Papers.

System of Intelligent Actors: The DevOps chapter

Risk and Russia's Takeover of Yukos: Why Russian and World Oil Production May Peak Even if There is No Scarcity of Oil

Harpocrates: Oblivious Privacy in a Statically Typed World

Cluster Formation and Encrypted search in Big Data

Development of a Fully Non-Viral 1XX-enhanced BCMA CAR-T Cell Therapy for Multiple Myeloma

NSF CI Compass Virtual Workshop Report: AI meets CI - Intelligent Infrastructure for Major & Midscale Facilities

truera/trulens: TruLens 2.7.0

Active Rules for Embedded Databases: Lightweight Event-Driven Query Processing for Resource-Constrained Devices.

Shape Prior Fusion for Efficient 3D Reconstruction

Creating accurate 3D models from images is a challenging problem in computer vision, especially for applications like autonomous driving, virtual reality, and mobile computing. Traditional Multi-View Stereo (MVS) methods rely on geometry to reconstruct shapes but often struggle with missing details due to occlusions or textureless surfaces. In this work, we introduce a learning-based approach that enhances MVS by integrating deep priors, allowing for more complete and accurate 3D reconstructions with fewer input images. Our method refines fine details using masked silhouette and depth map losses, ensuring better shape recovery. Tested on the ShapeNet dataset, our model achieves a Chamfer Distance of 0.018 and an IoU of 0.80, outperforming classical MVS while also improving surface coverage to 91.1% and density score to 85.7%. By combining deep learning with traditional geometry, our approach makes 3D reconstruction more reliable and efficient, opening new possibilities for real-world applications.

What do news readers want?

Using a novel dataset covering the complete history of individual-level web traffic and digital subscriptions from a major metropolitan newspaper in the United States between 2020 and 2024, we investigate consumers' willingness to pay for different categories of news content, with particular focus on the kinds of coverage believed to generate civic externalities. Our identification strategy relies on the quasi-random arrival of paywall events which force consumers to subscribe if they wish to continue reading. Using this variation, we estimate a model of consumer demand and construct the optimal staff allocation for the paper under different counterfactual revenue models: a fully subscription-based model and a fully ad-supported model. Our results suggest that readers are willing to pay for local reporting, and that measures of demand based only on time-use substantially underestimate the value of “hard” news coverage on topics like local politics and public health. However, digital subscription revenues alone are insufficient to cover staff costs even at the highest revenue-generating sections of the paper. We use our model to estimate the subsidy required to expand the newspaper's production of investigative coverage.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

A Common Language for Responsible AI: Methods and Insights from a Fieldwide Consensus-Building Effort

A Deep Learning Approach to Quantitative PCR that Learns from Ground Truth

INSURE-Dial: A Phase-Aware Conversational Dataset & Benchmark for Compliance Verification and Phase Detection

Administrative phone tasks drain roughly 1 trillion USD annually from U.S. healthcare, with over 500 million insurance-benefit verification calls manually handled in 2024. We introduce INSURE-Dial, to our knowledge the first public benchmark for developing and assessing compliance-aware voice agents for phase-aware call auditing with span-based compliance verification. The corpus includes 50 de-identified, AI-initiated calls with live insurance representatives (mean 71 turns/call) and 1,000 synthetically generated calls that mirror the same workflow. All calls are annotated with a phase-structured JSON schema covering IVR navigation, patient identification, coverage status, medication checks (up to two drugs), and agent identification (CRN), and each phase is labeled for Information and Procedural compliance under explicit ask/answer logic. We define two novel evaluation tasks: (1) Phase Boundary Detection (span segmentation under phase-specific acceptance rules) and (2) Compliance Verification (IC/PC decisions given fixed spans). Per-phase scores are strong across small, low-latency baselines, but end-to-end reliability is constrained by span-boundary errors. On real calls, full-call exact segmentation is low, showing a gap between conversational fluency and audit-grade evidence.

Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey.

Dual Dopaminergic and Limbic-Cognitive Contributions to Gait Parameters in De Novo Parkinson Disease

Stageleft: Multi-stage Programming in Standard Rust

Rust has emerged as a popular systems language with growing interest in metaprogramming, yet it lacks staging support—developers must write unsafe, untyped macros instead. We present Stageleft, a library that brings type-safe staged programming to standard Rust without compiler modifications. Stageleft ensures hygienic code generation through ahead-of-time AST analysis, and handles free variables via a trait system that respects Rust's ownership rules. Stageleft demonstrates that staging can be practical and safe in Rust, enabling domain-specific optimizations while maintaining familiar developer interfaces.

