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 11642 publications
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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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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Quantization methods have significantly improved the compute and memory efficiency of Large Language Model (LLM) training. However, existing approaches still rely on accumulating their updates into high precision: concretely, gradient updates must be applied to a high-precision weight buffer, known as \textit{master weights}. This buffer introduces substantial memory overhead, particularly for Sparse Mixture of Experts (SMoE) models, where model parameters and optimizer states dominate memory usage. In this work, we introduce the Error-Compensating Optimizer (ECO), which \textit{for the first time} enables the complete elimination of master weights by directly accumulating updates into quantized parameters, by leveraging existing optimizer states. ECO quantizes the weights after every gradient step and injects the resulting quantization error into the optimizer's momentum buffer, creating an error-feedback loop with zero additional memory overhead for quantization. Beyond its practical efficiency, ECO comes with theoretical guarantees. Specifically, under standard assumptions, naive master weight removal can lead to unbounded drift from the ideal parameter trajectory, whereas ECO provably bounds this drift, ensuring stable convergence. We validate ECO across a range of models, including small transformers (30M--800M), Gemma-3 1B, and an SMoE 2.1B model, using FP8 quantization. In all cases, ECO achieves near-lossless accuracy compared to high-precision baselines. For large SMoE models, ECO reduces memory usage by up to 25\%, establishing a new Pareto frontier for the trade-off between static memory and training loss.
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Grounding AI Agents in Contracts: An Empirical Evaluation of Spec-Driven Test Generation
James McClure
José Cambronero
Renyao Wei
Dorothy Chen
Livio Dalloro
SpecOps '26: Proceedings of the 1st International Workshop on Specification-Driven Development Life Cycle, ACM (Association for Computing Machinery), New York, NY, USA (2026)
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LLM-based agents are increasingly used for coding tasks, where they have outperformed many classical approaches and scaled to repository-level tasks, such as test generation. However, when directly prompted to generate tests, these agents can fail to reason about the code and its underlying contracts, thereby missing edge cases and behavioral boundaries that affect test quality. To address this limitation, we propose Spec-Driven Test Generation, where we instruct an agent to first reason about – and explicitly document – code pre-conditions, post-conditions, and undefined behaviors. This intermediate semi-formal specification acts as a cognitive scaffold to guide subsequent test generation. Our evaluation on production bugs from Google shows that the spec-driven agent can deliver a 9.8 percentage points (p = 0.0352) improvement in bug detection rate and a 2.5 percentage point (p = 0.0034) improvement in branch coverage, compared to a traditional test generation agent baseline. Using LLM-as-a-Judge, we further show that test suites generated by the spec-driven agent are superior to the baseline and human-authored tests in 77.8% and 56.7% of the cases, respectively, and demonstrated improvements on following best practices, readability, and edge-case coverage.
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Superconducting qubits are a leading platform for realizing fault-tolerant quantum computers. Current generations demonstrate fast, high fidelity quantum gates and readout on the order of hundreds of nanoseconds, while maintaining coherence times exceeding one hundred microseconds. Achieving this state-of-the-art performance requires a tight co-design, balancing fundamental physics, microwave engineering, and semiconductor fabrication. Readout designs, in particular, benefit from this multidisciplinary approach.
In this talk, we discuss the current challenges for readout in superconducting quantum processors from the perspective of Google Quantum AI. We examine the intersection of device physics and microwave engineering constraints, illustrating how optimizing both is essential for scaling next-generation quantum systems.
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In modern Kubernetes environments, eBPF (Extended Berkeley Packet Filter) has become the de facto standard for high-performance dataplane enforcement. However, this architecture introduces a complex distributed state problem: the asynchronous synchronization between the Kubernetes control plane (Intent) and the kernel-space BPF maps (Reality).
A critical failure mode, termed “Silent Divergence,” occurs when the control plane believes a network policy or identity is applied, but the underlying kernel state is missing or corrupted. In this “Gray Failure” state, standard observability tools—including logs, liveness probes, and agent status checks—report health, while the network silently drops traffic.
This paper introduces eBPF-Auditor, a specialized consistency verification framework. Unlike standard agents that rely on event-based reconciliation, eBPF-Auditor performs a periodic “Two-Way State Audit” that mathematically verifies the intersection of Kubernetes Intent and BPF Reality. We demonstrate through fault injection and benchmarks on 5,000 pods that this approach successfully detects state drift with 100% accuracy and negligible sub-millisecond overhead (ms), making it a viable solution for high-frequency runtime verification in production hyperscale clusters.
