这论文搞了个ClinPRISM框架，专门搞定不规则时间序列的医疗问答，只用16个token和0.15秒回答一个问题，比现有大模型省资源还更准。
ClinPRISM是一个针对不规则临床时间序列（ICTS）问答的多模态LLM推理框架，采用4B参数LLM骨干。它通过不规则感知多尺度编码器捕获稀疏临床证据，使用时间证据蒸馏器将多尺度表示压缩为16个LLM兼容tokens。框架引入渐进对齐策略，将不规则轨迹与LLM文本嵌入空间对齐。训练数据包含30,000个临床时间序列和41,000个指令微调实例，覆盖11个任务。在保留评估基准上，ClinPRISM达到最先进性能，平均推理延迟仅0.15秒每问题。
A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Series
Question answering (QA) over irregular clinical time series (ICTS) plays a pivotal role in a wide range of healthcare applications. Although recent multimodal time-series large language models (LLMs) have shown considerable promise in general-purpose time-series QA, they remain poorly equipped to model the sparsity, asynchrony, and irregular sampling patterns of clinical observations. To fill this gap, we propose ClinPRISM, a cost-effective multimodal LLM reasoning framework for question answering over ICTS data. First, we devise an irregularity-aware multi-scale encoder to capture sparse clinical evidence at diverse temporal scales. Then, we propose a temporal evidence distiller to integrate representations across these scales and compress them into a small number of LLM-compatible tokens. Moreover, we introduce a progressive alignment strategy that sequentially aligns the irregular trajectories with the LLM's textual embedding space. To facilitate training, we construct 30,000 clinical time series paired with multi-scale descriptions, together with 41,000 instruction-tuning instances spanning 11 tasks. Using a 4-billion-parameter LLM backbone, ClinPRISM achieves state-of-the-art performance on the held-out evaluation benchmark while using only 16 time-series tokens and achieving an average inference latency of 0.15 seconds per question.