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.
Sort By
1 - 15 of 11673 publications
Preview abstract
Modern transformers have been scaled up in size, data, training-time compute, and more recently test-time compute based on Chain-of-Thought (CoT), but the consequences of trading off these parameters are unknown. We construct explicit tradeoffs between two of these aspects: the number of layers in a transformer and the number of CoT tokens. Our first result is general: with mild assumptions and a single-token prompt, for any integer k, a transformer using soft attention and SwiGLU MLP layers with L layers and n CoT tokens can be converted to a transformer with at most ckL layers and ⌈n/k⌉ CoT tokens, and vice versa, although with an increase to the size of other parameters. We then a multi-token prompt path following task, finding that additional preprocessing from the multi-token prompt changes the depth-CoT relationship in this case. We construct an attention-only network with L layers and 2^(L−2) heads that can traverse 2^(L−1) − 1 edges along a path with a single CoT token, demonstrating at least an exponential relationship between increases in depth and reductions in the number of CoT tokens needed to traverse the entire path. This relationship for the path following task holds empirically as well, as demonstrated by training networks with up to 5 layers, with the tradeoff between depth and the maximum number of edge traversals in a single CoT token exactly matching our theoretical constructions.
View details
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)
Preview abstract
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.
View details
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)
Preview abstract
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.
View details
Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
Han Zhou
Shariq Iqbal
Ivan Vulić
Anna Korhonen
International Conference on Learning Representations (ICLR) (2026)
Preview abstract
Large language models (LLMs), employed as multiple agents that interact and collaborate with each other, have excelled at solving complex tasks. The agents are programmed with {prompts} that declare their functionality, along with the {workflows} that orchestrate interactions within a structured flow. Designing prompts and workflows for multi-agent systems is inherently complex, especially when addressing a new task. It often demands expert-level knowledge and involves significant trial and error. Gaining a deep understanding of the factors that contribute to effective multi-agent systems is essential for automating the entire process. Motivated by this, we first conduct an in-depth analysis of the design spaces for multi-agent systems, focusing on the impact of prompts, scaling the number of agents, and common types of agentic modules. Our findings reveal that top-performing systems often emerge from simpler design spaces, where prompts play a critical role in enhancing agent functionality and enabling more effective scaling. Based on the insights, we propose Multi-Agent System Search (MASS), a multi-stage optimization framework that performs the optimization in a pruned design space, with prompts and an influential subset of modules. We show that MASS-optimized multi-agent systems outperform existing alterntives by a substantial margin. Based on the MASS-found systems, we finally propose design principles behind building effective multi-agent systems.
View details
Preview abstract
In a prior column, we wrote about how measuring productivity can be viewed as a form of modeling and that all models are wrong, but some are useful. That discussion centered on the idea of ensuring that a productivity model was inclusive of multiple metrics and that those metrics covered the various facets of productivity and covered each facet reasonably well. In that article, we set aside the question of what makes a good individual productivity metric that can be combined with others into a (hopefully) useful model of productivity. In this article, we’ll share some things we consider when building an individual metric, including an example of a novel metric we built in the aftermath of the COVID pandemic.
View details
AI, Identity, and Ethical Governance: Building Trust in High-Stakes Systems
Ibrahim Waziri Jr.
Abhilasha Bhargav - Spantzel
RSAC (2026)
Preview abstract
As AI redefines identity verification in high stakes systems, it introduces novel risks like deepfake fraud and algorithmic bias, creating a critical trust deficit. This session will provide a practical framework for ethical governance, equipping leaders to build and manage secure, fair, and fundamentally trustworthy AI systems by design.
View details
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi
Guy Tennenholtz
Jihwan Jeong
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL-26), Rabat, Morocco (2026), pp. 5270-5304
Preview abstract
LLM-based user simulators are a scalable solution for improving conversational AI, but a critical realism gap undermines their effectiveness. To close this gap, we introduce a framework for building and validating high-fidelity simulators. We present a novel dataset of human-AI shopping conversations designed to capture a wide spectrum of user experiences. To measure fidelity, we propose a hybrid evaluation protocol that combines statistical alignment with a learned, discriminator-based Human-Likeness Score. Our most sophisticated simulator, trained via reinforcement learning with iterative critique, achieves a significant leap in realism. Critically, we demonstrate through counterfactual validation that our simulator—trained exclusively on optimal interactions—realistically adapts its behavior to suboptimal system responses, mirroring real user reactions and marking a key advance in creating reliable simulators for robust AI development.
View details
Ten Insights from Other Domains That Inform Responsible AI Frameworks
Patrick Gage Kelley
Allison Woodruff
Angela McKay
Dunstan Allison-Hope
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (2026), 104–115
Preview abstract
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.
