arXiv:2603.26783v1 Announce Type: cross Abstract: Diffusion models can be challenged in the low signal-to-noise regime, where they have to make pixel-level predictions despite the presence of high noise. The geometric intuition is akin to using the finest stroke for oil painting throughout, which may be ineffective. We therefore study stroke-size control as a controlled intervention […]
DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting
arXiv:2603.26800v1 Announce Type: cross Abstract: Long-term fluid dynamics forecasting is a critically important problem in science and engineering. While neural operators have emerged as a promising paradigm for modeling systems governed by partial differential equations (PDEs), they often struggle with long-term stability and precision. We identify two fundamental failure modes in existing architectures: (1) local […]
Fractional epidemics from quantum loops
arXiv:2603.26708v1 Announce Type: cross Abstract: Classical compartmental models of epidemiology rely on well-mixed, local interaction approximations that fail to capture the heavy-tailed burst dynamics and long-range spatial correlations observed in real-world outbreaks. While fractional calculus is frequently employed to model these anomalous behaviors, fractional operators are introduced phenomenologically. In this work, we demonstrate that fractional […]
A Multimodal Deep Learning Framework for Edema Classification Using HCT and Clinical Data
arXiv:2603.26726v1 Announce Type: cross Abstract: We propose AttentionMixer, a unified deep learning framework for multimodal detection of brain edema that combines structural head CT (HCT) with routine clinical metadata. While HCT provides rich spatial information, clinical variables such as age, laboratory values, and scan timing capture complementary context that might be ignored or naively concatenated. […]
ReCQR: Incorporating conversational query rewriting to improve Multimodal Image Retrieval
arXiv:2603.26669v1 Announce Type: cross Abstract: With the rise of multimodal learning, image retrieval plays a crucial role in connecting visual information with natural language queries. Existing image retrievers struggle with processing long texts and handling unclear user expressions. To address these issues, we introduce the conversational query rewriting (CQR) task into the image retrieval domain […]
Contextual Graph Representations for Task-Driven 3D Perception and Planning
arXiv:2603.26685v1 Announce Type: cross Abstract: Recent advances in computer vision facilitate fully automatic extraction of object-centric relational representations from visual-inertial data. These state representations, dubbed 3D scene graphs, are a hierarchical decomposition of real-world scenes with a dense multiplex graph structure. While 3D scene graphs claim to promote efficient task planning for robot systems, they […]
The Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation
arXiv:2603.28387v1 Announce Type: new Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts. We evaluate 12 open-weight vision-language models (VLMs) on binary classification across two clinical neuroimaging cohorts, textscFOR2107 (affective disorders) and textscOASIS-3 (cognitive decline). Both datasets come with structural MRI data that carries no reliable individual-level diagnostic […]
The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGENIE from its Bottle
arXiv:2603.28643v1 Announce Type: new Abstract: Psychological scale development has traditionally required extensive expert involvement, iterative revision, and large-scale pilot testing before psychometric evaluation can begin. The `AIGENIE` R package implements the AI-GENIE framework (Automatic Item Generation with Network-Integrated Evaluation), which integrates large language model (LLM) text generation with network psychometric methods to automate the early […]
Aesthetic Assessment of Chinese Handwritings Based on Vision Language Models
arXiv:2603.26768v1 Announce Type: cross Abstract: The handwriting of Chinese characters is a fundamental aspect of learning the Chinese language. Previous automated assessment methods often framed scoring as a regression problem. However, this score-only feedback lacks actionable guidance, which limits its effectiveness in helping learners improve their handwriting skills. In this paper, we leverage vision-language models […]
Towards a Medical AI Scientist
arXiv:2603.28589v1 Announce Type: new Abstract: Autonomous systems that generate scientific hypotheses, conduct experiments, and draft manuscripts have recently emerged as a promising paradigm for accelerating discovery. However, existing AI Scientists remain largely domain-agnostic, limiting their applicability to clinical medicine, where research is required to be grounded in medical evidence with specialized data modalities. In this […]
SimulCost: A Cost-Aware Benchmark and Toolkit for Automating Physics Simulations with LLMs
arXiv:2603.20253v1 Announce Type: cross Abstract: Evaluating LLM agents for scientific tasks has focused on token costs while ignoring tool-use costs like simulation time and experimental resources. As a result, metrics like pass@k become impractical under realistic budget constraints. To address this gap, we introduce SimulCost, the first benchmark targeting cost-sensitive parameter tuning in physics simulations. […]
AI Meets Mathematics Education: A Case Study on Supporting an Instructor in a Large Mathematics Class with Context-Aware AI
arXiv:2603.26679v1 Announce Type: cross Abstract: Large-enrollment university courses face persistent challenges in providing timely and scalable instructional support. While generative AI holds promise, its effective use depends on reliability and pedagogical alignment. We present a human-centered case study of AI-assisted support in a Calculus I course, implemented in close collaboration with the course instructor. We […]