Long-term memory in Large Language Model (LLM) agents requires selective persistence: only a subset of interactions should be consolidated beyond the current context window. Existing memory systems rely on heuristic importance rules or similarity-based novelty, which remain external to the model’s internal computation. We propose a geometric principle for memory formation: episodic memory can emerge from compression failure in latent representation space. We approximate the manifold of routine LLM activations using Sparse Autoencoders (SAEs) and define representational surprise as reconstruction error relative to this learned manifold. Deviations from routine structure yield elevated residuals, providing an unsupervised, model-internal signal for memory writing. We demonstrate this principle through ReSuME, a surprise-gated memory mechanism that commits turns to memory only when normalized reconstruction error exceeds a calibrated threshold. In long-horizon multi-turn dialogue settings, representational surprise separates routine, critical, and out-of-distribution states, and achieves a superior performance–memory trade-off against established memory-agent systems (MemGPT, EM-LLM) and unsupervised anomaly detectors, while operating without labels and at substantially lower inference cost than LLM-judge controllers. Covariance-aware normalization further enables robust cross-domain calibration. These results suggest that episodic memory gating in neural agents can be grounded in intrinsic latent geometry rather than externally engineered rules.