View details
Preview abstract
While modern text-to-image (T2I) models excel at generating high-quality images, they are typically trained to optimize with respect to generalized, population-level preferences. This homogeneous approach ignores the diverse, individual tastes and aesthetic judgments of different users. In this work, we propose a novel framework that learns fine-grained user preferences without relying on computationally expensive visual language models (VLMs) or prompt-sensitive text profiles. Instead, we introduce a robust, continuous user representation that models a user’s reward function as a linear combination of K base user types. We learn user-specific weights $\lambda_u$ via logistic regression on pairwise preference data to construct a continuous user embedding. This embedding is integrated into the diffusion process via an IP-Adapter, and fine-tuned using Diffusion-DPO. Our approach consistently generates images aligned with individual reward functions, achieving a 66.2% win rate against a pre-trained SDXL baseline and a 63.2% win rate against the state-of-the-art PPD framework.
View details
Digital Shadow AI Risk Theoretical Framework (DART): A Framework for Managing Data Disclosure and Privacy Risks of AI tools at Work
Master's Thesis (2026)
Preview abstract
The accelerated integration of generative AI technologies and agentic AI tools, particularly those like ChatGPT, into workplace settings has introduced complex challenges concerning data governance, regulatory compliance, and organizational privacy (GDPR 2016; CCPA/CPRA). This study introduces the Digital Shadow AI Risk Theoretical Framework (DART)—a novel theoretical framework designed to systematically identify, classify, and address the latent risks arising from the widespread, and often unregulated, use of AI systems in professional environments (NIST, 2023; OECD AI Policy Observatory, 2023). DART introduces six original, interrelated constructs developed in this study: Unintentional Disclosure Risk, Trust-Dependence Paradox, Data Sovereignty Conflict, Knowledge Dilution Phenomenon, Ethical Black Box Problem, and Organizational Feedback Loops. Each construct reflects a unique dimension of risk that emerges as organizations increasingly rely on AI-driven tools for knowledge work and decision-making.
The framework is empirically tested through a mixed-methods research design involving hypothesis testing and statistical analysis of behavioral data gathered from cross-sectional surveys of industry professionals. Two cross-industry surveys (Survey-1: 416 responses, 374 analyzed; Survey-2: 203 responses, 179 analyzed) and CB-SEM tests supported seven of eight hypotheses; H4 (sovereignty) was not significant; H7 (knowledge dilution) was confirmed in replication. The findings highlight critical gaps in employee training, policy awareness, and risk mitigation strategies—underscoring the urgent need for updated governance frameworks, comprehensive AI-use policies, and targeted educational interventions. This paper contributes to emerging scholarship by offering a robust model for understanding and mitigating digital risks in AI-enabled workplaces, providing practical implications for compliance officers, risk managers, and organizational leaders aiming to harness the benefits of generative AI responsibly and securely. The novelty of DART lies in its explicit theorization of workplace-level behavioral risks—especially Shadow AI, which unlike Shadow IT externalizes organizational knowledge into adaptive systems—thereby offering a unified framework that bridges fragmented literatures and grounds them in empirical evidence.
View details
Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion
Pengcheng Jiang
Judith Yue Li
Moonkyung Ryu
R. Lily Hu
Kun Su
Liam Hebert
Hao Peng
Jiawei Han
Dima Kuzmin
2026
Preview abstract
Many modern retrieval problems are set-valued: given a broad intent, the system must return a collection of results that optimizes higher-order properties (e.g., diversity, coverage, complementarity, coherence) while staying grounded to a fixed database. Set-valued objectives are inherently non-decomposable and are not captured by existing supervised (query, content) datasets which only prioritize top-1 retrieval. While reinforcement learning (RL) can optimize set-level objectives via interaction, deploying an RL-tuned LLM for fan-out retrieval is prohibitively expensive at query time. Conversely, diffusion-based generative retrieval enables efficient single-pass fan-out in embedding space, but requires objective-aligned training targets. To address these issues, we propose R4T (Retrieve-for-Train), which uses RL once as an objective transducer in a three step process: (i) train a fan-out LLM with composite set-level rewards, (ii) synthesize objective-consistent training pairs, and (iii) train a lightweight diffusion retriever to model the conditional distribution of set-valued outputs. Across Polyvore and a music playlist dataset, R4T improves retrieval quality over strong baselines while reducing query-time fan-out latency by an order of magnitude.
