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Continual Learning with Regulatory Checkpoints: Adapting to New Requirements While Maintaining Validated Performance

Literature Review

Author: Matthew Martz Date: November 24, 2025 Status: Comprehensive Survey for Paper 3

Table of Contents

  1. Introduction
  2. Theoretical Foundations
  3. Catastrophic Forgetting in LLMs
  4. Continual Learning Paradigms
  5. Forgetting Prevention Methods
  6. Regulatory Requirements for AI/ML Systems
  7. Validated Performance Maintenance
  8. Connection to ADAPT-Q
  9. Bibliography

1. Introduction

In regulated industries—healthcare, finance, aviation, pharmaceuticals—AI/ML systems must maintain validated performance while adapting to evolving requirements. New medical guidelines emerge, financial regulations change, safety protocols update. Models must learn new knowledge without forgetting validated behaviors that enabled regulatory approval.

This creates a continual learning challenge with regulatory constraints: how to incrementally adapt models to new tasks, domains, or regulations while provably maintaining performance on previous validated requirements. Unlike traditional continual learning focused solely on avoiding catastrophic forgetting, regulatory continual learning requires:

  1. Validation preservation: Performance on validation test suites must not degrade
  2. Audit trails: All adaptations logged and traceable
  3. Rollback capability: Ability to revert to last validated state
  4. Incremental validation: New adaptations pass validation before deployment
  5. Compliance maintenance: Continued satisfaction of regulatory requirements

1.1 Regulatory Checkpoints Defined

A regulatory checkpoint is a validated model state that has passed: - Performance validation: Meets accuracy/safety thresholds on test suite - Safety validation: Passes adversarial robustness, fairness, privacy tests - Compliance validation: Satisfies domain-specific regulations (FDA, FinCEN, etc.) - Documentation: Complete audit trail and technical documentation

Challenge: When adapting to new requirements (new regulations, new tasks), how do we ensure the model still passes all previous checkpoints?

1.2 Key Research Questions

  • What continual learning methods best preserve validated performance?
  • How can we provide guarantees that checkpoints remain satisfied after adaptation?
  • What architectural designs support checkpoint preservation?
  • How can ADAPT-Q's forgetting protection enable regulatory continual learning?
  • What validation frameworks are needed for continual learning in regulated domains?

2. Theoretical Foundations

2.1 Continual Learning Problem Formulation

Classical formulation: Given sequence of tasks \(T_1, T_2, ..., T_n\), learn model that performs well on all tasks:

\[ \min_\theta \sum_{i=1}^{n} \mathcal{L}_i(\theta; \mathcal{D}_i) \]

subject to: - Bounded capacity: \(|\theta| \leq C\) - Sequential access: At time \(t\), only \(\mathcal{D}_t\) available

Regulatory formulation (extended):

\[ \min_\theta \sum_{i=1}^{n} \mathcal{L}_i(\theta; \mathcal{D}_i) \]

subject to: - Checkpoint constraints: \(\text{Performance}(\theta, \mathcal{V}_j) \geq \tau_j\) for all previous validation sets \(\mathcal{V}_j\), \(j < i\) - Safety constraints: \(\text{Safety}(\theta, \mathcal{A}_j) \geq \sigma_j\) for all adversarial test sets \(\mathcal{A}_j\) - Compliance constraints: \(\theta\) satisfies regulatory requirements \(\mathcal{R}_j\) - Audit trail: All parameter changes \(\Delta\theta\) logged

2.2 Catastrophic Forgetting

Definition: Catastrophic forgetting [McCloskey & Cohen, 1989; French, 1999] is the phenomenon where neural networks rapidly forget previously learned information when learning new information.

Mathematical characterization: After learning task \(T_2\), performance on task \(T_1\) degrades:

\[ \text{Performance}_{T_1}(\theta_2) \ll \text{Performance}_{T_1}(\theta_1) \]

where \(\theta_1\) is parameters after learning \(T_1\), \(\theta_2\) is parameters after sequentially learning \(T_1\) then \(T_2\).

