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 11363 publications
Type-Aware Ranking of Urban Similarity from Aerial Imagery
Idan Kligvasser
Yotam Intrator
Yuval Desheh
Aviad Barzilai
Niv Efron
Ehud Rivlin
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops (2026), pp. 821-829
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Estimating and ranking cross-city similarity from aerial imagery is a fundamental challenge in remote sensing and geospatial representation learning. Urban environments differ widely in road layout, marking conventions, and infrastructure design, yet standard visual representations often struggle to disentangle these meaningful structural variations from superficial appearances. In this work, we propose a type-aware contrastive learning framework that measures urban similarity by explicitly modeling distinct infrastructure elements. Leveraging open-vocabulary retrieval, we construct a globally diverse dataset of road-related features, such as intersections, crosswalks, and bus lanes, and train a type-conditioned Vision Transformer that fuses visual features with CLIP-derived semantic embeddings. Crucially, we introduce an adaptive per-type contrastive loss that dynamically emphasizes infrastructure categories with high discriminative power while down-weighting less informative types. To quantify city-level similarity, we aggregate per-type cosine similarities via a lightweight classifier to generate a global city-to-city similarity matrix. Experiments demonstrate that this type-aware approach significantly improves clustering quality and successfully generalizes to unseen cities, establishing a scalable, interpretable foundation for comparative urban analysis.
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Here’s a thought experiment. Say I wave a magic wand across a codebase and an entire class of technical debt, poof, goes away and immediately evaporates if introduced in the future. For example, maybe I make it so that dead feature flags are simply no longer a problem: they just delete themselves as soon as the engineer wills it. Or maybe large-scale migrations just migrate themselves. Maybe we magically have 100% test coverage, without an engineer lifting a finger.
What will happen to developer productivity?
Surely, developer productivity increases overall.
But will the productivity metrics that we all use as a proxy for “developer productivity” move up and to the right. Let’s explore this idea.
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This paper introduces Operationalized Temporal Entity Resolution, a distributed system architecture designed to resolve data consistency challenges in modern Security Information and Event Management (SIEM) environments. processing petabytes of high-velocity telemetry. We address the critical failure mode of ”State Smearing”—a temporal discrepancy between an entity’s state at event time versus analysis time—which frequently corrupts forensic timelines, particularly regarding ephemeral assets like containers and DHCP leases. Our approach coalesces heterogeneous data from diverse log sources into a single, canonical representation, processing over 2 billion entity fragments daily. By leveraging a deterministic Dynamic Graph Resolution via modified distributed connected components and a novel Density-Aware Temporal Checkpointing algorithm, we generate precise validity intervals. This method embeds temporal state directly into the resolution graph, eliminating the need for computationally expensive query-time joins. Ultimately, this architecture enables security analysts to perform ”time-travel” queries to reconstruct historical states accurately. Analysis of a production environment demonstrates that 8–16% of threat detection rules critically depend on this enriched temporal merging.
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**Agentic Engineering** is the rigorous discipline of treating Large Language Models as semi-autonomous systems that execute complex, multi-step workflows (trajectories) based on verifiable specifications, rather than using them as simple autocomplete engines.
Here is a brief summary of its core principles:
* **Main Goals:** It aims to maximize the agent's autonomous run-time, multiply a single engineer's impact by running parallel tasks, and offload tedious boilerplate coding.
* **The "Harness":** A raw model is virtually useless without heavy investment in a harness—comprising tools, system prompts, and strict guardrails—to reliably guide the model and enforce coding policies.
* **Loss of Micro-Control:** Engineers must surrender idiosyncratic stylistic preferences; if the agent's code passes automated linters and tests, it is accepted.
* **Meta-Debugging:** When failures occur, engineers no longer fix code syntax. Instead, they debug the workflow itself—adjusting the agent's tools, search queries, or prompt constraints to ensure repeatable success.
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Securing Elliptic Curve Cryptocurrencies against Quantum Vulnerabilities: Resource Estimates and Mitigations
Michael Broughton
Thiago Bergamaschi
Justin Drake
Dan Boneh
arXiv:2603.28846 (2026)
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This whitepaper seeks to elucidate implications that the capabilities of developing quantum architectures have on blockchain vulnerabilities and mitigation strategies. First, we provide new resource estimates for breaking the 256-bit Elliptic Curve Discrete Logarithm Problem, the core of modern blockchain cryptography. We demonstrate that Shor's algorithm for this problem can execute with either <1200 logical qubits and <90 million Toffoli gates or <1450 logical qubits and <70 million Toffoli gates. In the interest of responsible disclosure, we use a zero-knowledge proof to validate these results without disclosing attack vectors. On superconducting architectures with 1e-3 physical error rates and planar connectivity, those circuits can execute in minutes using fewer than half a million physical qubits. We introduce a critical distinction between fast-clock (such as superconducting and photonic) and slow-clock (such as neutral atom and ion trap) architectures. Our analysis reveals that the first fast-clock CRQCs would enable on-spend attacks on public mempool transactions of some cryptocurrencies. We survey major cryptocurrency vulnerabilities through this lens, identifying systemic risks associated with advanced features in some blockchains such as smart contracts, Proof-of-Stake consensus, and Data Availability Sampling, as well as the enduring concern of abandoned assets. We argue that technical solutions would benefit from accompanying public policy and discuss various frameworks of digital salvage to regulate the recovery or destruction of dormant assets while preventing adversarial seizure. We also discuss implications for other digital assets and tokenization as well as challenges and successful examples of the ongoing transition to Post-Quantum Cryptography (PQC). Finally, we urge all vulnerable cryptocurrency communities to join the ongoing migration to PQC without delay.
