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 11260 publications
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When managing complex, unpredictable (non-deterministic) AI agents using simple, fixed control systems (like finite state machines), operational failures and accountability issues often arise. This document introduces a probabilistic governance and telemetry framework to resolve these problems. Instead of following a rigid sequence of steps, this framework defines a multi-dimensional operational boundary, a 'behavioral volume', and assigns the agent a goal. This allows the agent to use its own reasoning to achieve the goal while remaining within the defined boundaries. A separate telemetry layer monitors the agent's actions by calculating metrics, such as alignment scores and drift velocity, to measure how much the agent deviates from its intended behavior. This system provides a method for guiding, monitoring, and securing autonomous agents, effectively managing the performance and security of an unpredictable AI workforce in complex environments.
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Existing image simplification techniques often rely on Non-Photorealistic Rendering (NPR), transforming photographs into stylized sketches, cartoons, or paintings. While effective at reducing visual complexity, such approaches typically sacrifice photographic realism. In this work, we explore a complementary direction: simplifying images while preserving their photorealistic appearance.
We introduce progressive semantic image simplification, a framework that iteratively reduces scene complexity by removing and inpainting elements in a controlled manner. At each step, the resulting image remains a plausible natural photograph. Our method combines semantic understanding with generative editing, leveraging Vision-Language Models (VLMs) to identify and prioritize elements for removal, and a learned verifier to ensure photorealism and coherence throughout the process. This is implemented via an iterative \emph{Select–Remove–Verify} pipeline that produces high-quality simplification trajectories.
To improve efficiency, we further distill this process into an image-to-video generation model that directly predicts coherent simplification sequences from a single input image. Beyond generating cleaner and more focused compositions, our approach enables applications such as content-aware decluttering, semantic layer decomposition, and interactive editing.
More broadly, our work suggests that simplification through structured content removal can serve as a practical mechanism for guiding visual interpretation within the photorealistic domain, complementing traditional abstraction methods.
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In some multi-stage software build pipelines, downstream compiler errors may be reported against ephemeral, machine-generated intermediate artifacts rather than original, human-written source code, which can make remediation challenging. A system and method may address this by intercepting a downstream error, mapping its location back to the original source file, and programmatically injecting a dormant suppression tag into the original source code. During a subsequent build, an intermediate transpiler can propagate this tag into a newly generated intermediate artifact. In the intermediate file, the tag may become active and be recognized by the downstream compiler as a directive to suppress the specific error. This approach can facilitate an automated remediation process for certain build failures that avoids direct modification of ephemeral files and uses the original source code as a record for suppression.
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In today's AI landscape, success depends not just on prompting large language models but on orchestrating them into intelligent systems that are scalable, compliant, and cost-effective. GenAI on Google Cloud is your hands-on guide to bridging that gap. Whether you're an ML engineer or an enterprise leader, this book offers a practical game plan for taking agentic systems from prototype to production.
Written by practitioners with deep experience in AgentOps, data engineering, and GenAI infrastructure, this guide takes you through real-world workflows from data prep and deployment to orchestration and integration. With concrete examples, field-tested frameworks, and honest insights, you'll learn how to build agentic systems that deliver measurable business value.
> Bridge the production gap that stalls 90% of vertical AI initiatives using systematic deployment frameworks
> Navigate AgentOps complexities through practical guidance on orchestration, evaluation, and responsible AI practices
> Build robust multimodal systems for text, images, and video using proven agent architectures
> Optimize for scale with strategies for cost management, performance tuning, and production monitoring
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Beyond Vector Similarity: Hierarchical Context-Aware Graph RAG vs Standard RAG in Enterprise Code Migration
Suddhasatwa Bhaumik
Nilesh Jaiswal
Arjit Shukla
Divya Malhotra
Aniket Agrawal
Saurabh Garg
Suchit Puri
Google Cloud India, Google, S. No, AP81, 83, N Main Rd, near Hard Rock Cafe, Koregaon Park Annexe, Mundhwa, Pune, Maharashtra 411036 (2026)
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As enterprises modernize legacy systems (e.g., monolithic Java architectures to Python microservices), Large Language Models (LLMs) have become instrumental in automated code translation.
However, traditional vector-based Retrieval-Augmented Generation (Standard RAG) struggles
with topological relationships, fetching isolated text chunks that frequently sever inheritance chains and lead to high compilation failure rates.