Quantifying forgetting [Chaudhry et al., 2018]: $$ \text{Forgetting}i = \max_i \right) $$} \left( \text{Acc}_i^j - \text{Acc

where \(\text{Acc}_i^j\) is accuracy on task \(j\) after learning task \(i\).

Backward transfer: $$ \text{BWT} = \frac{1}{n-1} \sum_{i=1}^{n-1} \text{Acc}_n^i - \text{Acc}_i^i $$

Negative BWT indicates catastrophic forgetting.

2.3 Plasticity-Stability Trade-Off

Fundamental dilemma [Abraham & Robins, 2005]: - Plasticity: Ability to learn new information quickly - Stability: Ability to retain old information reliably

High plasticity → rapid learning but catastrophic forgetting High stability → robust retention but poor new learning

Goal: Optimal balance achieving both plasticity (learn new regulations) and stability (maintain validated checkpoints).

2.4 Continual Learning Scenarios

Task-incremental learning (TIL): - Task identity provided at test time - Model knows which task being evaluated - Easier setting, task-specific heads possible

Domain-incremental learning (DIL): - Tasks share output space, differ in input distribution - No task identity at test - Example: Medical model adapts to new hospital's patient distribution

Class-incremental learning (CIL): - New classes added incrementally - Must classify across all seen classes - Hardest setting, requires maintaining decision boundaries for all classes

Regulatory scenario mapping: - New regulation → TIL: New compliance requirement is distinct task - New data distribution → DIL: Same requirements, different patient/customer population - New categories → CIL: New disease codes, new fraud patterns

3. Catastrophic Forgetting in LLMs

3.1 Scale and Scope

Comprehensive survey [Wang et al., 2024; Van de Ven et al., 2024]: Catastrophic forgetting occurs across all neural architectures, including transformers and large language models.

Definition for LLMs: When LLM is fine-tuned on new data \(\mathcal{D}_{\text{new}}\), it rapidly forgets capabilities learned during pre-training or previous fine-tuning.

Example: - Pre-trained LLM: 85% accuracy on medical QA - Fine-tune on legal documents - Result: 42% accuracy on medical QA (50% relative degradation)

3.2 Forgetting in Domain Adaptation

ADAPT-Q evidence [Martz, 2025]: LoRA exhibits catastrophic forgetting with critical threshold at 100-150 samples: - 50 samples: +0.9% perplexity degradation - 500 samples: +163% degradation - 5,000 samples: +3,671% degradation - 50,000 samples: +17,768% degradation

Scale-dependency: Forgetting worsens exponentially with more adaptation data—precisely when models need to learn more.

Medical domain [Yang et al., 2024]: LoRA fine-tuning for clinical specialization causes 94% degradation on general medical knowledge.

3.3 Forgetting vs. Regulation Violation

Critical distinction:

Forgetting: Loss of general capability - Model forgets how to perform task A after learning task B - Measured by performance degradation on task A

Regulation violation: Failure to satisfy specific requirements - Model outputs violate compliance rules after adaptation - Measured by compliance test suite pass/fail

Example (medical): - Forgetting: Model forgets rare disease diagnoses after specializing in cardiology - Regulation violation: Model recommends drug dosages outside FDA-approved ranges

Both are unacceptable in regulated settings.

3.4 Recent LLM Continual Learning Research

Comprehensive survey [Wang et al., 2024; GitHub survey, 2025]: Major advances in continual learning for LLMs documented in ACM Computing Surveys 2025.

Key findings: 1. Prompting-based methods reduce forgetting but limit capacity 2. Replay methods maintain performance but require storing previous data 3. Regularization methods partially mitigate forgetting without full preservation 4. Architecture methods (modularity, adapters) show promise for continual learning

NeurIPS 2024 papers: - "WISE: Rethinking the Knowledge Memory for Lifelong Model Editing" [Li et al., 2024] - "Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning" [Chen et al., 2024] - "D-CPT Law: Domain-specific Continual Pre-Training Scaling Law" [Zhang et al., 2024]

4. Continual Learning Paradigms

4.1 Regularization-Based Methods

Core idea: Add regularization term penalizing changes to important parameters.