In Code They Think; In Proof We Trust

A preemptive strike against exfiltration

Beyond Semantic Similarity: Explicit Intent Modeling for Query-Product Matching

Buyer intent in e-commerce is multi-faceted and is expressed through explicit attributes—such as brand, size, color, and material, rather than through general topical relevance. However, many state-of-the-art scalable query-product matching systems rely on aggregate representations, scoring a single query embedding against a single item embedding. While efficient, this aggregation frequently fails to satisfy individual attribute intent: items can be semantically related, yet violate key aspects specified in the query. In contrast, fine-grained interaction methods can better capture aspect-level constraints, but are typically too expensive due to increased run-time computation and storage costs. We propose an aspect-aware ranking framework that retrieves and resolves aspects in queries and performs fine-grained semantic affinity match against aspects in products to compute an aggregate query-product level aspect affinity score. The proposed approach integrates (i) query aspect resolution (canonicalization) using structured aspect data, (ii) a model to learn granular aspect affinity signal capturing individual aspect-level understanding; and iii) an efficient design for online serving, significantly cutting cost associated with inference speed and storage. This design preserves the scalability of two-tower retrieval while substantially improving explicit intent satisfaction.

Context Viewer: Turning LLM Contexts into Analyzable Artifacts

Nexa: Automatically Surfacing Business Impacting Insights in E-commerce Applications

Internet-scale e-commerce storefronts serve millions of users (and increasingly user appointed agents). These storefronts are being rearchitected as compound AI systems with agentic workflows for customer interactions and backend processing. As this AI transformation and agentic economy is underway, product teams need to get actionable insights into business-impacting outcomes. Classical approaches such as static funnels or static dashboards cannot deal with the scale, diversity, and contextual interactions that happen over billions of user interactions. As such, we need novel agentic approaches to automatically surface business-impacting insights. We present Nexa, an agentic framework that surfaces business insights automatically. We formalize the target of automated insight discovery in terms of Contrastive Stateful Trajectories (CST): a structural specification over contextual and sequential behavioral patterns whose presence or absence significantly shifts a business KPI across user cohorts. Nexa satisfies three design requirements simultaneously: expressivity through the CST abstraction, scalability through a custom analytics backend for CST computations, and explainability by overlaying usable presentation layers for analysts to verify the insights. We demonstrate Nexa on representative workloads and show that it surfaces actionable contextual patterns spanning user, app, agent, and backend behaviors.

Tracking Capabilities for Safer Agents

AI agents that interact with the real world through tool calls pose fundamental safety challenges: agents might leak private information, cause unintended side effects, or be manipulated through prompt injection. To address these challenges, we propose to put the agent in a programming-language-based "safety harness": instead of calling tools directly, agents express their intentions as code in a capability-safe language: Scala 3 with capture checking. Capabilities are program variables that regulate access to effects and resources of interest. Scala's type system tracks capabilities statically, providing fine-grained control over what an agent can do. In particular, it enables local purity, the ability to enforce that sub-computations are side-effect-free, preventing information leakage when agents process classified data. We demonstrate that extensible agent safety harnesses can be built by leveraging a strong type system with tracked capabilities. Our experiments show that agents can generate capability-safe code with no significant loss in task performance, while the type system reliably prevents unsafe behaviors such as information leakage and malicious side effects.

Whose Knowledge Counts? Co-Designing Community-Centered AI Auditing Tools with Educators in Hawai'i

Although generative AI is being deployed into classrooms with promises of aiding teachers, educators caution that these tools can have unintended pedagogical repercussions, including cultural misrepresentation and bias. These concerns are heightened in low-resource language and Indigenous education settings, where AI systems frequently underperform. We investigate these challenges in Hawai`i, where public schools operate under a statewide mandate to integrate Hawaiian language and culture into education. Through four co-design workshops with 22 public school educators, we surfaced concerns about using generative AI in educational settings, particularly around cultural misrepresentation, and corresponding designs for auditing tools that address these issues. We find that educators envision tools grounded in specific Hawaiian cultural values and practices, such as tracing the genealogy of knowledge in source materials. Building on these insights, we conceptualize AI auditing as a community-oriented process rather than the work of isolated individuals, and discuss implications for designing auditing tools.

