发表机构
Tsinghua University; State Key Laboratory of Space Network and Communications, Tsinghua University（清华大学; 清华空间网络与通信国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种面向原生AI通信的信息论框架，以源诱导的预测状态为保真度对象，将通信失真定义为预测性能损失，实现从源重构到预测状态保留的转变。
AI 中文摘要
原生AI通信日益旨在支持预测，而非复现源的每一个细节。这一转变引出了一个常规源编码未明确提及的基本问题：当终端目标是预测时，应保留什么？我们将源诱导的预测状态作为保真度对象，它是指定未来在源观测和共享上下文条件下的分布。我们证明该状态是精确预测保留的充分且最小的条件。终端预测损失随后将通信失真定义为预测性能的损失，而非源重构误差。在对数损失下，该失真等于通信过程中损失的条件互信息。以接收端预测状态作为贝叶斯基准，我们将AI接收端误差分为三类：通信中损失的预测价值、模型族无法利用的接收端可用价值，以及部署模型未实现的族能力。同样的预测状态也足以实现匹配压缩。对于有限字母无记忆源，访问原始源相比直接编码该状态无速率-失真优势。将相同的目标条件构造应用于序列预测，会揭示出一个动态边界：固定时域诱导的状态对于该时域是最小的，但当目标窗口移动时可能无法支持递归更新；将整个未来作为目标则会产生一个最小的全未来状态，该状态可递归更新并具有马尔可夫表示。这些结果共同将AI通信从源重构转向预测状态保留。
英文摘要
AI-native communication increasingly aims to support prediction rather than reproduce every detail of the source. This shift raises a basic question left implicit by conventional source coding: what should be preserved when the terminal goal is prediction? We take the source-induced predictive state as the fidelity object. It is the distribution of the specified future conditioned on the source observation and shared context. We show that this state is sufficient and minimal for exact predictive preservation. The terminal prediction loss then defines communication distortion as lost predictive performance rather than source reconstruction error. Under logarithmic loss, this distortion equals the conditional mutual information lost through communication. Using the receiver-side predictive state as a Bayes reference, we separate AI-receiver error into predictive value lost in communication, receiver-available value unusable by the model family, and family capability not realized by the deployed model. The same predictive state also suffices for matched compression. For finite-alphabet memoryless sources, access to the raw source gives no rate-distortion advantage over coding the state directly. Applying the same target-conditioned construction to sequential prediction reveals a dynamic boundary. A state induced by a fixed horizon is minimal for that horizon but may not support recursive updating as the target window shifts. Taking the entire future as the target yields a minimal full-future state that updates recursively and admits a Markov representation. Together, these results shift AI communication from source reconstruction to predictive-state preservation.