Elastic Weight Consolidation (EWC) [Kirkpatrick et al., 2017]: $$ \mathcal{L}(\theta) = \mathcal{L}B(\theta) + \frac{\lambda}{2} \sum_i F_i (\theta_i - \theta*)2 $$

where \(F_i\) is Fisher information (parameter importance), \(\theta_A^*\) is optimized parameters from task A.

Intuition: Important parameters (high Fisher) penalized heavily for changes, unimportant parameters free to adapt.

Synaptic Intelligence (SI) [Zenke et al., 2017]: Tracks parameter importance based on contribution to loss during training: $$ \omega_i = \sum_t g_i^{(t)} \delta_i^{(t)} $$

where \(g_i\) is gradient, \(\delta_i\) is parameter change.

Application to LLMs: - Compute Fisher information on validation data - Apply EWC during fine-tuning on new task/regulation - Challenge: Scales poorly to billion-parameter models (storing Fisher matrix)

Results: - Reduces forgetting: Backward transfer improves from -15% → -5% - Trade-off: Constrains adaptation (plasticity reduced)

4.2 Replay-Based Methods

Core idea: Interleave new task training with replays of previous task data.

Experience Replay [Rolnick et al., 2019]: - Store subset of previous task data in memory buffer - During training on new task, sample batches from both new data and buffer - Joint optimization: \(\mathcal{L} = \mathcal{L}_{\text{new}} + \mathcal{L}_{\text{replay}}\)

Gradient Episodic Memory (GEM) [Lopez-Paz & Ranzato, 2017]: - Store previous task examples - Constrain new task gradient to not increase loss on previous tasks: $$ g^T g_k \geq 0 \quad \forall k < \text{current task} $$ - If constraint violated, project gradient to feasible region

Dark Experience Replay (DER) [Buzzega et al., 2020]: - Store both examples and model outputs from previous tasks - Use distillation loss to match previous outputs - Prevents forgetting even if data distribution shifts

Regulatory implications: - Advantage: Provable preservation (replaying validation data ensures checkpoints maintained) - Challenge: Storing sensitive data (HIPAA, GDPR compliance issues) - Solution: Synthetic replay using generative models [Shin et al., 2017]

4.3 Parameter Isolation Methods

Core idea: Allocate separate parameters for each task, preventing interference.

Progressive Neural Networks [Rusu et al., 2016]: - Add new columns (subnetworks) for each new task - Previous columns frozen, new columns connect to previous via lateral connections - Advantage: Zero forgetting (previous tasks unmodified) - Limitation: Linear parameter growth with tasks

PackNet [Mallya & Lazebnik, 2018]: - Learn binary masks allocating network capacity to tasks - Each task uses subset of parameters - Iterative pruning to free capacity

Adapter-based methods [Houlsby et al., 2019]: - Insert small adapter modules for each task - Base model frozen, adapters trained - Advantage: Parameter-efficient (only adapters grow) - Limitation: Adapter capacity may be insufficient

Task-Incremental LoRA [Wang et al., 2024]: - Apply separate LoRA module for each new task/regulation - Freeze previous LoRAs, train new LoRA - At inference, select or combine LoRAs based on task - Challenge: LoRA still exhibits forgetting (see ADAPT-Q evidence)

4.4 Prompt-Based Continual Learning

Learning to Prompt (L2P) [Wang et al., 2022]: - Maintain pool of learnable prompts - Select relevant prompts for each input via similarity matching - Only prompts updated, base model frozen

Method: 1. Prompt pool: \(\{p_1, p_2, ..., p_M\}\) 2. Query matching: For input \(x\), compute similarity to each prompt 3. Top-k selection: Select \(k\) most relevant prompts 4. Prepend to input: \([p_{i_1}, p_{i_2}, ..., p_{i_k}, x]\) 5. Optimize prompts: Update selected prompts via gradient descent

Results (class-incremental): - 91.7% average accuracy over 10 tasks - Minimal forgetting (backward transfer -2.3%) - Advantage for regulation: Can add new prompts for new requirements without touching base model

DualPrompt [Wang et al., 2023]: - Separate general prompts (shared across tasks) and expert prompts (task-specific) - Dynamically select based on input - Better plasticity-stability balance

4.5 Meta-Learning for Continual Learning

Meta-Continual Learning: Learn learning algorithm optimized for continual learning setting.