This paper presents a comparative analysis between Standard RAG and a novel Hierarchical
Context-Resident Graph (HCRG) methodology. Our pipeline utilizes tree-sitter for polyglot
Abstract Syntax Tree (AST) extraction, mapping architectural edges into a Google Cloud Spanner Property Graph, and serializing this structure into a Gemini (on Vertex AI) Context Cache to enable topological, parent-first code translation.
By shifting evaluation from naive text-overlap to a custom 7-metric framework measuring Software Engineering (SE) utility, empirical evaluations on the spring-petclinic-genai repository
demonstrate significant structural improvements. Graph RAG decisively mitigates dependency loss, dropping the API hallucination rate from 56.4% to 16.2%.
Furthermore, it improves Dependency Resolution Quality (DRQ) from 34.8% to
65.9% and enhances Parent-Child Consistency (PCC) from 26.7% to 45.5%. Interestingly, traditional lexical metrics fail to capture this divergence; both methodologies achieved
an identical 91% average CodeBLEU score, effectively masking Standard RAG’s structural
failures behind syntactically plausible but broken code.
However, the results indicate that Graph RAG is not strictly superior across all dimensions. Providing the LLM with dense, global structural context introduces new vulnerabilities: Graph RAG suffers a severe degradation in Cyclomatic Complexity Consistency
(dropping from Standard RAG’s 71.6% to 46.7%) due to defensive over-engineering
by the LLM, alongside a slight drop in Docstring Preservation (67.0% down to 61.0%)
caused by prompt attention dilution.
Ultimately, this research validates that while Graph RAG trades an increase in code complexity
for critical reductions in API hallucinations, it offers a substantially more viable and architecturally sound path for automated enterprise codebase modernisation.
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Voice activity detection (VAD) plays a vital role in enabling applications such as speech recognition. We analyze the impact of window size on the accuracy of three VAD algorithms: Silero, WebRTC, and Root Mean Square (RMS) across a set of diverse real-world digital audio streams. We additionally explore the use of hysteresis on top of each VAD output. Our results offer practical references for optimizing VAD systems. Silero significantly outperforms WebRTC and RMS, and hysteresis provides a benefit for WebRTC.
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Conformal prediction provides rigorous uncertainty guarantees for model outputs but can produce prohibitively large prediction sets in structured domains such as routing, planning, or sequential recommendation. We introduce \emph{graph-based conformal compression}, a framework for constructing compact subgraphs that preserve the statistical validity of conformal prediction while reducing structural complexity. We study a formulation that selects a smallest subgraph capturing a prescribed fraction of conditional probability mass, and reduce to a weighted version of densest $k$-subgraphs in hypergraphs, in the regime where the subgraph has a large fraction of edges. We design efficient approximation algorithms that achieve constant factor coverage and size trade-offs. Our results highlight an algorithmic regime, distinct from classical densest-$k$-subgraph hardness settings, where the problem can be approximated efficiently, bridging conformal prediction with combinatorial graph compression. We finally validate our algorithmic approach on synthetic and real-world instances of trip planning and navigation, showing in each case that our approach handily beats natural baselines.
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Source-to-source compilers may perform inefficiently by executing transpilation passes on scripts that do not contain the specific language features a pass is designed to transform, potentially leading to redundant processing. A compiler can analyze a script to generate a per-script feature map, for example, by identifying language features in its abstract syntax tree (AST). Before executing a transpilation pass, the compiler can check this map and may bypass the pass for that script if the specific feature targeted by the pass is not present. This feature map can also be dynamically updated throughout the compilation process as other passes transform the code. This method of conditional pass execution based on content-aware analysis may reduce redundant AST traversals, which could decrease overall compilation time and computational resource consumption.
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Preview abstract
Source-to-source compilers may perform inefficiently by executing transpilation passes on scripts that do not contain the specific language features a pass is designed to transform, potentially leading to redundant processing. A compiler can analyze a script to generate a per-script feature map, for example, by identifying language features in its abstract syntax tree (AST). Before executing a transpilation pass, the compiler can check this map and may bypass the pass for that script if the specific feature targeted by the pass is not present. This feature map can also be dynamically updated throughout the compilation process as other passes transform the code. This method of conditional pass execution based on content-aware analysis may reduce redundant AST traversals, which could decrease overall compilation time and computational resource consumption.
