Publications – Google Research

Towards Conversational AI for Disease Management

Preview abstract While large language models (LLMs) have shown promise in diagnostic dialogue, their capabilities for effective management reasoning - including disease progression, therapeutic response, and safe medication prescription - remain under-explored. We advance the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) through a new LLM-based agentic system optimised for clinical management and dialogue, incorporating reasoning over the evolution of disease and multiple patient visit encounters, response to therapy, and professional competence in medication prescription. To ground its reasoning in authoritative clinical knowledge, AMIE leverages Gemini's long-context capabilities, combining in-context retrieval with structured reasoning to align its output with relevant and up-to-date clinical practice guidelines and drug formularies. In a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) study, AMIE was compared to 21 primary care physicians (PCPs) across 100 multi-visit case scenarios designed to reflect UK NICE Guidance and BMJ Best Practice guidelines. AMIE was non-inferior to PCPs in management reasoning as assessed by specialist physicians and scored better in both preciseness of treatments and investigations, and in its alignment with and grounding of management plans in clinical guidelines. To benchmark medication reasoning, we developed RxQA, a multiple-choice question benchmark derived from two national drug formularies (US, UK) and validated by board-certified pharmacists. While AMIE and PCPs both benefited from the ability to access external drug information, AMIE outperformed PCPs on higher difficulty questions. While further research would be needed before real-world translation, AMIE's strong performance across evaluations marks a significant step towards conversational AI as a tool in disease management. View details

MoXaRt: Audio-Visual Object-Guided Sound Interaction for XR

Preview abstract In Extended Reality (XR), complex acoustic environments often overwhelm users, compromising both scene awareness and social engagement due to entangled sound sources. We introduce MoXaRt, a real-time XR system that uses audio-visual cues to separate these sources and enable fine-grained sound interaction. MoXaRt's core is a cascaded architecture that performs coarse, audio-only separation in parallel with visual detection of sources (e.g. faces, instruments). These visual anchors then guide refinement networks to isolate individual sources, separating complex mixes of up to five concurrent sources (e.g. two voices + three instruments) with ca. 2 second processing latency. We validate MoXaRt through a technical evaluation on a new, complex dataset we collected, and a 22-participant user study. Our results demonstrate that MoXaRt significantly improves communication clarity—boosting listening comprehension in noisy conditions by 33.2% (p=0.0058)—and significantly reduces cognitive load (M=7.50 vs. M=3.36, p<0.001), paving the way for more perceptive and socially adept XR experiences. View details

Improved Differentially Private Algorithms for Rank Aggregation

Preview abstract Rank aggregation is a task of combining the rankings of items from multiple users into a single ranking that best represents the users' rankings. Alabi et al. (AAAI'22) presents differentially-private (DP) polynomial-time approximation schemes (PTASes) and 5-approximation algorithms with certain additive errors for the Kemeny rank aggregation problem in both central and local models. In this paper, we present improved DP PTASes with smaller additive error in the central model. Furthermore, we are first to study the footrule rank aggregation problem under DP. We give a near-optimal algorithm for this problem; as a corollary, this leads to 2-approximation algorithms with the same additive error as the 5-approximation algorithms of Alabi et al. for the Kemeny rank aggregation problem in both central and local models. View details

CrossCheck: Input Validation for WAN Control Systems

Preview abstract We present CrossCheck, a system that validates inputs to the Software-Defined Networking (SDN) controller in a Wide Area Network (WAN). By detecting incorrect inputs—often stemming from bugs in the SDN control infrastructure—CrossCheck alerts operators before they trigger network outages. Our analysis at a large-scale WAN operator identifies invalid inputs as a leading cause of major outages, and we show how CrossCheck would have prevented those incidents. We deployed CrossCheck as a shadow validation system for four weeks in a production WAN, during which it accurately detected the single incident of invalid inputs that occurred while sustaining a 0% false positive rate under normal operation, hence imposing little additional burden on operators. In addition, we show through simulation that CrossCheck reliably detects a wide range of invalid inputs (e.g., detecting demand perturbations as small as 5% with 100% accuracy) and maintains a near-zero false positive rate for realistic levels of noisy, missing, or buggy telemetry data (e.g., sustaining zero false positives with up to 30% of corrupted telemetry data). View details