Meta-Experience Replay (MER) [Riemer et al., 2019]: - Meta-objective: Minimize forgetting after experiencing replay data - MAML-style meta-learning: Simulate continual learning during meta-training - Results: 15% improvement over standard replay

Train-Attention [Chen et al., 2024; NeurIPS 2024]: - Meta-learn where to focus attention during continual learning - Automatically identifies which model components to preserve vs. adapt - Reduces forgetting while maintaining plasticity

5. Forgetting Prevention Methods

5.1 Knowledge Distillation

Learning Without Forgetting (LwF) [Li & Hoiem, 2017]: - No access to previous task data - Use knowledge distillation to preserve previous task outputs

Method: $$ \mathcal{L} = \mathcal{L}{\text{new}}(y, f\theta(x)) + \lambda \mathcal{L}{\text{KD}}(f(x), f_\theta(x)) $$}}

where \(\mathcal{L}_{\text{KD}}\) is KL divergence between old and new model outputs.

Regulatory application: - Store outputs on validation data from previous checkpoint - When adapting to new requirement, distill validation outputs - Ensures validation performance maintained

Results: - Reduces forgetting by 40-60% compared to naive fine-tuning - No data storage required (only old model outputs)

5.2 Dynamic Architectures

Dynamically Expandable Networks (DEN) [Yoon et al., 2018]: - Add neurons/layers when existing capacity insufficient - Prune redundant parameters - Grow/shrink architecture based on task requirements

Continual Pre-Training (CPT) [Fan et al., 2024]: - Initialize new model from previous checkpoint - Enables architectural changes while retaining knowledge - Key for regulation: Can update model architecture while preserving validation

TS-ACL (Time Series Analytical Continual Learning) [Fan et al., 2024]: - Gradient-free recursive regression learning - Specifically designed for time series (financial data, patient monitoring) - Mitigates catastrophic forgetting without explicit replay

5.3 Sparse Activation and Routing

Mixture of Experts for Continual Learning: - Allocate different experts to different tasks/regulations - Route inputs to appropriate experts - Advantage: Natural parameter isolation

Continual Learning via Expert Gating [Aljundi et al., 2017]: - Train gating network to route to appropriate expert - Add new expert for each new task - Results: Zero forgetting (experts don't interfere)

Hierarchical routing: - First level: Select task family (medical, financial, legal) - Second level: Select specific requirement (HIPAA, FDA, FinCEN) - Enables fine-grained control over which knowledge activated

5.4 Compression and Pruning

Piggyback [Mallya et al., 2018]: - Learn task-specific binary masks over shared backbone - Extremely parameter-efficient (1 bit per parameter per task)

Supermasks [Wortsman et al., 2020]: - Lottery ticket hypothesis: Winning tickets exist for each task - Find masks that perform well without weight training - Implication: Single base model can serve multiple tasks via masking

Relevance to ADAPT-Q: ADAPT-Q's quantization + selective adaptation similar to compression-based continual learning: - Quantized frozen layers: Compressed preservation of previous knowledge - Adapted layers: Plastic capacity for new requirements

6. Regulatory Requirements for AI/ML Systems

6.1 FDA Guidance for Medical Devices

FDA AI/ML SaMD Action Plan [FDA, 2021]: - Software as a Medical Device (SaMD) using AI/ML must ensure safety and effectiveness - Algorithm Change Protocol (ACP): Required for models that update/adapt

Key requirements: 1. Pre-market validation: Initial model must pass clinical validation 2. Post-market monitoring: Continuous performance monitoring 3. Change control: All algorithm changes documented and validated 4. Re-validation triggers: Significant changes require new clinical validation

Continual learning implications: - Each adaptation is an algorithm change - Must demonstrate adaptation doesn't degrade validated performance - Need audit trail showing all parameter changes

FDA's approach to continual learning: - Allows "locked" algorithms (no updates) or - Predetermined Change Control Plan: Pre-approved adaptation methodology - Opportunity: ADAPT-Q with checkpoint preservation could be pre-approved methodology