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Peeking Ahead of the Field Study: Exploring VLM Personas as Support Tools for Embodied Studies in HCI
Xinyue Gui
Ding Xia
Mark Colley
Yuan Li
Vishal Chauhan
Anubhav Anubhav
Ehsan Javanmardi
Stela Hanbyeol Seo
Chia-Ming Chang
Manabu Tsukada
Takeo Igarashi
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI 26)
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Field studies are irreplaceable but costly, time-consuming, and error-prone, which need careful preparation. Inspired by rapid-prototyping in manufacturing, we propose a fast, low-cost evaluation method using Vision-Language Model (VLM) personas to simulate outcomes comparable to field results. While LLMs show human-like reasoning and language capabilities, autonomous vehicle (AV)-pedestrian interaction requires spatial awareness, emotional empathy, and behavioral generation. This raises our research question: To what extent can VLM personas mimic human responses in field studies? We conducted parallel studies: 1) one real-world study with 20 participants, and 2) one video-study using 20 VLM personas, both on a street-crossing task. We compared their responses and interviewed five HCI researchers on potential applications. Results show that VLM personas mimic human response patterns (e.g., average crossing times of 5.25 s vs. 5.07 s) lack the behavioral variability and depth. They show promise for formative studies, field study preparation, and human data augmentation.
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We introduce AASE (Activation-based AI Safety Enforcement), a framework for post-perception safety monitoring in large language models. Unlike pre-perception approaches that analyze input or output text, AASE monitors the model's internal activation patterns—what the model "understands" rather than what text it processes or generates—enabling detection of safety-relevant states before harmful outputs are produced. The framework comprises three techniques: Activation Fingerprinting (AF) for harmful content detection, Agent Action Gating (AAG) for prompt injection defense, and Activation Policy Compliance (APC) for enterprise policy enforcement. We introduce paired contrastive training to isolate safety-relevant signals from confounding factors such as topic and style, addressing signal entanglement in polysemantic activations. Validation across 7 models from 3 architecture families shows strong class separation: Gemma-2-9B achieves AUC 1.00 with 7.2σ separation across all probes; AAG achieves AUC ≥0.88 across all models on the InjecAgent benchmark; APC achieves 0.97-1.00 AUC across three enterprise policies. Model size correlates with probe quality—Gemma-2-9B (7.2σ separation) outperforms Gemma-2-2B (4.3σ). All techniques survive INT4 quantization with minimal separation degradation. AASE is 9× faster than Llama Guard 3 (33ms vs 306ms) with higher TPR (88% vs 50%) at a tunable threshold that trades FPR for detection sensitivity, adding only 0.002ms probe overhead to existing inference.
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Validating conversational artificial intelligence (AI) for regulated medical software applications may present challenges, as static test datasets and manual review may be limited in identifying emergent, conversational anomalies. A multi-agent AI system may be configured in a closed-loop for automated validation. The system can, for example, utilize an end user persona simulator agent to generate prompts for a target model and a domain /regulatory expert adjudicator agent to evaluate the target model’s responses against a configurable rubric. A meta-analysis agent can analyze anomalies to identify underlying vulnerabilities, which may then be used to programmatically synthesize new adversarial personas. This adaptive process can generate evidence to support regulatory compliance and continuous performance monitoring for medical software algorithms systems.
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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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Managing and Securing Google's Fleet of Multi-Node Servers
Richard Hanley
Havard Skinnemoen
Andrés Lagar-Cavilla
Michael Wong
Jeff Andersen
Kishan Prasad
Patrick Leis
Shiva Rao
Chris Koch
Jad Baydoun
Anna Sapek
Communications of the ACM, 69:3 (2026), pp. 82 - 92
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Server hardware and software co-design for a secure, efficient cloud.
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CoDaS: AI Co-Data-Scientist for Biomarker Discovery via Wearable Sensors
Juro Gottweis
CJ Park
Salman Rahman
Ahmed Metwally
Hong Yu
Ivor Rendulic
Yuzhe Yang
Petar Sirkovic
Daniel McDuff
Shwetak Patel
Nicolas Stroppa
Yubin Kim
Mark Malhotra
Orson Xu
Sam Schmidgall
Tim Althoff
Elahe Vedadi
Cynthia Breazeal
Hae Won Park
(2026)
