Predictive Memory Systems
As AI models learn continuously, they face catastrophic forgetting.
Replay buffers treat frequency as importance, causing rare but safety-critical events to disappear under fixed GPU memory budgets.
Continual learning exists. Memory governance does not.
MemorySafe transforms passive memory storage into a predictive decision system.
Predict which memories are likely to be forgotten.
Allocate memory based on future relevance.
Automate protect / replay / forget decisions.
Predicts forgetting risk before performance degrades.
Estimates memory value independently from exposure frequency.
Decision layer that governs retention policies.
Demo available: Colab benchmark (Taste Demo v2)
View BenchmarksHigher accuracy indicates better retention.
medical imaging • anomaly detection • activity recognition • vision rare-class retention
MemorySafe consistently protects rare-event performance.
Dramatically smaller storage requirements while maintaining high performance.
Maximum efficiency in GPU memory utilization.
Enables continual learning on:
MemorySafe is optimized for GPU-accelerated continual learning workflows.
Memory governance becomes critical as models move from static training to on-device continual learning.
MemorySafe Labs builds the intelligence layer that helps AI remember what matters.
Predictive memory systems for the next generation of AI.