6.2 Financial Regulations

Model Risk Management [Federal Reserve SR 11-7, 2011]: - Banks must validate models used for capital allocation, risk management - Ongoing monitoring: Models must be continuously monitored for performance degradation - Revalidation: Trigger revalidation if material changes or performance degrades

FinCEN AML Requirements: - Anti-money laundering (AML) models must detect suspicious activities - Auditability: Must explain why transaction flagged/not flagged - Adaptation constraint: Model updates cannot reduce detection capability

Basel III: Capital requirements based on risk models - Models must be validated by regulators - Changes require re-approval - Incentive against adaptation: Regulatory burden makes frequent updates expensive

Continual learning opportunity: - Enable rapid adaptation to new fraud patterns - Provable checkpoint preservation for regulatory compliance - Automated validation against compliance test suite

6.3 GDPR and Privacy Regulations

Right to be forgotten: - Individuals can request data deletion - Model must "unlearn" individual's data

Machine unlearning [Bourtoule et al., 2020]: - Remove influence of specific training examples - Continual learning in reverse (forget specific data, preserve rest) - ADAPT-Q relevance: Selective neuron targeting could enable targeted unlearning

Data minimization: - Only necessary data should be processed - Replay challenge: Storing previous task data may violate GDPR - Solution: Synthetic replay, prompt-based methods (no data storage)

6.4 Aviation and Safety-Critical Systems

DO-178C (Software in Airborne Systems): - Software must be verified and validated - Changes require re-verification at appropriate level - Adaptation challenge: Model updates are software changes

ISO 26262 (Automotive functional safety): - Safety-critical functions must meet ASIL requirements - AI/ML components face additional scrutiny - Continual learning: Must prove adaptation doesn't violate safety requirements

6.5 Common Regulatory Themes

Across all regulated domains: 1. Validation before deployment: Models must pass validation before use 2. Performance monitoring: Continuous monitoring required 3. Change control: All changes documented and justified 4. Revalidation triggers: Material changes trigger re-validation 5. Audit trail: Complete history of model development and changes

Continual learning requirements: - Checkpoint preservation: Adapted model must still pass previous validations - Incremental validation: New capabilities validated before deployment - Rollback capability: Can revert to last validated checkpoint - Audit logging: All adaptations traceable

7. Validated Performance Maintenance

7.1 Validation Test Suites

Regulatory validation test suite: - Collection of test cases model must pass for regulatory approval - Includes: - Performance tests: Accuracy, precision, recall on benchmark data - Safety tests: Adversarial robustness, fairness, privacy - Compliance tests: Domain-specific requirements (drug interactions, financial rules)

Example (medical device): - Diagnostic accuracy: ≥95% sensitivity on rare disease benchmark - Fairness: <5% performance gap across demographic groups - Safety: No false negatives on life-threatening conditions - Drug safety: 100% of recommendations respect interaction rules

Checkpoint = passing all validation tests

7.2 Checkpoint Preservation Strategies

Strategy 1: Frozen parameter validation - Identify parameters critical for validation performance - Freeze those parameters during adaptation - ADAPT-Q alignment: Neuron-level freezing

Strategy 2: Validation-aware regularization - During adaptation, add regularization term: $$ \mathcal{L} = \mathcal{L}{\text{new}} + \lambda \sum \ell(f_\theta(x_v), y_v) $$ where }\(\mathcal{V}\) is validation set from previous checkpoints

Strategy 3: Dual-model approach - Maintain validated model \(\theta_{\text{val}}\) (frozen) - Train adapted model \(\theta_{\text{new}}\) - Before deployment, verify \(\text{Performance}(\theta_{\text{new}}, \mathcal{V}) \geq \text{Performance}(\theta_{\text{val}}, \mathcal{V})\) - Deploy only if validation maintained

Strategy 4: Incremental validation - After each adaptation step, run validation suite - If any test fails, rollback to previous checkpoint - Advantage: Early detection of degradation

7.3 Continuous Validation Frameworks

MLOps pipeline with validation gates:

1. Adaptation request (new regulation, new data)
2. Create adaptation branch from last validated checkpoint
3. Apply adaptation (fine-tuning, PEFT)
4. Run validation suite on adapted model
5. If validation passes:
   a. Log adaptation in audit trail
   b. Create new checkpoint
   c. Deploy to production
6. If validation fails:
   a. Reject adaptation
   b. Alert developers
   c. Remain on previous checkpoint

Automated testing: - Validation suite runs automatically after each adaptation - No human intervention required - Advantage: Rapid iteration while maintaining compliance

Shadow deployment: - Run adapted model in parallel with validated model - Compare outputs, monitor for degradation - Deploy only after confidence established

7.4 Rollback and Recovery

Checkpoint management: - Store all validated checkpoints - Maintain complete history of adaptations - Enable rollback to any previous validated state

Trigger-based rollback: - Performance degradation: If monitoring detects accuracy drop, auto-rollback - Safety violation: If adversarial example found, rollback - Compliance failure: If audit finds violation, rollback to last compliant checkpoint

Recovery procedures: - Root cause analysis: Why did adaptation degrade performance? - Targeted fixing: Address specific issue (e.g., re-freeze forgotten parameters) - Re-validation: Validate fix before redeployment

8. Connection to ADAPT-Q

8.1 ADAPT-Q's Forgetting Protection

Core advantage: ADAPT-Q already addresses catastrophic forgetting [Martz, 2025]: - 34-967× better general knowledge preservation than LoRA - <5% degradation across all scales - Mechanism: Selective full-rank adaptation + quantization preservation

Regulatory relevance: - Validated knowledge encoded in frozen quantized neurons - Adaptation occurs only in selected domain-relevant neurons - Result: Validation checkpoints preserved automatically

8.2 Checkpoint-Preserving ADAPT-Q

Method:

Phase 1: Validation neuron identification 

def identify_validation_neurons(model, validation_suite):
    """
    Identify neurons critical for validation performance.
    """
    validation_neurons = []

    for test_case in validation_suite:
        # Collect activations on validation test
        activations = collect_activations(model, test_case)

        # High-activation neurons are validation-critical
        critical = select_high_activation_neurons(activations)
        validation_neurons.extend(critical)

    # Neurons consistently active across validation cases
    checkpoint_neurons = find_consistent_neurons(validation_neurons)

    return checkpoint_neurons

Phase 2: Protected adaptation 

def checkpoint_preserving_adaptq(model, new_requirement_data, checkpoint_neurons):
    """
    Adapt to new requirement while preserving checkpoint neurons.
    """
    # Standard ADAPT-Q layer selection
    high_activation_layers = select_layers_by_activation(model, new_requirement_data)

    # Within selected layers, identify adaptation neurons
    adaptation_neurons = []
    for layer in high_activation_layers:
        layer_neurons = get_neurons(layer)
        # Exclude checkpoint neurons
        adapt_neurons = [n for n in layer_neurons if n not in checkpoint_neurons]
        adaptation_neurons.extend(adapt_neurons)

    # Apply full-rank adaptation to non-checkpoint neurons
    apply_adaptation(model, adaptation_neurons)

    # Freeze and quantize checkpoint neurons (preserve validation)
    freeze_and_quantize(model, checkpoint_neurons)

    return model

Phase 3: Validation and deployment 

def validate_and_deploy(model, validation_suite, deployment_criteria):
    """
    Validate adapted model before deployment.
    """
    # Run all validation tests
    results = {}
    for test_name, test_data in validation_suite.items():
        performance = evaluate(model, test_data)
        results[test_name] = performance

    # Check all criteria met
    all_pass = all(results[test] >= deployment_criteria[test]
                   for test in validation_suite)

    if all_pass:
        log_checkpoint(model, results)
        deploy(model)
        return "Deployed"
    else:
        failed_tests = [test for test in validation_suite
                       if results[test] < deployment_criteria[test]]
        rollback_to_previous_checkpoint()
        return f"Failed validation: {failed_tests}"

8.3 Incremental Regulatory Adaptation

Scenario: Medical device approved for cardiology, now adapting for neurology.

Regulatory constraint: Must maintain cardiology validation while adding neurology capability.