View details
Estimating high-resolution albedo for urban applications
Elizabeth Wesley
Salil Banerjee
Aniruddh Chennapragada
Kevin Crossan
Bryce Cronkite-Ratcliff
Ellie Delich
Tristan Goulden
Mansi Kansal
Eric Mackres
Yael Mayer
Rebecca Milman
Shravya Shetty
Charlotte Stanton
Wayne Sun
Lucy Hutyra
Nature Communications (2026)
Preview abstract
Extreme urban heat poses a growing threat to global health, yet a critical cooling intervention-reflective cool roofs-lacks the high-resolution data needed for building-level planning. While satellite-based albedo estimates are globally available, their coarse resolution cannot resolve individual rooftops, limiting targeted climate action. Here we show that fusing freely available 10-m Sentinel-2 imagery with high-resolution satellite data enables urban albedo mapping at 30-cm resolution. This approach combines the radiometric accuracy and global coverage of Sentinel-2 with the spatial detail of commercial imagery to produce actionable urban datasets. Validated against airborne hyperspectral measurements (root mean square error = 0.04), the method resolves albedo at individual building footprints across diverse urban environments. Applied to 12 global cities, our analysis shows that prioritizing large-footprint buildings for cool roof retrofits can reduce citywide temperatures by up to 0.5 °C. This work validates Sentinel-2 for city-scale albedo estimation and enables globally deployable, building-scale rooftop albedo mapping to support targeted cool roof interventions.
View details
Outrunning LLM Cutoffs: A Live Kernel Crash Resolution Benchmark for All,
Chenxi Huang
Alex Mathai
Feiyang Yu
Aleksandr Nogikh
Eugene Wu
Kostis Kaffes
Junfeng Yang
Baishakhi Ray
Proceedings of the 43rd International Conference on Machine Learning (ICML) (2026)
Preview abstract
Repairing system crashes discovered by kernel fuzzers like Syzkaller is a critical yet underexplored challenge in software engineering. While recent works have introduced Large Language Model (LLM) based agents for Linux kernel crash-resolution, their evaluation benchmarks are usually static and thus, do not capture the evolving nature of the Linux kernel, and suffer from potential data contamination due to LLM knowledge cutoffs. To address the above problem, we present (i) Live-kBench, an evaluation framework for self-evolving benchmarks that continuously scrapes and evaluates agents on freshly discovered kernel bugs, and (ii) kEnv, an agent-agnostic standardized crash-resolution environment for kernel compilation, execution, and feedback. This design decouples agent workflows from heavy-weight execution, enabling fair and scalable comparison across diverse agent frameworks under identical conditions. To this end, we curate an inaugural dataset of 534 Linux kernel bugs and empirically demonstrate a significant performance gap, with agents achieving up to 25% higher equivalent patch rate on bugs fixed before the LLM knowledge cutoff. Using kEnv, we benchmark three state-of-the-art agents, showing that they resolve 74% of crashes on the first attempt (plausible patches); however only ~20% of generated patches closely match developer fixes. Additionally, exposing crash resolution feedback improves crash resolution rate by 29%. Live-kBench provides the community with an evaluation infrastructure for self-evolving benchmarks that is both time and attribute sensitive; complete with a public dashboard to track agent progress on Linux kernel bugs.
View details
Preview abstract
Enterprise service centers, particularly in domains like People Operations, are critical hubs of organizational knowledge work. They face a persistent difficulty in disseminating the tacit, case-specific expertise of senior agents, which can lead to inconsistent service and slower onboarding for new hires. While existing Knowledge Management (KM) and Case-Based Reasoning (CBR) systems have improved the retrieval of historically similar cases, they inadvertently shift the cognitive burden of synthesizing this information to the time-constrained agent. This paper introduces the Dynamic Case Precedent (DCP) architecture, a novel socio-technical framework designed to address this gap. The DCP architecture moves beyond simple precedent recommendation to automated precedent synthesis. It achieves this by integrating a semantic retrieval model with the large-context reasoning capabilities of a generative Large Language Model (LLM). We propose a three-pillar framework—(1) Contextual Similarity Indexing, (2) Generative Insight Synthesis, and (3) Human-in-the-Loop Refinement. By analyzing multiple relevant historical cases to generate a concise summary of resolution patterns, the DCP architecture aims to reduce agent cognitive load, accelerate proficiency, and improve service consistency. This conceptual framework offers a new model for human-AI collaboration, framing the AI not as a mere information tool, but as an active partner in sensemaking.
View details
Budget-Aware Tool Use Enables Effective Agent Scaling
Tengxiao Liu
Jin Miao
I-Hung Hsu
Samira Daruki
COLM 2026 (2026)
Preview abstract
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.
View details
