From Experience to Knowledge: Recursive Predictive Compression for Agent Memory

With limited resources, agents must turn experience into reusable knowledge while retaining key details. We introduce Recursive Predictive Compression (RPC), which unifies experience internalization and generative recall through one recursive predictive representation. RPC repeatedly decomposes an experience's contribution to prediction into shared structure and local residuals using the same rule across scales. Measured predictive transfer proposes candidate scopes; later task and recall outcomes select the depth and scope of reuse and the details to retain. Compatible contributions enter a bounded world state, allowing separate records to be released while their predictive influence survives. Recall draws on internalized world knowledge, supplemented by retained key details. RPC thus connects compression, transfer, and internalization while giving each operation a distinct role. Under a fixed linear predictive interface and the stated compatibility conditions, decomposition is exact before lossy release, and absorption preserves the full predictive distribution. Evaluations on controlled decisions, hidden-rule inference, long-horizon agent memory, and visual prediction show gains in prediction and reconstruction, alongside improvements in downstream performance.