Current approaches to memory in neural systems rely on similarity-based retrieval: given a query, find the most representationally similar stored state. This assumption -- that useful memories are similar memories -- fails to capture a fundamental property of biological memory: association through temporal co-occurrence. We propose Predictive Associative Memory (PAM), an architecture in which a JEPA-style predictor, trained on temporal co-occurrence within a continuous experience stream, learns to navigate the associative structure of an embedding space. We introduce an Inward JEPA that operates over stored experience (predicting associatively reachable past states) as the complement to the standard Outward JEPA that operates over incoming sensory data (predicting future states). We evaluate PAM as an associative recall system -- testing faithfulness of recall for experienced associations -- rather than as a retrieval system evaluated on generalisation to unseen associations. On a synthetic benchmark, the predictor's top retrieval is a true temporal associate 97% of the time (Association Precision@1 = 0.970); it achieves cross-boundary Recall@20 = 0.421 where cosine similarity scores zero; and it separates experienced-together from never-experienced-together states with a discrimination AUC of 0.916 (cosine: 0.789). Even restricted to cross-room pairs where embedding similarity is uninformative, the predictor achieves AUC = 0.849 (cosine: 0.503, chance). A temporal shuffle control confirms the signal is genuine temporal co-occurrence structure, not embedding geometry: shuffling collapses cross-boundary recall by 90%, replicated across training seeds. All results are stable across seeds (SD < 0.006) and query selections (SD $\leq$ 0.012).

翻译：当前神经系统中记忆方法依赖于基于相似性的检索：给定一个查询，寻找表征最相似的存储状态。这一假设——即有用的记忆是相似的记忆——未能捕捉到生物记忆的一个基本特性：通过时间共现形成的联想。我们提出了预测性联想记忆（Predictive Associative Memory, PAM），该架构采用一个JEPA风格的预测器，通过在连续经验流上训练时间共现关系，学习在嵌入空间的联想结构中导航。我们引入了一种向内JEPA，其操作基于存储的经验（预测可通过联想到达的过去状态），作为标准向外JEPA（操作基于传入的感官数据，预测未来状态）的补充。我们将PAM评估为一个联想回忆系统——测试其对已经验证关联的回忆忠实度——而非一个评估对未见关联泛化能力的检索系统。在合成基准测试中，预测器的Top-1检索结果在97%的情况下是真实的时间关联体（关联精确率@1 = 0.970）；它在余弦相似度得分为零的情况下实现了跨边界召回率@20 = 0.421；并且它以0.916的区分AUC（余弦相似度：0.789）将共同经验过的状态与从未共同经验过的状态区分开来。即使仅限于嵌入相似性无信息量的跨房间配对，预测器仍能达到AUC = 0.849（余弦相似度：0.503，随机水平）。时间随机重排对照实验证实该信号源自真实的时间共现结构，而非嵌入几何：重排使跨边界召回率下降90%，该结果在不同训练种子下可复现。所有结果在不同种子（标准差 < 0.006）和查询选择（标准差 $\leq$ 0.012）下均保持稳定。