TimelyLLM: Time-sensitive LLM Serving System for Physical-I/O Limited Agents

Large Language Models (LLMs) are increasingly integrated into Physical-I/O limited agents, such as robots and voice assistants, which execute outputs sequentially. However, existing LLM serving systems typically employ a throughput-oriented batching mechanism, ignoring the large gap between LLM generation speed and the constrained physical I/O rates of agents, thus wasting execution slack and worsening resource contention. Besides, they treat all tokens equally and cannot anticipate the execution implications of different content, preventing scheduling aligned with agent-side behavior. To address it, we propose a new system named TimelyLLM that coordinates LLM generation with the physical behavior of agents. TimelyLLM introduces a novel segmented generation and scheduling mechanism, strategically leveraging the time gap between agent plan generation and execution to reduce contention and improve response latency under multi-agent workloads. We implement TimelyLLM on top of a widely-used LLM serving framework. We also build a dataset collection system to construct serving workloads from real-world robots, including drones, robot arms, and quadruped robots. Our evaluation demonstrates that TimelyLLM improves the time utility up to 1.52×, and reduces the overall waiting time by 84%.

Behavioral Transfer via Automated Prompt Optimization and LLM-as-a-Judge Evaluation Loops for Prompt-Based Knowledge Distillation

Large language models (LLMs) are efficient and expensive to implement. We present APO-KD, a framework that uses zero-fine-tuning to replicate the observable behavior of a teacher LLM using a cheaper student by maximising discrete packages of prompts rather than weights. APO-KD (i) produces teacher reference outputs, (ii) executes the student with candidate prompts, (iii) assesses alignment with an LLM-as-a-Judge rubric in terms of answer quality, format fidelity, constraint adherence, and consistency, and (iv) rewrites prompts based on judge feedback. On limited bullet-point summarization and code generation (function and unit tests only), distilled prompts are much more effective in getting students to comply and lowering the behavior gap to the teacher compared to zero-shot and manual prompts, and can be deployed quickly and with control using a small budget when fine-tuning is not feasible.

Agent-Centric Column-Aware Prompt Optimization for Structured Data Tasks with LLMs

Large language models (LLMs) are being used more commonly in applications that use structured data (tables and databases) in reasoning. Nonetheless, naive few-shot prompt selection tends to disregard the underlying schema, which results in poor performance and higher rates of hallucinations. The paper presents a column-aware few-shot prompt optimization model that uses schema knowledge and a multi-objective search based on Optuna to sample and rank examples to solve structured data problems. The proposed system has tunable parameters, which include the selection of columns, examples, and formatting, and are optimized based on the desired metrics, including the accuracy of the tasks and the rate of hallucinations. Table question answering, report generation and analytics assistant task experiments show that faster convergence and better performance are realized by column-aware optimization than by random or best-of-one/best-of-three selection. The framework is also incorporated into an agent optimization platform, which allows automatic and adaptive timely management of production environments.

BulletTime: Time Dilation for High-Fidelity Tracing

Much of computer systems and architecture research depends on accurate, high-fidelity program tracing for simulation, profiling, and debugging. Unfortunately, with significant improvements in compute and memory instruction execution speeds, tracing frameworks incur frequent I/O to persist traced events to disk. We find that such delays can be bursty and asymmetric across application threads, resulting in an inadvertent reordering of application and system operations relative to untraced execution. In our analysis, such reordering often leads to significant changes in the behavior of the studied application, thereby contaminating insights from simulation and profiling studies of the corresponding captured traces. In this work, we formalize the application behavior under study to establish correctness requirements for traced application execution in the presence of tracing-induced delays. We propose a novel time dilation approach that strategically slows execution for application and system threads while meeting correctness requirements. We implement the time-dilation approach in Bul-letTime, a tracing framework built atop Pin, the de facto binary instrumentation tool. We evaluate BulletTime for memorycontiguity and synchronization studies on real-world applications and workloads. Our results show that while existing tracing approaches can cause application behavior to deviate by as much as $20 \times$ compared to untraced execution, BulletTime's deviations are < 10% even in extreme cases of asymmetric tracing delays.