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Who Controls the Curriculum for AI? The Limits of Participatory Design for Educational AI
Learning Under Algorithmic Conditions, University of Minnesota Press (2026)
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Participatory design is a long-standing effort to shift control over technology design from technologists to users and communities impacted by technologies. For educational AI, this means involving students, families, teachers, and other stakeholders in shaping the design of AI systems. While promising, in this article, I situate the recent calls for participatory design of educational AI systems within a different historical tradition—that of contests over local control of educational curricula. I argue that approaches that attempt to steer the design and development of educational AI through participatory methods may inadvertently reproduce the history of political contestation of educational curricula, in ways that may privilege the most powerful communities, rather than those inequitably impacted. What might it look like to treat participatory AI design as a site for political contestation? How might these approaches avoid reproducing the same majoritarian tendencies that led to educational inequities in the first place?
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Marginalized Bundle Adjustment: Multi-View Camera Pose from Monocular Depth Estimates
Shengjie Zhu
Xiaoming Liu
Vincent Chu
International Conference on 3D Vision (2026)
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Structure-from-Motion (SfM) is a classical 3D vision task for recovering camera parameters and scene geometry from multi-view images. Recent advances in deep learning enable accurate monocular depth estimation (MDE) that infers structure from a single image without depending on camera motion. But integrating MDE into SfM remains challenging. Unlike classical triangulated sparse pointclouds, MDE produces dense depthmaps with significantly higher error variance. Inspired by modern RANSAC estimators, we propose a Marginalized Bundle Adjustment (MBA) to accommodate MDE error variance with its density. With MBA, we show that MDE depthmaps are sufficiently accurate to support SoTA or competitive results in Structure-from-Motion and camera relocalization. Our benchmark demonstrates consistent remarkable results from two-view, few-frames small multiview, to thousands-frames large multiview system. Our method highlights the significant potential of MDE on multi-view 3D vision tasks.
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IBBench-Light: A Paired Evaluation of Task-Conditioned Responses to External Directives
Mark Zhou
Zhaoyi Li
Janet Sung
Iris Qian
Hang Xiao
2026
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in many large language model (LLM) applications, a serialized prompt contains both a user request and external records such as webpages, email, memory, or tool outputs. The same embedded directive may need to be applied for one task and treated as data for another. IBBench-Light tests this contrast with 12 semantic tasks rendered through four source wrappers and three embedding forms. The construction yields 144 matched records and 288 prompts per model. Four 4-bit open-weight instruction models produce 1,152 archived single-run greedy responses. Under the exact output contract, paired exact-contract accuracy (PECA) ranges from 1.4% to 67.4%; Qwen3-4B and Mistral-7B have the two highest point estimates on this four-model panel. Task-cluster resampling describes variation across the 12 semantic bases, while standalone-target scoring separates some target-selection failures from response-form errors. A post-hoc leading-target rule changes Phi-4-mini’s paired score substantially; the archived run lacks the stop-reason metadata needed to resolve whether generation termination caused these continuations. The findings describe synthetic, single-turn tasks under one user-role serialization. Adaptive attacks, role-level hierarchy, and tool-mediated effects require separate experiments
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AI-Simulated Expert Panels for Socio-Technical Scenarios and Decision Guidance
Andrew G. Ross
Alan Ross
(2026) (to appear)
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Socio-technical scenarios for net-zero and other transformation pathways combine qualitative storylines with quantitative models, embedding them in plausible societal contexts for model assessment. Conventional scenario generation is resource-intensive, can be limited in internal consistency and diversity of expert and stakeholder perspectives, and is rarely stress-tested.
This paper introduces a synthetic, AI-based expert panel to address these bottlenecks. An AI model first simulates domain experts who agree on descriptors, states, and their interactions. A probabilistic Cross-Impact Balance analysis then generates internally consistent pathways, using stochastic shocks to assess robustness and pathway diversity. An AI stakeholder panel
uses multi-criteria decision analysis to select a preferred pathway; an AI expert panel translates it into model-ready quantitative inputs. Although scalable and applicable to any other country or region, the framework is applied to Germany's energy transition as a proof of concept, and offers an alternative and/or supplement to scenario generation. Furthermore, it enables Virtual AI-Led Decision Laboratories for exploratory policy stress-testing and provides an approach for rapid, structured expert elicitation and decision support in other domains.
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Looping, reusing a block of layers across depth, and depth growing, training shallow-to-deep models by duplicating middle layers, have both been linked to stronger reasoning, but their relationship remains unclear. We provide a mechanistic unification: looped and depth-grown models exhibit convergent depth-wise signatures, including increased reliance on late layers and recurring patterns aligned with the looped or grown block. These shared signatures support the view that their gains stem from a common form of iterative computation. In particular, applying inference-time looping to the middle blocks of a depth-grown model yields additional improvements in reasoning primitives accuracy, up to 2x, despite the model never being trained to loop. Building on this connection, we show that the two techniques are both adaptable and composable. Both approaches adapt better than the baseline when given more in-context examples or additional supervised fine-tuning data. Additionally, depth-grown models achieve the largest reasoning gains when using higher-quality, math-heavy cooldown mixtures, which can be further boosted by adapting a looped block in the middle of the network. Overall, our results position depth growth and looping as complementary, practical methods for inducing and scaling iterative computation to improve reasoning.