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Config Driven User Interface (CDUI) frameworks, often referred to interchangeably as
Server Driven UI (SDUI) have fundamentally altered mobile and web application development. By shifting layout and orchestration logic from compiled client binaries to dynamic
server responses, organizations bypass slow app store review cycles and ensure cross-platform
consistency. Despite its widespread adoption by industry leaders, CDUI remains underexplored in formal software engineering literature. This paper investigates the evolution of
CDUI through a hybrid methodology, combining a Multivocal Literature Review (MLR) of
grey literature from organizations including Uber, Airbnb, Lyft, Spotify, Faire, and Zalando,
with a structural artifact analysis of open-source production frameworks (Yandex DivKit,
Zup Beagle, and Spotify Hub). We introduce a taxonomy grounded in established foundational UI components and evaluate systems across three axes: Modularity, Centralization,
and Strictness. By tracing the architectural evolution from simple remote configurations to
strongly typed, Protobuf-driven contracts, our empirical artifact analysis (evaluating 570
schema commits across three repositories) demonstrates the critical magnitude of schema
governance and the complex challenge of managing “contract fragility.” We further analyze
cross-cutting concerns such as security sandboxing, “BFF Bloat,” and native accessibility
mapping, culminating in a future research agenda focused on the formal verification of UI
configurations.
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Generative AI is reshaping software development, yet its psychological impact remains under-researched. During May and August 2025 we conducted reflexive thematic analysis of interviews with 12 senior engineers (≥5 years experience) recruited from Western technology hubs to explore shifts in professional identity. We identify a central transition from "coder to conductor," where AI acts as a cognitive partner. Key findings include: (1) a re-architecting of focus from implementation to strategy; (2) a shift in productivity metrics from output to impact; and (3) a dual-impact on agency, where AI empowers autonomy but threatens competence through de-skilling anxieties. These findings suggest that as implementation becomes commoditised, organisational training and career progression must prioritise architectural mastery and metacognitive oversight to ensure sustained developer motivation and system integrity.
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An Empirical Study of Tablet Ergonomics: The Interplay of Temperature, Orientation, and Use Behaviors
Carmen Van Ommen
Mikki Phan
Arun Raghupathy
Daniel Huynh
Barbara Chaparro
Ergonomics in Design: The Quarterly of Human Factors Applications Journal (2026)
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To balance computational performance with thermal comfort, this study explores a consolidated hotspot architecture at the top center of a tablet. We tested hotspot (39°C, 43°C, 45°C, 47°C) and ambient temperatures (25°C, 35°C) with 60 participants, measuring perception, action likelihood, and expectation. The hotspot was observed away from high contact areas, with 43°C identified as the threshold for significant discomfort. Discomfort increased with portrait mode use and higher device and ambient temperatures, while active use duration influenced acceptability. The findings underscore the importance of thermal mapping and contextual sensing, with direct applications for software throttling thresholds of coated aluminum enclosures.
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Standard evaluations of backdoor attacks on text-to-image (T2I) models primarily measure trigger activation and visual fidelity. We challenge this paradigm, demonstrating that encoder-side poisoning induces persistent, trigger-free semantic corruption that fundamentally reshapes the representation manifold. We trace this vulnerability to a geometric mechanism: a Jacobian-based analysis reveals that backdoors act as low-rank, target-centered deformations that amplify local sensitivity, causing distortion to propagate coherently across semantic neighborhoods. To rigorously quantify this structural degradation, we introduce SEMAD (Semantic Alignment and Drift), a diagnostic framework that measures both internal embedding drift and downstream functional misalignment. Our findings, validated across diffusion and contrastive paradigms, expose the deep structural risks of encoder poisoning and highlight the necessity of geometric audits beyond simple attack success rates.