High-Performance Durable LLM Observability at Scale Architecture and Benchmarking of the Opik Platform

The current trend to implement large language models (LLMs) in production settings has generated a must-have requirement to embed scalable, lasting, and high-fidelity observability solutions. Conventional logging and ephemeral tracing is not detailed enough to capture the interactions which are complex and multi-turned as well as tool augmented of the modern LLM applications. This paper describes Opik, a high-performance observability platform, developed to provide both long-term and real-time trace retention of systems based on LLM. Opik has an asynchronous ingestion pipeline, hierarchical trace models, and a hybrid storage design that combines time-series and a vector database. By stringent benchmarking against the set standards we prove that Opik has better ingestion latency, efficient storage and query performance, enabling advanced analytics and continuous regression analysis. The outcomes of our work set Opik to be a future-proof implementation of the LLM observability at enterprise level.

Evaluating Reliability Gaps in Large Language Model Safety via Repeated Prompt Sampling

Fairness and Bias Management in Health AI: consensus-Based Recommendations for Best Practices Across the AI Lifecycle

In a landscape marked by uneven oversight, consensus-based guidance plays a crucial role in advancing AI fairness and managing bias. It contributes to a shared understanding across disciplines, ena...

Microfluidic capillary transit velocity as a functional measure for sickle cell disease and in vitro -derived red blood cells

Impact of using artificial intelligence as a second reader in breast screening including arbitration

The impact of incorporating artificial intelligence (AI) into a double-read breast-screening workflow, including arbitration, is unclear. This retrospective study included 50,000 representative women from two NHS breast-screening centers. All the women had long-term follow-up, allowing us to determine whether use of AI leads to earlier cancer detection. Cases requiring arbitration (8,732 cases) were read by 22 readers in a reader study, following their normal arbitration workflow. Overall, after arbitration, replacing the second reader with AI was noninferior (5% margin) to two human readers in terms of sensitivity and specificity (P < 0.001) while offering a workload benefit. Arbitration improved the specificity of the AI arm by overruling cases incorrectly recalled by the AI tool; however, it also overruled the AI tool recall decision for some interval and next-round cancers. Further development of the AI tool alongside improvement in its explainability could lead to the earlier detection of cancers.

Diagnostic accuracy, fairness and clinical implementation of AI for breast cancer screening: results of multicenter retrospective and prospective technical feasibility studies

Artificial intelligence (AI) promises to enhance breast cancer screening. Here we evaluated Google’s mammography AI system (version 1.2) across two phases: a retrospective study using 115,973 mammograms from five National Health Service screening services with 39-month follow-up and prospective noninterventional feasibility deployment at 12 sites (9,266 cases). The primary endpoint was AI sensitivity and specificity versus first reader using a 5% noninferiority margin. The secondary endpoints were performance versus second or consensus readers and breast-level analyses. Retrospectively, AI achieved superior sensitivity (0.541 versus 0.437 for first reader, P < 0.001) and noninferior specificity (0.943 versus 0.952, P < 0.001). Cancer detection rate increased from 7.54 to 9.33 per 1,000 women, with AI detecting 25.0% of interval cancers. Performance was particularly strong for first screens (39.3% fewer recalls, 8.8% higher detection) and invasive cancers. No systematic demographic disparities were observed. Simulated second-reader replacement reduced reading time by 32% while increasing detection by 17.7%. Prospective deployment confirmed technical feasibility but revealed a distribution shift requiring threshold recalibration. Implementation requires adaptive calibration and continuous monitoring to ensure safety and equity.

Harmonizing standards and resources for the medical genome

Genome modelling and design across all domains of life with Evo 2

SmartCert: A Multi-modal framework for automated guided vehicle screening

Arrayed dual-gRNA CRISPR screening platform for C9orf72 repeat expansion excision in patient iPSCs

Interventional genomics: Bridging germline diagnosis and therapeutic action

Towards a trade-off of interpretability, accuracy and scalability: Enhanced formulations in linear classification models

Assessing the robustness of evaluation metrics for synthetic ECG signal quality

Higher calcium levels exacerbate sickling of RBCs retaining mitochondria in pediatric patients with SCD

Planning the development of an AI-driven decision support architecture for the recognition of sudden cardiac arrest by 9-1-1 telecommunicators: report of a community engagement and brainstorming meeting.

