MemorySafe
Predictive Memory Systems
MemorySafe Labs

Predictive Memory Systems for
Continual Learning AI

A decision layer that helps continual-learning models choose what to protect, what to replay, and what to forget when memory is limited.

Rare-case retention
GPU-constrained
Model-agnostic
Discover Solution View Colab Demo
Carla Centeno — Founder • Montreal, Canada • February 2026

Continual learning is here.

Memory governance is not.

As models learn continuously, they can overwrite rare, high-impact knowledge. Most replay buffers treat frequency as importance, which fails in long-tail, safety-critical settings.

Without governance, memory becomes an unmanaged system resource.

Predictive Memory Governance

MemorySafe turns replay from a passive buffer into an active decision system using the Memory Vulnerability Index (MVI).

Forecast Risk

Predict which memories are likely to be forgotten before performance degrades.

Allocate Intentionally

Use a future-aware signal, not exposure frequency, to guide what stays.

Govern Actions

Automate protect / replay / forget through a model-agnostic layer.

The MemorySafe Decision Layer

Memory Vulnerability Index (MVI)

Predicts forgetting risk before performance degrades.

Relevance Signal

Estimates memory value independently from exposure frequency.

ProtectScore

Policy layer that governs protect / replay / forget decisions.

Input Buffer Raw Data Stream
Decision Layer MVI + ProtectScore
Protect
Replay
Forget

Designed for GPU-constrained continual learning where retention choices must be explicit, measurable, and reproducible.

See MemorySafe in Action

Watch how the Memory Vulnerability Index (MVI) protects rare-case data in real-time compared to standard replay buffers.

Validation & Results

10-seed experiments confirm reproducible gains and tighter confidence intervals.

PneumoniaMNIST

10-Seed Validation p < 0.001

0.941 Mean AUPRC ± 0.007
64.1% Minority Recall ± 4.2%
84% Memory Reduction
100% Task-0 Retention
Baseline Recall 41.2%
Recall @1% FPR 0.652
Memory Usage 23.4MB → 3.75MB
Buffer Allocation 53.1%
Stable rare-case performance with an 84% smaller replay memory footprint across 10 independent runs.

CIFAR-100

Class Incremental • 10-Seed (Current)

54.2% Final Accuracy
17.9% Forgetting ↓ better
+9.3% vs DER++ (minority)
10 Seeds Completed
Accuracy (5 → 10) 53.7% → 54.2%
Forgetting (5 → 10) 18.6% → 17.9%
Outliers None
Confidence Tightened
Reproducible improvements in accuracy, forgetting, and minority performance under continual learning.
All results statistically significant (p < 0.01), with p < 0.001 for AUPRC improvement
83
Replay-Buffer Memory Reduction

Dramatically smaller storage requirements while maintaining rare-case performance.

99
Feature-Storage Reduction

Efficient memory usage for GPU-constrained deployment settings.

Built for:

Edge Devices Robotics Systems Medical Hardware GPU-Constrained
NVIDIA Inception Program Member

Optimized for GPU Workflows

MemorySafe is designed for GPU-accelerated continual learning. As models move from static training to on-device updates, memory governance becomes the reliability bottleneck.

Jetson edge AI• Robotics• Medical AI systems• Industrial monitoring
NVIDIA Inception Member Badge

Memory governance for continual AI.

MemorySafe Labs builds the intelligence layer that helps models remember what matters.

Predictive memory systems for the next generation of AI.

Architecture Preview

Beyond Replay

MemorySafe is evolving beyond replay-based retention into a broader architecture for predictive memory governance. The goal is not only to preserve rare knowledge, but to regulate how AI systems remember, adapt, and learn over time.

  • SENSING Detecting memory vulnerability and drift
  • GOVERNANCE Assigning value to knowledge through protection policies
  • PLASTICITY Managing neural adaptation under continual learning
Predictive Memory Governance · MemorySafe Labs · 2026