探索全球前沿学术脉络

Agentic Autodiscovery of Diastolic Dysfunction Phenotypes from Surface Electrocardiogram

Background: Left ventricular diastolic dysfunction (LVDD) is a major determinant of heart failure (HF), yet its assessment relies on multiparametric echocardiography, limiting scalability. We previously demonstrated that generative artificial intelligence (AI) can synthesize tissue Doppler imaging (TDI) waveforms from the 12-lead ECG. The growing complexity of candidate architecture creates a need for automated model-discovery frameworks. Objectives: To evaluate agentic AI-based auto-discovery for ECG-based LVDD assessment using either raw ECG or synthetic TDI waveforms. Methods: Two attention-based agentic AI architectures were developed using an automated large language model-driven refinement framework that optimized transfer-learning and multimodal architectures through autonomous proposal, validation, and selection of candidate model configurations. Development was performed in 1,011 paired ECG-echocardiography studies and externally validated in 983 patients using two reference frameworks: (i) data-driven phenogroups and (ii) the 2025 ASE Diastolic Function Guidelines. External validation was performed in CODE-15% (n=219,567) for HF-related mortality and EchoNext (n=35,718) for structural heart disease associations. Results: Despite the modest cohort size, the ECG-based agentic search achieved area under the receiver operating characteristic curve (AUCs) of 0.87 (95% CI: 0.85-0.89) and 0.83 (95% CI: 0.80-0.86) for phenogroup and guideline-based LVDD severity classification. Corresponding AUCs for the synthetic TDI-based model were 0.82 (95% CI: 0.80-0.85) and 0.80 (95% CI: 0.77-0.84), respectively. In large-scale external validation, both models stratified incident HF mortality with subdistribution hazard ratios ranging 5.5 to 9.5 (Gray's p

SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning

Spatial reasoning, the ability to determine where objects are, how they relate, and how they move in 3D, remains a fundamental challenge for vision-language models (VLMs). Tool-augmented agents attempt to address this by augmenting VLMs with specialist perception modules, yet their effectiveness is bounded by the action interface through which those tools are invoked. In this work, we study how the design of this interface shapes the agent's capacity for open-ended spatial reasoning. Existing spatial agents either employ single-pass code execution, which commits to a full analysis strategy before any intermediate result is observed, or rely on a structured tool-call interface that often offers less flexibility for freely composing operations or tailoring the analysis to each task. Both designs offer limited flexibility for open-ended, complex 3D/4D spatial reasoning. We therefore propose SpatialClaw, a training-free framework for spatial reasoning that adopts code as the action interface. SpatialClaw maintains a stateful Python kernel pre-loaded with input frames and a suite of perception and geometry primitives, letting a VLM-backed agent write one executable cell per step conditioned on all prior outputs, enabling the agent to flexibly compose and manipulate perception results and adapt its analysis to both intermediate text and visual observations and the demands of each problem. Evaluated across 20 spatial reasoning benchmarks spanning a broad range of static and dynamic 3D/4D spatial reasoning tasks, SpatialClaw achieves 59.9% average accuracy, outperforming the recent spatial agent by +11.2 points, with consistent gains across six VLM backbones from two model families without any benchmark- or model-specific adaptation.