Memory Governance for Continual Learning Systems
MemorySafe is a memory governance framework for continual learning systems, designed to prevent safety-critical information from being forgotten under memory and compute constraints.
Rather than modifying how models learn, MemorySafe governs what they retain.
It operates as a policy-level decision layer for memory, acting as an intelligent governor that disciplines data retention independently of the learning algorithm.
Use cases include medical AI, edge systems, fraud detection, robotics, and privacy-aware AI.
Experience predictive memory governance in action.
The demo compares MemorySafe against FIFO, Reservoir, and Random Replay under memory pressure.
No setup required. Just click Run all.
Modern continual learning systems implicitly conflate exposure with importance.
Under standard replay strategies:
- frequent samples dominate memory
- rare but critical events are overwritten
- long-term reliability degrades
This is especially harmful in:
- medical AI
- edge systems
- fraud detection
- safety-critical robotics
MemorySafe reframes memory as a resource allocation and lifecycle management problem, explicitly separating:
Risk ≠ Value ≠ Decision
MVI estimates how likely a memory is to be forgotten under future learning pressure.
It captures:
- interference from new tasks
- sensitivity to gradient updates
- temporal competition effects
MVI is:
- predictive (not retrospective)
- continuous and interpretable
- model-agnostic
Relevance estimates how valuable a memory is independently of vulnerability.
Guiding principles:
- repetition ≠ importance
- rare but salient events retain value
- relevance decays over time
ProtectScore combines MVI and Relevance into a deterministic decision signal that governs:
- protection
- consolidation
- eviction under fixed capacity
This treats forgetting as an active functional choice, not a system failure.
MemorySafe is:
- a memory governance layer
- model-agnostic and pluggable
- compatible with any learning algorithm
- interpretable and low-overhead
MemorySafe is not:
- a training algorithm
- a benchmark-optimized model
- a replacement for continual learning methods
It governs memory decisions, not learning dynamics.
MemorySafe acts as a policy layer on top of replay buffers or memory modules.
Each memory maintains:
- task_id
- MVI
- relevance
- protect_score
- replay_count
- protected flag
Design guarantees:
- no gradients inside memory logic
- no dataset-specific heuristics
- deterministic and auditable behavior
The same unchanged MemorySafe policy was evaluated across heterogeneous continual learning benchmarks:
- MNIST
- Fashion-MNIST
- CIFAR-10
- CIFAR-100
- Omniglot
- Permuted MNIST
- PneumoniaMNIST (medical imaging)
Without dataset-specific tuning, MemorySafe demonstrated:
- consistent Task-0 protection
- strong rare-event retention
- stability across increasing task difficulty
These results suggest zero-shot generalization of a memory allocation policy.
Medical AI
Protection of rare pathologies and safety-critical cases.
Edge AI (Jetson / embedded)
Memory governance under tight RAM and compute budgets.
Fraud Detection
Retention of delayed or rare anomalies.
Robotics
Preservation of critical failures and safety events.
Privacy-Aware AI
Predictive forgetting and sensitive data governance (MVI-P).
MemorySafe can integrate with:
- Experience Replay
- GEM / A-GEM
- PackNet
- Progressive Neural Networks
- Custom replay buffers
Integration modes:
- replay gate
- eviction governor
- diagnostic layer for memory risk
- PyTorch
- CUDA / GPU acceleration
- Continual learning research prototype (Alpha)
MemorySafe treats AI memory as a governed resource, separating vulnerability from value to enable intentional, interpretable, and generalizable memory decisions under real-world constraints.
Apache 2.0 License