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This article delves into how Google Site Reliability Engineers (SREs) leverage Gemini 3 and the Gemini CLI to aggressively reduce Mean Time to Mitigation (MTTM) during real-world outages. By focusing on the SRE motto of "Eliminate Toil," the article walks through a simulated incident, demonstrating how an agentic CLI acts as a human-in-the-loop copilot across the entire incident lifecycle: from initial paging and investigation, through safe, tool-driven mitigation and root cause analysis, to automated postmortem generation and action item filing. This direct integration of Gemini's reasoning capabilities with operational data and internal tools creates a virtuous cycle where past incident learnings continuously inform and improve future solutions.
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Large Language Models utilizing reasoning techniques improve task performance but incur significant latency and token costs due to verbose generation. Existing automatic prompt optimization(APO) frameworks target task accuracy exclusively at the expense of generating long reasoning traces. We propose Cost-Regularized Optimization of Prompts (CROP), an APO method that introduces regularization on response length by generating textual feedback in addition to standard accuracy feedback. This forces the optimization process to produce prompts that elicit concise responses containing only critical information and reasoning. We evaluate our approach on complex reasoning datasets, specifically GSM8K, LogiQA and BIG-Bench Hard. We achieved an 80.6% reduction in token consumption while maintaining competitive accuracy, seeing only a nominal decline in performance. This presents a pragmatic solution for deploying token-efficient and cost-effective agentic AI systems in production pipelines.
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Coding agents are rapidly changing the landscape of software development, moving from inline com- pletion to autonomous systems that edit repositories, open pull requests, respond to issues, and run scheduled or webhook triggered routines across the development life cycle. The next generation is increasingly described as proactive and long-horizon: agents should notice relevant changes before the developer asks, connect signals across tools, decide when to interrupt, and carry preferences across sessions. Yet the field lacks a precise account of what proactivity means for software development, how it differs from autonomy, what acceptance criteria proactive long-horizon tasks should satisfy, and which metrics determine whether unsolicited agent behavior is useful rather than merely active. We argue that proactive coding agents should be evaluated by the quality and improvement of their insight policy: the policy that decides what matters next, what evidence supports it, whether to surface it, and how to adapt after feedback. We re-anchor this view in mixed initiative interaction, introduce a three level taxonomy (Reactive, Scheduled, and Situation Aware), compare contemporary coding agents against five operational criteria, and sketch an active user simulation protocol with three evaluation targets: Insight Decision Quality (IDQ), Context Grounding Score (CGS), and Learning Lift (LL).
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VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
Dj Dvijotham
Mirko Montanari
ACL 2026
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Artificial intelligence is rapidly evolving, marked by the emergence of Large Language Model (LLM) agents – systems capable of complex reasoning, planning, and interaction with digital and physical environments. These agents, powered by advancements in LLMs, demonstrate remarkable capabilities across diverse domains, including finance, healthcare, web navigation, software development, and daily task assistance. Unlike traditional AI systems, LLM agents can perceive their surroundings, formulate multi-step plans, utilize external tools and APIs, access memory or knowledge bases, and execute actions to achieve specified goals. This ability to act upon the world, however, introduces significant safety and security challenges.
The safety paradigms developed for traditional LLMs, primarily focused on mitigating harmful textual outputs (e.g., toxicity, bias), are insufficient for safeguarding LLM agents. Agents interacting with dynamic environments and executing actions present a broader attack surface and new categories of risk. These include performing unsafe operations, violating privacy constraints through improper data handling or access control failures, deviating from user objectives (task misalignment), and susceptibility to novel manipulation techniques like indirect prompt injection and memory poisoning. Ensuring the trustworthy operation of these powerful agents is paramount, especially as they are integrated into high-stakes applications. To address this critical challenge, we introduce VeriGuard, a novel framework designed to enhance the safety and reliability of LLM agents by interactively verifying their policies and the actions. VeriGuard integrates a verification module that intercepts code-based actions proposed by the agent. In the first step, VeriGuard will generates and verifies the policies. The policies are rigorously checked against a set of predefined safety and security specifications Then each action will be verified to make sure it will align with the agent specification. This interactive verification loop ensures that the agent's behavior remains within safe operational bounds, effectively preventing the execution of harmful or unintended operations. By verifying each step, VeriGuard provides a robust safeguard, substantially improving the trustworthiness of LLM agents in complex, real-world environments.
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Multimodal large language models (LLMs) integrate and process information from multiple modalities such as text, images, audio, and video, enabling complex tasks such as audio translation and visual question answering. While powerful, this complexity introduces novel vulnerabilities to sophisticated adversarial attacks. This survey paper provides a comprehensive overview of this rapidly expanding field, systematically categorizing attacks that range from manipulations of single modalities (e.g., perturbed images or audio) to those exploiting cross-modal interactions. We overview how these attacks exploit weaknesses in model fusion, attention mechanisms, and representation learning and provided analyses on their potential for real-world consequences.
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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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