Improving Model Safety by Targeted Error Correction

Scaling Healthcare Engagement with AI Voice Agents: A Real-World Evaluation Study

Neurophysiological Recovery Following Nerve Transfer Surgery to Restore Upper Limb Function after Cervical Spinal Cord Injury

Generalization of AI-Based Gestational Age Assessment Using Blind Sweep Ultrasonography

Key Points Question Can artificial intelligence (AI) models trained on blind sweep ultrasonography scans generalize to new clinical settings and perform as well as traditional sonographers on estimating gestational age? Findings In this diagnostic study of 385 participants, the AI model effectively generalized to new clinical environments and institutions, achieving a mean absolute error of 4.2 days, which was noninferior to the clinical standard. Meaning This AI system demonstrated strong potential to expand access to diagnostic tasks such as gestational age estimation, particularly in low-resource settings, by enabling novice operators to perform accurate ultrasonography assessments.

Model Card for OpenAI Privacy Filter

OpenAI Privacy Filter is a compact, bidirectional token-classification model for detecting and redacting personally identifiable information (PII) and secrets in unstructured text. The model is derived from an autoregressively pretrained checkpoint and converted into a bidirectional, banded-attention classifier that labels an input sequence in a single forward pass. A constrained Viterbi decoder produces coherent spans across eight privacy categories and exposes configurable operating points for precision-recall tradeoffs. Privacy Filter has 1.5 billion total parameters, 50 million active parameters per token, and a 128,000-token context window. It is designed for efficient local deployment and domain-specific fine-tuning. Privacy Filter is intended as a configurable data-minimization component within layered privacy workflows, not as an anonymization or compliance guarantee.

Eigenius: A Typed Knowledge-Graph DBMS with Epistemic Stratification and Institution-Mediated Reasoning

AMD SEV-SNP: A Confidential Computing Primer

AI Security Priorities: A Field-Wide Agenda

As AI systems are rapidly integrated into critical economic, governmental, and national security functions, the gap between AI adoption and AI security readiness continues to widen. This paper presents a prioritized agenda for advancing AI security, informed by structured interviews with leaders across industry, government, and civil society, and refined through a multi-sector expert workshop. Participants identified and ranked the highest-importance and most cost-effective areas where progress could strengthen AI security - from protecting frontier AI systems and their underlying infrastructure to improving cybersecurity practices as AI reshapes the threat landscape. The resulting priorities are organized across four themes: establishing strategic foundations and policy frameworks; advancing public-private coordination and institutional infrastructure; advancing technical security engineering and assurance; and governing agentic AI under adversarial pressure. For each priority area, expert authors provide detailed analyses that define the problem, assess the current landscape, and identify actionable projects that stakeholders across sectors can pursue. The paper aims to serve as an initial practical foundation for coordinated investment and action across the AI security field. It is designed to serve both current practitioners and individuals and organizations looking to enter the field by identifying concrete, high-impact contributions suited to a range of strengths and capacities.

Classifying Capabilities (Extended Version)

Capture checking in Scala 3 enables lightweight and practical effect and resource tracking by recording capabilities in types. However, the system offers no way to reason about kinds of capabilities. Natural constraints such as "retaining only the control-flow capabilities of this closure" or "excluding all thread-local capabilities from this argument" become inexpressible. Both arise in the Scala 3 standard library: "Try" re-throws caught exceptions, so it retains only the control-flow capabilities of its body, and "Future" must not capture thread-local resources. The inability to state these constraints has kept parts of the library outside capture checking. We introduce capability classifiers: a tree-structured, user-extensible hierarchy of tags that classify capabilities by their semantic role. Projections filter capture sets by classifier, supporting both inclusion ("c.only[C]") and exclusion ("c.except[C]"). The tree structure enables decidable disjointness reasoning: classifiers on separate branches are guaranteed to be disjoint regardless of unknown extensions elsewhere in the hierarchy. We formalize classifiers as an extension of System Capless, a core calculus for capture checking, introducing a classifier kind algebra based on intersection, union, and subtraction of classifier subtrees. We extend the operational semantics to model exception interception and establish type safety, effect safety, and handler coverage via a big-step proof, fully mechanized in Lean 4. Classifiers are implemented in the Scala 3 capture checker, and we demonstrate their use on standard library types and real-world effect exclusion patterns.