Neural operator learning accelerates PDE solution by approximating operators as mappings between continuous function spaces. Yet in many engineering settings, varying geometry induces discrete structural changes, including topological changes, abrupt changes in boundary conditions or boundary types, and changes in the computational domain, which break the smooth-variation premise. Here we introduce Discrete Solution Operator Learning (DiSOL), a complementary paradigm that learns discrete solution procedures rather than continuous function-space operators. DiSOL factorizes the solver into learnable stages that mirror […]

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arXiv:2602.15876v1 Announce Type: new Abstract: Adapting instruction to the fine-grained needs of individual students is a powerful application of recent advances in large language models. These generative AI models can create tasks that correspond to students’ interests and enact context personalization, enhancing students’ interest in learning academic content. However, when there is a teacher in-the-loop creating or modifying tasks with generative AI, it is unclear how efficient this process might be, despite commercial generative AI tools’ claims that […]

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arXiv:2603.02270v1 Announce Type: new Abstract: Automated animal identification is a practical task for reuniting lost pets with their owners, yet current systems often struggle due to limited dataset scale and reliance on unimodal visual cues. This study introduces a multimodal verification framework that enhances visual features with semantic identity priors derived from synthetic textual descriptions. We constructed a massive training corpus of 1.9 million photographs covering 695,091~unique animals to support this investigation. Through systematic ablation studies, we identified […]

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arXiv:2604.07525v1 Announce Type: new Abstract: Harnessing the predictive capability of Markov process models requires propagating probability density functions (beliefs) through the model. For many existing models however, belief propagation is analytically infeasible, requiring approximation or sampling to generate predictions. This paper proposes a functional modeling framework leveraging sparse Sum-of-Squares (SoS) forms for valid (conditional) density estimation. We study the theoretical restrictions of modeling conditional densities using the SoS form, and propose a novel functional form for addressing such […]

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arXiv:2601.10773v1 Announce Type: new Abstract: Understanding large software systems is a challenging task, especially when code is distributed across multiple repositories and microservices. Developers often need to reason not only about the structure of the code, but also about its domain logic and runtime behaviors, which are typically implicit and scattered. We introduce LogicLens, a reactive conversational agent that assists developers in exploring complex software systems through a semantic multi-repository graph. This graph is built in a preprocessing […]

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arXiv:2604.06260v1 Announce Type: new Abstract: Test-time scaling investigates whether a fixed diffusion language model (DLM) can generate better outputs when given more inference compute, without additional training. However, naive best-of-$K$ sampling is fundamentally limited because it repeatedly draws from the same base diffusion distribution, whose high-probability regions are often misaligned with high-quality outputs. We propose $S^3$ (Stratified Scaling Search), a classical verifier-guided search method that improves generation by reallocating compute during the denoising process rather than only at […]

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arXiv:2603.06687v1 Announce Type: new Abstract: Geo-temporal understanding, the ability to infer location, time, and contextual properties from visual input alone, underpins applications such as disaster management, traffic planning, embodied navigation, world modeling, and geography education. Although recent vision-language models (VLMs) have advanced image geo-localization using cues like landmarks and road signs, their ability to reason about temporal signals and physically grounded spatial cues remains limited. To address this gap, we introduce TimeSpot, a benchmark for evaluating real-world geo-temporal […]

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The proliferation of healthcare time series data presents a transformative opportunity for personalized healthcare. However, utilizing these observational data for clinical decision support requires moving beyond risk prediction to causal inference. In particular, estimating time-varying individual treatment effects is crucial for answering “what-if” questions in dynamic settings, where patient states and treatment efficacy evolve continuously over time. Traditional statistical and machine learning methods face well-documented difficulties in capturing the long-range temporal dependencies, high dimensionality, and non-linear treatment-response dynamics […]

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arXiv:2602.22293v1 Announce Type: new Abstract: River systems operate as inherently interconnected continuous networks, meaning river hydrodynamic simulation ought to be a systemic process. However, widespread hydrology data scarcity often restricts data-driven forecasting to isolated predictions. To achieve systemic simulation and reduce reliance on river observations, we present GraphRiverCast (GRC), a topology-informed AI foundation model designed to simulate multivariate river hydrodynamics in global river systems. GRC is capable of operating in a “ColdStart” mode, generating predictions without relying on […]

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When marketing emails fail, they almost never fail loudly. The campaigns are queued. Dashboards look fine. Open rates dip a little, then a little more. Someone suggests testing a new subject line. Another person tweaks send time. What’s actually breaking isn’t copy or creative. It’s trust. And trust erodes quietly. I learned this running marketing technology and operations at Nextdoor, where we send hundreds of millions of emails a year to neighbors and businesses around the world. Some […]

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