发表机构
Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对多模态时序数据均匀离散化效率低的问题，提出EvtGraph框架，通过事件自适应压缩等技术实现预算约束下的高效图学习，在多模态临床等基准上性能优于基线且效率显著提升。
AI 中文摘要
多模态时序数据本质上存在不规则性与信息密度不均的问题，但大多数模型依赖均匀离散化处理，导致表示效率低下。我们提出EvtGraph这一统一框架，在明确的预算约束下使计算与时序显著性对齐。EvtGraph通过事件自适应压缩（EAMC）将序列重新参数化为事件级令牌，利用节点预算（NBC）选择紧凑子集，并执行时序约束的稀疏图推理（T2SG），这一过程将密集序列转化为对显著事件的结构化计算，在保留关键转换的同时降低了复杂度。我们证明该设计提供了在固定预算下分配表示能力的实用机制，实现了一致的性能-效率权衡，实践中较小的预算通常就足够。在多模态临床（MIMIC-IV + CXR）和跨域基准上的实验表明，EvtGraph的性能优于基于Transformer和循环神经网络的基线，同时效率显著提升。这些结果表明，预算约束下的以事件为中心的表示为从高冗余时序数据中学习提供了通用范式。
英文摘要
Multimodal temporal data are inherently irregular and uneven in information density, yet most models rely on uniform discretization, leading to inefficient representations. We propose \textbf{EvtGraph}, a unified framework that aligns computation with temporal salience under explicit budget constraints. EvtGraph reparameterizes sequences into event-level tokens via event-adaptive compression (EAMC), selects a compact subset with a node budget (NBC), and performs temporally constrained sparse graph reasoning (T2SG). This transforms dense sequences into structured computation over salient events, reducing complexity while preserving critical transitions. We show that this design provides a practical mechanism for allocating representational capacity under a fixed budget, yielding a consistent performance--efficiency trade-off, where a small budget is often sufficient in practice. Experiments on multimodal clinical (MIMIC-IV + CXR) and cross-domain benchmarks demonstrate that EvtGraph outperforms both Transformer-based and recurrent baselines while significantly improving efficiency. These results suggest that budget-constrained event-centric representation provides a general paradigm for learning from high-redundancy temporal data.
Comments9 page, 9 figures
