction is inherently ill-posed due to the absence of sufficient multiview constraints,
making accurate recovery of object geometry and temporal coherence particularly challenging.
To address this, we leverage multiple geometric cues, such as depth, optical flow, dynamic object segmentation, and tracking trajectories,
combined with rigidity constraints to estimate preliminary 3D scene dynamics during an advanced initialization stage.
Recovering scene flow prior to the photometric optimization phase reduces reliance on motion inference from visual appearance alone, which is often ambiguous in monocular settings.
To enable compact representations, fast training, and real-time rendering while supporting non-rigid deformations, the scene is decomposed into static and dynamic components,
with dynamic trajectories represented as time-dependent Poly-Fourier curves for parameter-efficient motion encoding.
We demonstrate that MOSAIC-GS achieves substantially faster optimization and rendering compared to existing methods,
while maintaining reconstruction quality on par with state-of-the-art approaches across standard monocular dynamic scene benchmarks.
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