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 11626 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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Fine-Grained Table Retrieval for Open-Domain Tabular Question Answering
Xingyu Ji
Wojciech Kosiuk
Madelon Hulsebos
Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026), Association for Computational Linguistics
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This work introduces a fine-grained table retrieval framework for grounding large
language models in heterogeneous, open-domain relational data. Instead of encoding a query as a single vector, the approach decomposes natural
language queries into semantic components and embeds each independently, enabling
more precise matching of compositional query intent. These representations are used in a staged retrieval pipeline with component-level
search, connectivity-aware grouping, and reranking. Experiments on three TARGET benchmark corpora show consistent improvements in capped recall@k and stronger alignment between query intent and tabular structure over dense retrieval baselines, particularly for longer and more complex queries and when using lightweight embedding models.
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Profit Maximization in Bilateral Trade against a Smooth Adversary
Simone di Gregorio
Paul Duetting
Federico Fusco
Chris Schwiegelshohn
NeurIPS 2026
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Bilateral trade models the task of intermediating between two strategic agents, a seller and a buyer, who wish to trade a good. We study this problem from the perspective of a profit-maximizing broker within an online learning framework, where the agents' valuations are generated by a smooth adversary.
We devise a learning algorithm that guarantees a $\tilde{O}(\sqrt{T})$ regret bound, which is tight in the time horizon $T$ up to poly-logarithmic factors. This matches the minimax rate for the stochastic i.i.d.~case, and is also well separated from the adversarial setting, where sublinear-regret is unattainable. By extending the strong regret guarantees from the i.i.d.~case to the smooth adversary, we significantly broaden the scope of settings where such fast rate is achievable, while closing an important gap in the regret landscape of this fundamental economic problem.
To overcome the challenges posed by this adversary, we leverage a continuity property of smooth instances and combines this with a hierarchical net-construction of the broker's action space, which is analyzed via algorithmic chaining. We showcase the applicability of these techniques by deriving a similarly tight $\tilde{O}(\sqrt{T})$ regret bound for a related mechanism design model: the joint ads problem.
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DDRop: Generic Memory Interposer Attacks on Confidential VMs by Dropping DDR5 Writes
Jesse Demeulemeester
Stefan Gloor
Patrick Jattke
David Oswald
Martin Thompson
Kaveh Razavi
Ingrid Verbauwhede
Jo Van Bulck
ACM Conference on Computer and Communications Security (CCS) (2026)
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Trusted Execution Environments (TEEs) are increasingly deployed in the cloud to protect sensitive workloads through hardwareenforced isolation, remote attestation, and transparent memory encryption. However, to meet memory performance and size demands, modern TEEs omit cryptographic freshness guarantees, leaving them vulnerable to replay attacks by adversaries with physical memory access. Prior work demonstrated low-cost active interposition attacks on DDR4 without requiring expensive specialized equipment, but these techniques do not extend to DDR5, where existing approaches are limited to passive ciphertext side-channel analysis and rely on bus downclocking to accommodate legacy memory bus analyzers.
We present the first low-cost (<200$) DDR5 RDIMM interposer capable of active fault injection at native speeds. By injecting targeted parity errors to silently discard cache line writebacks, we introduce DDRop, a new primitive that exploits the absence of cryptographic freshness to break the integrity of Intel TDX, Scalable SGX, and AMD SEV-SNP. Building on this primitive and targeting the APIs exposed by the TDX module and AMD Secure Processor, we show that adversaries can gain ciphertext access, copy arbitrary victim pages, and inject malicious secure page-table entries. We demonstrate end-to-end attacks on an up-to-date TDX platform, including forcing any TD into debug mode and forging attestation reports. While software-level mitigations, including timing-based interposer detection and API hardening, may reduce the attack surface, our results demonstrate that active DDR5 bus interposition is practical at low cost, highlighting the need for robust cryptographic memory integrity protections against physical adversaries.
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Budget-Aware Tool Use Enables Effective Agent Scaling
Tengxiao Liu
Jin Miao
I-Hung Hsu
Samira Daruki
COLM 2026 (2026)
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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.
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DAC: Hierarchical Functional Fault Grading with Auto-Identification of Observation Points for Enhanced DFT Coverage in Complex SoC Designs
Kasi Chunduri
Rajesh Gottumukkala
Karthikeyan Sundaramahalingam
Veerabhadra Rao Vasa
2026
Preview abstract
As semiconductor designs increase in complexity, achieving near-zero Defective Parts Per Million (DPPM) has become a critical challenge that traditional scan-based Design-for-Test (DFT) methods often difficult to meet. Conventional structural tests often leave quality gaps in custom circuits and hard-to-detect logic, leading to costly field returns (RMA). While functional verification patterns can bridge these coverage gaps, their integration is typically hindered by unoptimized stimuli, redundant simulations, and extreme memory demands that can make "flat" SoC fault grading unfeasible.