Bi-level Hierarchical Neural Contextual Bandits for Online Recommendation

Preview abstract Contextual bandit algorithms aim to identify the optimal choice among a set of candidate arms, based on their contextual information. Among others, the neural contextual bandit algorithms have demonstrated generally superior performance compared to traditional linear and kernel-based methods. Nevertheless, neural methods are not inherently suitable to handle a large number of candidate arms due to their high computational cost when performing neural exploration. Motivated by the widespread availability of arm category information (e.g., movie genres, retailer types), we formulate contextual bandits into a bi-level recommendation problem based on the accessible arm category information, and propose a novel neural bandit framework, named H2N-Bandit, which utilizes a bi-level hierarchical neural structure to mitigate the substantial computational cost found in conventional neural bandit methods. To demonstrate its effectiveness, we provide the regret bound for H2N-Bandit under the over-parameterized neural bandit settings. Furthermore, to illustrate its efficiency, we conduct extensive experiments on multiple real-world public data sets with various specifications, showing that H2N-Bandit can significantly reduce the computational cost over existing non-linear methods while achieving better or comparable performances against state-of-the-art baselines. View details

Nudging Developers Toward Privacy: Evaluating the Impact of Personalized App Review Reports

Preview abstract Mobile application developers often struggle to create accurate privacy notices or implement robust privacy practices due to limited expertise or resources. While users share unsolicited privacy feedback in app reviews, and prior research has characterized this privacy feedback, uncovering developer reactions to this feedback remains unexplored. This study explores whether personalized privacy review reports---summarizing real user feedback for a developer's own app---can effectively nudge them toward planning privacy improvements. We surveyed 42 app developers, presenting them with reports containing privacy themes, temporal trends, peer benchmarks, and emotion distributions derived from their apps' reviews. Our findings indicate that these privacy report interventions proved highly effective, with 76% (32 of 42) of participants finding at least one section of the report useful. Furthermore, exposure to the report increased the participants' intent to pursue privacy-relevant actions -- such as reorganizing the UI, enhancing privacy communications, or adding/removing features -- with 69% (29 of 42) of participants indicating an increased intent to do so. Almost all developers expressed a desire to receive such privacy reports periodically or on demand. These results indicate that making this style of report broadly available across the industry could foster a more privacy-conscious mobile ecosystem. View details

Ten Insights from Other Domains That Inform Responsible AI Frameworks

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

An experimental evaluation of an AI-powered interactive learning platform

Preview abstract Generative AI, which is capable of transforming static content into dynamic learning experiences, holds the potential to revolutionize student engagement in educational contexts. However, questions still remain around whether or not these tools are effective at facilitating student learning. In this research, we test the effectiveness of an AI-powered platform incorporating multiple representations and assessment through Learn Your Way, an experimental research platform that transforms textbook chapters into dynamic visual and audio representations. Through a between-subjects, mixed methods experiment with 60 US-based students, we demonstrate that students who used Learn Your Way had a more positive learning experience and had better learning outcomes compared to students learning the same content through a digital textbook. These findings indicate that AI-driven tools, capable of providing choice among interactive representations of content, constitute an effective and promising method for enhancing student learning. View details

Diffusion Controller: Framework, Algorithms and Parameterization

Preview abstract Controllable generation with diffusion models is often treated as a collection of heuristics rather than a unified optimization problem. We propose a principled control formulation by viewing the diffusion reverse process as an instance of a (generalized) linearly-solvable Markov decision process (LS-MDP). This perspective turns controllable generation into regularized optimal control around a pretrained diffusion policy, yielding tractable objectives and algorithmic updates. Under this framework, we study two practical finetuning regimes. When paired target data are available, we obtain a supervised finetuning (SFT) objective. When only a terminal reward model is available, we derive reinforcement-learning finetuning (RLFT) methods from the LS-MDP solution structure, including (i) a reward-weighted regression loss and (ii) a policy-gradient approach (with standard extensions such as PPO). Crucially, the LS-MDP optimality conditions imply an explicit relationship between the optimal and pretrained score functions. We leverage this to derive a new score-function parameterization that isolates the control signal and enables “gray-box” finetuning with substantially fewer trainable parameters. Experiments across SFT and RLFT show this parameterization improves over existing finetuning baselines while achieving stronger sample/parameter efficiency. View details

A Dynamic Numerical Model for Real-Time Estimation of Latent Cognitive States Using Oculomotor Metrics

Preview abstract Estimating internal cognitive states from oculomotor data is fundamentally challenging due to their context-dependency and the complex relationship between various metrics. This paper proposes a dynamic numerical framework to model a task-specific behavioral signature and monitor deviations from it in real-time. The model integrates key oculomotor and motion metrics into a differential equation, yielding a continuous score that serves as an estimate of a latent cognitive state. By using tunable, heuristic parameters, our approach offers a transparent and adaptable alternative to opaque machine learning models. The framework’s strength lies in its ability to pinpoint objective changes in behavior, providing a potential tool for interpreting events like the onset of fatigue, distraction, or cognitive load. View details