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Neural general circulation models for modeling precipitation
Stephan Hoyer
Dmitrii Kochkov
Janni Yuval
Ian Langmore
Science Advances (2026)
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Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. While hybrid models combining machine learning and physics have emerged with the premise of improving precipitation simulations, none have proven sufficiently skillful or stable enough to outperform existing models in simulating precipitation.
Here, we present the first hybrid model that is trained directly on precipitation observations. The model runs at 2.8 degrees resolution and is built on the differentiable NeuralGCM framework. This model is stable for decadal simulations and demonstrates significant improvements over existing GCMs, ERA5 reanalysis, and a Global Cloud-Resolving Model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle.
Furthermore, it outperforms the ECMWF ensemble for mid-range weather forecasting.
This advance paves the way for more reliable simulations of current climate and for the ability to fully utilize the abundance of existing observations to further improve GCMs.
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We consider a setting where we have a ground set ℳ together with real-valued set functions f₁, … , f_n, and the goal is to partition ℳ into two sets S₁,S₂ such that |f_i(S₁) - f_i(S₂)| is small for every i. Many results in discrepancy theory can be stated in this form with the functions f_i being additive. In this work, we initiate the study of the unstructured case where f_i is not assumed to be additive. We show that even without the additivity assumption, the upper bound remains at most O(√{n log n}).
Our result has implications on the fair allocation of indivisible goods. In particular, we show that a consensus halving up to O(√{n log n}) goods always exists for n agents with monotone utilities. Previously, only an O(n) bound was known for this setting.
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We present a polynomial-time algorithm for computing an optimal committee of size $k$ under any given Thiele voting rule for elections on the Voter Interval domain (i.e., voters can be ordered so that each candidate is approved by a contiguous block of voters). Our result extends to the Generalized Thiele rule, in which each voter has an individual weight (scoring) sequence. This resolves a 10-year-old open problem that was originally posed for Proportional Approval Voting and later extended to every Thiele rule.
Our main technical ingredient is a new structural result---a concavity theorem for families of intervals. It shows that, given two solutions of different sizes, one can construct a solution of any intermediate size whose score is at least the corresponding linear interpolation of the two scores. As a consequence, on Voter Interval profiles the optimal total Thiele score is a concave function of the committee size. We exploit this concavity within an optimization framework based on a Lagrangian relaxation of a natural integer linear program formulation, obtained by moving the cardinality constraint into the objective. On Voter Interval profiles, the resulting constraint matrix is totally unimodular, so it can be solved in polynomial time.
Our main algorithm and its proof were obtained via human--AI collaboration. In particular, a slightly simplified version of the main structural theorem used by the algorithm was obtained in a single call to Gemini Deep Think.
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Massive KV caches can cause severe memory-bandwidth bottlenecks during long-context decoding. Sparse attention methods mitigate this via selective loading, but that comes at a cost: rigid heuristics drop necessary context, leading to quality degradation. We introduce \textbf{Elastic Threshold Attention (ETA)}, an end-to-end trainable architecture that achieves hardware-accelerated decoding speed without sacrificing dense model quality. ETA predicts dynamic, contextual thresholds directly from query representations, allowing the model to allocate dense-like context to difficult retrieval or reasoning steps while pruning routine tokens. To learn this policy from scratch without representation collapse, ETA \emph{multiplicatively suppresses} sub-threshold logits toward zero during training rather than deleting them. Training against this smooth uniform attention floor provides a distributed probability reservoir that \textbf{causes localized attention sinks on initial tokens to disappear}. It also enables the model to hard-prune uninformative KV blocks at inference time and absorb incidental tokens co-admitted by coarse GPU block selection. As a result, a 1.45B pretrained ETA model rivals dense attention across language modeling, commonsense reasoning, and long-context needle retrieval at $\approx 85\%$ training sparsity and $\approx 38\%$ active decode density. At inference time, we implement a custom decode kernel in Triton that screens KV blocks in $O(1)$ time using cached geometric-probabilistic bounds, delivering up to $2.5\times$ wall-clock decode speedups over FlashAttention-2 on sequences up to 512K tokens. Finally, we introduce an offline calibration algorithm for domain-specific deployments that freezes per-head constant thresholds to eliminate predictor overhead, cutting attention compute by an additional $27\%$.
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