This paper presents a transformative hierarchical fault grading methodology that overcomes these bottlenecks through two core innovations: automated identification of optimal observation points and hierarchical partitioning for parallel simulation. Our approach utilizes a proprietary algorithm to automatically pinpoint internal nodes that maximize fault detection, significantly reducing the manual effort required for final coverage closure. By breaking complex SoCs into modular sub-scopes (chiplets), our framework enables parallelized fault grading that slashes RAM requirements—reducing them from over higher compute in traditional runs to standard server levels—and converts week-long simulation cycles into a predictable workflows.
Experimental results demonstrate the high scalability of this approach: where simulating just four faults at the SoC level was previously memory-prohibitive, our hierarchical method successfully processed 10,000 faults with consistent accuracy and a multi-fold reduction in total runtime. By leveraging functional test suites to detect structural defects that traditional DFT misses, this framework ensures earlier coverage closure and eliminates late-cycle engineering changes, ultimately accelerating time-to-market for high-quality, complex SoC designs.
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The promise of tailored agent behavior is undermined by a critical explainability challenge: it is difficult to assess how closely and consistently the agent follows user-defined rules. As Large Language Models (LLMs) transition from static assistants to autonomous agents, developers have pioneered markdown-based rule files (e.g., GEMINI.md, CIDER_AGENT.md) to steer agent behavior and mitigate a "organizational context gap" that emerges when general-purpose models lack the "organizational context" necessary for contextually relevant results. This paper presents a qualitative study of 12 Google software developers (n=12) to investigate the authoring and efficacy of these agent rules. Our findings reveal that while rules are intended as technical steering mechanisms, they function as a "Black Box" of validation, where 12/12 participants rely on anecdotal "vibe checks" due to a profound lack of formal evaluation and explainability frameworks. We identify this opacity as a systemic Attribution Gap, which prevents developers from discerning whether a successful outcome was the result of deliberate logic or "pure luck." Paradoxically, these files serve a dual role as "Living Documentation," bridging technical instruction for AI with sociotechnical onboarding for humans. We argue for a transition toward library-level governance and rigorous traceability to transform agent customization from an ad-hoc craft into a human-centered science by revealing the internal "seams" of rule interpretation.
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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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We address the problem of training conversion prediction models in advertising domains under privacy constraints, where direct links between ad clicks and conversions are unavailable. Motivated by privacy-preserving browser APIs and the deprecation of third-party cookies, we study a setting where the learner observes a sequence of clicks and a sequence of conversions, but can only link a conversion to a set of candidate clicks (an attribution set) rather than a unique source. We formalize this as learning from attribution sets generated by an oblivious adversary equipped with a prior distribution over the candidates. Despite the lack of explicit labels, we construct an unbiased estimator of the population loss from these coarse signals via a novel approach. Leveraging this estimator, we show that Empirical Risk Minimization achieves generalization guarantees that scale with the informativeness of the prior and is also robust against estimation errors in the prior, despite complex dependencies among attribution sets.
Simple empirical evaluations on standard datasets suggest our unbiased approach significantly outperforms common industry heuristics, particularly in regimes where attribution sets are large or overlapping.
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CSIG: Congestion Signaling for Datacenter Transports
Abhiram Ravi
Brad Karp
MJ Akhbari
Weida Huang
Konstantinos Prasopoulos
2026
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Optimizing burst-heavy datacenter workloads necessitates fine-grained network control and visibility. We introduce CSIG, a protocol that delivers precise, multi-bit bottleneck congestion signals via a fixed-length Ethernet header. The architecture captures 𝜇s-granularity switch metrics, such as available bandwidth, and signals them to end-hosts using in-band, line-rate operations. We propose Fast Ramp-Up, a congestion control primitive that leverages these bottleneck signals to reduce median RPC latency by 20% and unclaimed bandwidth by 60% in production. Beyond transport-level performance, CSIG enables flow-aware observability by embedding 𝜇s-scale metrics into every packet, allowing individual application transfers to pinpoint their bottleneck location, such as the topology tier limiting their performance. CSIG thus transforms network telemetry from post-hoc correlation into a real time, contextaware capability. We demonstrate CSIG’s broad deployability by validating it across five generations of commodity switch hardware (up to 102.4 Tbps), four NIC generations, and five transport stacks. Our design proves that a streamlined Layer 2 approach, focusing exclusively on the principal path bottleneck, provides transport-agnostic gains without requiring forklift hardware upgrades.
