LLMODE解决了LLM处理不规则时空观测的难题，通过双源门控交叉注意力将外部时空证据注入冻结LLM。
LLMODE是一种用于不规则时空预测的令牌高效框架，使用冻结的LLM主干网络。该方法首先使用图感知ODE编码器将不规则图观测重建为连续时间潜在轨迹，然后通过固定预算Perceiver重采样器将可变长度轨迹压缩为固定数量的动态记忆令牌。实验在三个真实城市数据集和两个物理动力学基准上展示了竞争力，在稀疏或动态复杂的不规则采样条件下优势更明显。
LLMODE: Aligning ODEs with LLMs via Gated Token Injection for Irregular Spatio-Temporal Forecasting
Large language models (LLMs) have shown promise for spatio-temporal forecasting, but existing approaches often rely on regularly sampled token sequences and struggle with irregular observations because of temporal asynchrony, representation-space misalignment, and limited context windows. We propose LLMODE, a token-efficient framework for irregular spatio-temporal forecasting with a frozen LLM backbone. LLMODE first uses a graph-aware ODE encoder to reconstruct irregular graph observations as a continuous-time latent trajectory. A Fixed-Budget Perceiver Resampler then compresses this variable-length trajectory into a fixed number of dynamic memory tokens. In parallel, compact statistical descriptors are encoded and resampled into context memory tokens. A dual-source gated cross-attention module injects both memories into the frozen LLM, enabling controlled utilization of external spatio-temporal evidence. Experiments on three real-world urban datasets and two physical-dynamics benchmarks show competitive overall performance, with clearer advantages under sparse or dynamically complex irregular sampling. Additional evaluations on unseen urban regions further demonstrate strong zero-shot generalization without adaptation.