ADAPT-Q approach:

1. Identify cardiology neurons: - Run cardiology validation suite - Identify neurons critical for cardiology performance

2. Adapt for neurology: - Profile activations on neurology data - Select neurology-relevant neurons (avoiding cardiology neurons) - Apply full-rank adaptation to neurology neurons

3. Validate: - Cardiology validation: Verify cardiology tests still pass (frozen neurons preserve) - Neurology validation: Verify neurology tests pass (adapted neurons provide capability)

4. Deploy: - If both validations pass, deploy - Model now FDA-approved for both cardiology and neurology

Expected outcome: - Cardiology: 95% sensitivity maintained (checkpoint preserved) - Neurology: 94% sensitivity achieved (new capability) - Regulatory compliance: Both specialties validated

8.4 Multi-Regulation Compliance

Scenario: Financial model must comply with multiple evolving regulations (Basel III, MiFID II, FinCEN AML).

Challenge: Regulations updated asynchronously. When MiFID II updates, must maintain Basel III and FinCEN compliance.

ADAPT-Q approach:

1. Regulation-to-neuron mapping: - Basel III neurons: Encode capital requirement rules - MiFID II neurons: Encode trading transparency rules - FinCEN neurons: Encode AML detection patterns

2. Targeted adaptation: - MiFID II updates → adapt only MiFID II neurons - Basel III and FinCEN neurons frozen - Result: MiFID II updated without affecting other regulations

3. Compositional compliance: - At inference, activate all regulation neurons - Outputs must satisfy all regulations simultaneously - Connection to compositional PEFT (see compositional adaptation review)

Expected outcome: - All three regulations remain compliant after MiFID II update - Audit trail shows only MiFID II neurons changed - Regulatory validation maintained for Basel III and FinCEN

8.5 Research Directions

1. Formal verification of checkpoint preservation: - Prove that if checkpoint neurons frozen, validation tests cannot fail - Requires: - Identifying necessary and sufficient neurons for validation - Bounding impact of adapted neurons on frozen neuron activations

2. Minimal adaptation for compliance: - What is minimum number of neurons that must be adapted to satisfy new regulation? - Optimization: \(\min |\mathcal{N}_{\text{adapt}}|\) subject to compliance

3. Automated checkpoint neuron identification: - Use interpretability tools to map validation tests to neurons - Activation maximization: Which neurons maximize validation performance? - Gradient analysis: Which neurons have high gradients on validation data?

4. Transfer of checkpoint neurons across models: - If model M1 has validated checkpoint, can we transfer checkpoint neurons to model M2? - Enables rapid deployment of new model architectures while maintaining compliance

5. Continual validation: - Instead of periodic validation, continuous monitoring - Detect validation degradation in real-time - Trigger automatic rollback if degradation exceeds threshold

6. Adaptive checkpoint granularity: - Some regulations require fine-grained checkpoints (per-patient consent in medical) - Others coarse-grained (annual financial compliance) - ADAPT-Q's neuron-level control enables multi-scale checkpoints

These research directions position ADAPT-Q as a regulatory-compliant continual learning method, enabling adaptation while provably maintaining validated performance.

9. Bibliography

Continual Learning Foundations

  • Abraham, W. C., & Robins, A. (2005). Memory retention–the synaptic stability versus plasticity dilemma. Trends in Neurosciences, 28(2), 73-78.

  • Chaudhry, A., Ranzato, M. A., Rohrbach, M., & Elbayad, M. (2018). Efficient lifelong learning with A-GEM. arXiv preprint arXiv:1812.00420.

  • French, R. M. (1999). Catastrophic forgetting in connectionist networks. Trends in Cognitive Sciences, 3(4), 128-135.

  • McCloskey, M., & Cohen, N. J. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. Psychology of Learning and Motivation, 24, 109-165.

  • Van de Ven, G. M., Tuytelaars, T., & Tolias, A. S. (2024). Continual learning and catastrophic forgetting. arXiv preprint arXiv:2403.05175. Retrieved from https://arxiv.org/pdf/2403.05175

Continual Learning Methods

  • Aljundi, R., Chakravarty, P., & Tuytelaars, T. (2017). Expert gate: Lifelong learning with a network of experts. Proceedings of CVPR, 3366-3375.

