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Memory Vulnerability Index (MVI)

Predictive Forgetting Risk Scoring for Intelligent Replay Buffer Management in Continual Learning

MemorySafe Labs
Carla Centeno, Founder
February 2026
https://memorysafe.ca | NVIDIA Inception Program Member

License & IP Notice

This document describes the conceptual foundation of the Memory Vulnerability Index (MVI), a core component of the MemorySafe system.
All rights reserved. This is proprietary technology developed by MemorySafe Labs. No part of this method may be reproduced, adapted, or used commercially without explicit written permission. For collaboration, licensing, or inquiries: carla@memorysafe.ca.

1. The Problem: Catastrophic Forgetting in Continual Learning

In continual (lifelong) learning, models must learn from non-stationary data streams without access to the full past dataset. Replay buffers help mitigate catastrophic forgetting by storing and replaying a small subset of previous experiences.

Standard replay methods (reservoir sampling, FIFO, uncertainty/hard-example mining) treat buffer samples mostly equally or use simple heuristics. This leads to:

  • Disproportionate forgetting of rare or safety-critical experiences (e.g., anomalies, minority classes in medical imaging, unusual obstacles in robotics).
  • Inefficient buffer usage under fixed memory constraints (especially on edge devices like Jetson).
  • No proactive prediction of which samples are at highest risk of being forgotten before degradation occurs.

2. Memory Vulnerability Index (MVI) — Core Idea

MVI is a predictive, per-sample forgetting risk score (scalar ∈ [0, 1]) that quantifies how vulnerable a stored experience is to catastrophic forgetting if not replayed or protected soon.

Instead of reactive or uniform buffer management, MVI enables proactive, risk-aware governance:

  • High-MVI samples → prioritize for protection / forced replay / harder eviction.
  • Low-MVI samples → safe candidates for eviction when buffer is full.

This turns the replay buffer into an intelligent, predictive cache rather than a dumb FIFO or random sampler.

3. High-Level Signals Combined into MVI

MVI aggregates multiple complementary signals, each grounded in established continual learning research on forgetting mechanisms:

  • Forgetting Velocity
    Rate of performance degradation on this sample since last replay (e.g., change in prediction confidence, loss increase, or gradient magnitude during recent updates).

  • Feature / Representation Drift
    Shift between the sample's current embedding and the model's evolving feature space (e.g., distance in latent space).

  • Rarity & Imbalance Signal
    Inverse frequency of the sample's class/pseudo-class in recent stream + buffer (boosts minority / rare events).

  • Temporal / Task Age Decay
    How long ago the sample was collected (exponential decay — older experiences are generally more at risk).

These signals are normalized and combined into a single MVI score.
Exact combination weights, normalization functions, and update frequency are proprietary tuning parameters of MemorySafe.

4. How MVI is Used in MemorySafe

During training (after each task / minibatch / periodically):

  1. Compute MVI for all buffer samples.
  2. Apply ProtectScore policy → high-MVI samples are protected (e.g., eviction priority lowered, forced inclusion in replay batches).
  3. When buffer is full → evict lowest-MVI samples first.

Result: Intelligent rarity enrichment and protection of vulnerable experiences without over-protecting majority classes.

5. Empirical Validation (High-Level Results)

Evaluated on standard continual learning benchmarks (multi-seed averages):

  • PneumoniaMNIST (imbalanced binary medical classification)
    → Achieved perfect rare-class recall (1.000) across seeds.
    → Buffer positive fraction enriched to ~0.11–0.12 (vs. ~2% in stream).
    → Specificity remained strong (0.004–0.012 in best configs) — no majority collapse.

  • CIFAR-100 Class-Incremental (5 tasks × 20 classes, ResNet-18)
    → Final incremental accuracy: ~53.17% (10-seed avg).
    → +8.2% relative over standard reservoir replay.
    → ~18% reduction in forgetting vs. plain replay.
    → Low variance across seeds.

  • Memory efficiency gains demonstrated via downsampling + feature replay → 81–99% realistic savings.

These results show MVI enables strong rare-event protection while preserving overall performance.

6. Applications & Why It Matters

MVI is particularly valuable for safety-critical, edge-constrained domains:

  • Medical AI (rare pathology detection on ultrasound/X-ray devices)
  • Robotics (anomaly / failure / obstacle handling in dynamic environments)
  • Industrial monitoring (rare fault detection)
  • Any low-memory continual system where forgetting rare events is unacceptable.

7. Limitations & Future Directions

  • Current validation focused on image-based continual learning benchmarks.
  • Transfer to sequential/time-series robotics data (proprioception, video, actions) requires domain-specific adaptations (e.g., trajectory-level rarity, state-action signals).
  • Ongoing work: hybrid combinations (MVI + distillation), on-device implementations (Jetson), real robotics pilots.

For more information, licensing discussions, or collaboration:
Carla Centeno — carla@memorysafe.ca
MemorySafe Labs — https://memorysafe.ca

© 2026 MemorySafe Labs. All rights reserved.