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FabScore: Fine-Grained Evaluation of Fabrications in Automated AI Research
James Xu Zhao
See-Kiong Ng
Dongfu Jiang
Bryan Hooi
Qianyun Guo
Hui Chen
Pang Wei Koh
Muhao Chen
Yiwei Wang
2026
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In automated AI research, scientific rigor is not merely a matter of producing coherent papers and executable code; rather, it fundamentally requires that the experimental results claimed in a paper are faithfully supported by the accompanying implementation and verifiable through actual execution logs. When this alignment breaks down, AI-generated research may contain fabrications: discrepancies between the methods described in the paper and what the code actually implements, or between the reported results and those obtained by running the code. This motivates a systematic evaluation of fabrications that systematically examines the consistency between a paper's claimed experimental outcomes and its accompanying code files. In automated AI research, scientific rigor is not merely a matter of producing coherent papers and executable code; rather, it fundamentally requires that the experimental results claimed in a paper are faithfully supported by the accompanying implementation and verifiable through actual execution logs. When this alignment breaks down, AI-generated research may contain fabrications: discrepancies between the methods described in the paper and what the code actually implements, or between the reported results and those obtained by running the code. This motivates a systematic evaluation of fabrications that systematically examines the consistency between a paper's claimed experimental outcomes and its accompanying code files. This is a placeholder. This is a placeholder. This is a placeholder.
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Accurate ground state energy estimation with noise and imperfect state preparation
Tom O'Brien
Alicja Dutkiewicz
Stefano Polla
ArXiv (2026)
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We introduce a classical estimator for the post-processing of quantum phase estimation (QPE) data generated either by quantum-Fourier-transform-based or quantum-signal-processing-based methods. These methods sample from a distribution supported on $[0, 2\pi]$, depending on the location of the target phase within this window. We focus on the case where the target phase lies within a smaller promise region, where no other phases are present, which is typical of e.g. ground state energy estimation of gapped quantum systems. Our estimation technique is based on filtering the signal within the promise region and recovering the phase through a moment-projection estimator. We show that our methods are robust in the presence of both additional phases outside the promise region and global depolarizing noise. We observe in the noiseless case our estimator achieves an exponential improvement over naive mean estimation in certain parameter regimes. In the presence of global depolarizing noise, no robust state of the art is known, and our estimator achieves a bias exponentially small in the circuit depth $T$ at fixed circuit fidelity $F$, and a variance proportional to $T^{-2}$, improving by a factor of $T^4$ over the naive approach. To mitigate realistic circuit-level noise, we combine our method with the explicit unbiasing scheme described in Ref.~\cite{dutkiewiczError2025}. As an illustrative example, we implement these estimators on a small-scale simulation of the Ising model. We observe that the moment-projection estimator retains some residual bias removable by the explicit unbiasing scheme, but this was significantly smaller than the variance in the system for the parameter range tested. The robustness of the moment-projection estimator in the presence of both multiple eigenvalues and noise makes phase estimation with limited depth practical for early fault tolerant quantum experiments.
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Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search
Sarah Martinson
Nicholas Reich
Zahra Shamsi
arXiv (2026)
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Probabilistic forecasting of infectious diseases is crucial for public health but relies on labor-intensive manual curation by expert modeling teams. This bespoke development bottlenecks scalability to granular geographic resolutions or emerging pathogens. Here, we present an autonomous system utilizing Large Language Model (LLM)-guided tree search \cite{aygun_ai_2025} to iteratively generate, evaluate, and optimize executable forecasting software. In a fully prospective, real-time evaluation during the 2025–2026 US respiratory season, the system autonomously discovered methodologically diverse models for influenza, COVID-19, and respiratory syncytial virus (RSV). Aggregating these machine-generated models yielded an ensemble that consistently matched or outperformed the gold-standard, human-curated Centers for Disease Control and Prevention (CDC) hub ensembles out-of-sample. The system successfully navigated data-scarce "cold start" scenarios for RSV. Moreover, controlled ablations revealed that optimizing log-scale distance metrics prevents reward hacking, while an automated judge-in-the-loop ensures structural fidelity to complex scientific theories. By autonomously translating epidemiological theory into accurate, transparent code, this framework breaks the modeling labor bottleneck, enabling the rapid deployment of expert-level disease forecasting at unprecedented scales.
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Data-usage descriptors as search metadata: the case of food security data and the National Data Platform (2015-2025)
Julia Lane
Rafael Ladislau
Lauren Chenarides
Simon Porter
Manish Parashar
Scientific Data, 13 (2026)
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Scientific data is a critical input into scientific research. Yet the research data landscape is constantly changing as new datasets emerge, others are retired, or some disappear altogether. Without a systematic way to track how datasets are used across a research field, researchers have no reliable method for identifying relevant data resources or locating communities that work with them. Data-usage descriptors can substantially advance research productivity by reducing the time that researchers spend finding new and relevant datasets in their research field, and the communities that use them. This paper describes how to generate data-usage descriptors by finding how datasets are used in publications and then linking the dataset information to the publication metadata. It also shows how usage descriptors can be used to find other related datasets and their usage. It concludes by arguing that the approach represents a critical piece of foundational infrastructure that could be deployed in repositories as part of a referenceable, navigable, and contextual data framework. This article contains a reproducible workflow for constructing data-usage descriptors, based on analyzing the full text of publications in the Dimensions database. The illustrative use case is research on food security. The illustrative repository is the National Data Platform.
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