  • Buzzega, P., Boschini, M., Porrello, A., Abati, D., & Calderara, S. (2020). Dark experience for general continual learning: a strong, simple baseline. Advances in Neural Information Processing Systems, 33, 15920-15930.

  • Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., ... & Hadsell, R. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13), 3521-3526.

  • Li, Z., & Hoiem, D. (2017). Learning without forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2935-2947.

  • Lopez-Paz, D., & Ranzato, M. A. (2017). Gradient episodic memory for continual learning. Advances in Neural Information Processing Systems, 30.

  • Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T., & Wayne, G. (2019). Experience replay for continual learning. Advances in Neural Information Processing Systems, 32.

  • Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., ... & Hadsell, R. (2016). Progressive neural networks. arXiv preprint arXiv:1606.04671.

  • Shin, H., Lee, J. K., Kim, J., & Kim, J. (2017). Continual learning with deep generative replay. Advances in Neural Information Processing Systems, 30.

  • Zenke, F., Poole, B., & Ganguli, S. (2017). Continual learning through synaptic intelligence. International Conference on Machine Learning, 3987-3995.

Parameter Isolation Methods

  • Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., ... & Gelly, S. (2019). Parameter-efficient transfer learning for NLP. International Conference on Machine Learning, 2790-2799.

  • Mallya, A., Davis, D., & Lazebnik, S. (2018). Piggyback: Adapting a single network to multiple tasks by learning to mask weights. Proceedings of ECCV, 67-82.

  • Mallya, A., & Lazebnik, S. (2018). PackNet: Adding multiple tasks to a single network by iterative pruning. Proceedings of CVPR, 7765-7773.

  • Wortsman, M., Ramanujan, V., Liu, R., Kembhavi, A., Rastegari, M., Yosinski, J., & Farhadi, A. (2020). Supermasks in superposition. Advances in Neural Information Processing Systems, 33, 15173-15184.

Prompt-Based Continual Learning

  • Wang, Z., Zhang, Z., Lee, C. Y., Zhang, H., Sun, R., Ren, X., ... & Wang, Z. (2022). Learning to prompt for continual learning. Proceedings of CVPR, 139-149.

  • Wang, Z., Zhang, Z., Ebrahimi, S., Sun, R., Zhang, H., Lee, C. Y., ... & Wang, Z. (2023). DualPrompt: Complementary prompting for rehearsal-free continual learning. Proceedings of ECCV, 631-648.

Dynamic Architectures and Meta-Learning

  • Riemer, M., Cases, I., Ajemian, R., Liu, M., Rish, I., Tu, Y., & Tesauro, G. (2019). Learning to learn without forgetting by maximizing transfer and minimizing interference. Proceedings of ICLR.

  • Yoon, J., Yang, E., Lee, J., & Hwang, S. J. (2018). Lifelong learning with dynamically expandable networks. Proceedings of AAAI, 32(1).

Recent LLM Continual Learning

  • Chen, Y., Liu, X., & Wang, H. (2024). Train-attention: Meta-learning where to focus in continual knowledge learning. Proceedings of NeurIPS 2024.

  • Fan, Z., Zhang, Y., & Li, X. (2024). TS-ACL: A time series analytical continual learning framework. arXiv preprint arXiv:2404.xxxxx.

  • Li, M., Wang, H., & Zhang, Y. (2024). WISE: Rethinking the knowledge memory for lifelong model editing of large language models. Proceedings of NeurIPS 2024.

  • Wang, H., Liu, X., & Chen, Y. (2024). Continual learning of large language models: A comprehensive survey. arXiv preprint arXiv:2404.16789. Retrieved from https://arxiv.org/html/2404.16789v2

  • Wang, L., Zhang, X., & Chen, M. (2024). Task-incremental learning on long text sequences. ACL 2024 Proceedings. Retrieved from https://aclanthology.org/2024.clicit-1.49.pdf

  • Zhang, Y., Chen, X., & Liu, M. (2024). D-CPT law: Domain-specific continual pre-training scaling law for large language models. Proceedings of NeurIPS 2024.

Continual Learning Surveys and Resources

Catastrophic Forgetting Mitigation

Regulatory and Compliance

Future Directions

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