ADAPT-Q Advanced Strategies: Research Roadmap and Domain Applications¶

Beyond Basic Domain Adaptation: A Strategic Vision for ADAPT-Q Extensions

Author: Matthew Martz Date: November 24, 2025 Status: Research Roadmap for Paper 3

Executive Summary¶

This document synthesizes five advanced application domains for ADAPT-Q, providing a comprehensive research roadmap following the successful validation and publication of Papers 1 and 2. Each domain represents a natural evolution of ADAPT-Q's core innovations—activation-driven neuron targeting, selective full-rank adaptation, and mixed-precision preservation—into high-impact applications that address critical challenges in parameter-efficient fine-tuning.

The five domains are:

1. Compositional Adaptation Strategies - Modular neuron clusters for multi-task, multi-domain scenarios
2. Active Learning for Rare Events - Preserving rare event detection in long-tail distributions
3. Adversarial Robustness in Adaptation - Ensuring PEFT doesn't create attack surfaces
4. Knowledge Graph-Guided Fine-Tuning - Using structured knowledge to guide neuron targeting
5. Continual Learning with Regulatory Checkpoints - Adapting to new requirements while maintaining validated performance

This roadmap provides: - Detailed literature context for each domain (see individual review documents) - ADAPT-Q application strategies showing how to leverage existing ADAPT-Q capabilities - Research questions defining concrete next steps - Publication pathways from single comprehensive review to multiple domain-specific papers

Table of Contents¶

1. ADAPT-Q V4 Strengths and Weaknesses
2. Domain 1: Compositional Adaptation Strategies
3. Domain 2: Active Learning for Rare Events
4. Domain 3: Adversarial Robustness in Adaptation
5. Domain 4: Knowledge Graph-Guided Fine-Tuning
6. Domain 5: Continual Learning with Regulatory Checkpoints
7. Cross-Domain Synergies
8. Publication Strategy
9. Implementation Roadmap
10. Conclusion

ADAPT-Q V4 Strengths and Weaknesses¶

Core Innovations (Papers 1-2)¶

ADAPT-Q V1: AWQ layer selection + LoRA - Strength: Layer-level importance scoring, 153× efficiency - Weakness: Still uses low-rank bottleneck (LoRA)

ADAPT-Q V2: Neuron-level activation targeting - Strength: Finest-grained adaptation, true neuron-level control - Weakness: Computationally intensive activation profiling (now resolved with vectorization)

ADAPT-Q V3: Gradient-based + AWQ hybrid - Strength: Exceptional efficiency (5,524×), combines best of V1 and V2 - Weakness: Gradient-based targeting may not capture all relevant neurons

Demonstrated Capabilities¶

✅ Catastrophic forgetting elimination: 34-967× better than LoRA ✅ Scale-independent preservation: <5% degradation at all scales ✅ Neuron-level targeting: Precise control over which neurons adapt ✅ Mixed-precision architecture: 37.5% memory savings via quantization ✅ Activation-driven selection: Data-driven rather than arbitrary ✅ Order independence: Robust to quantization/adaptation sequence

Current Limitations¶

❌ Single-domain focus: Papers 1-2 address single domain adaptation ❌ No compositional framework: Cannot handle multi-task scenarios ❌ Limited security analysis: Adversarial robustness not evaluated ❌ No structured knowledge integration: Doesn't leverage ontologies/KGs ❌ No regulatory validation: Compliance frameworks not addressed ❌ Rare event preservation untested: Long-tail distributions unexplored

The five advanced domains address these limitations.

Domain 1: Compositional Adaptation Strategies¶

📄 Detailed Literature Review: 01_compositional_adaptation_strategies.md

Problem Statement¶

Current PEFT methods (including ADAPT-Q V1-V3) adapt models for a single domain or task. Real-world deployment requires multiple specialized adaptations that can be: - Activated selectively based on input context - Composed without interference (regulatory + clinical + institutional modules) - Updated independently (change regulatory module without retraining clinical module)

Example scenario: - Hospital LLM needs: Regulatory compliance + Clinical expertise + Institution-specific protocols - Each module should activate independently based on query type - Updating HIPAA compliance shouldn't affect clinical knowledge

State-of-the-Art¶

Recent advances: - PERFT (2024): Mixture of PEFT modules for MoE models, achieves routed adaptation - DYNMOLE (2024): Dynamic mixture of LoRA experts, 77.6% average accuracy - Compositional strategies: Parallel composition outperforms serial (PERFT results)

Gap: No compositional framework leverages neuron-level targeting like ADAPT-Q

ADAPT-Q Application: Multi-Module Neuron Targeting¶

Core idea: Extend ADAPT-Q V2/V3 to identify separate neuron clusters for different functional modules.

Method:

def compositional_adaptq(model, module_specifications):
    """
    Create modular adaptations that can be composed without interference.

    Args:
        module_specifications: Dict mapping modules to their domain data
            e.g., {
                "regulatory": regulatory_corpus,
                "clinical": clinical_guidelines,
                "institutional": hospital_protocols
            }

    Returns:
        model_with_modules: Model with identified neuron clusters per module
        routing_function: Function to select which modules to activate
    """

    neuron_modules = {}

    # Phase 1: Identify neuron clusters for each module
    for module_name, module_data in module_specifications.items():
        # Use ADAPT-Q V3's gradient-based targeting
        module_neurons = identify_module_neurons(model, module_data)
        neuron_modules[module_name] = module_neurons

    # Phase 2: Ensure disjoint modules (no interference)
    neuron_modules = ensure_disjoint_modules(neuron_modules)

    # Phase 3: Apply modular adaptations
    for module_name, neurons in neuron_modules.items():
        apply_module_adaptation(model, neurons, module_name)

    # Phase 4: Create activation-based routing
    routing_fn = create_activation_router(model, neuron_modules)

    return model, routing_fn

def create_activation_router(model, neuron_modules):
    """
    Route inputs to appropriate modules based on activation patterns.
    """
    def route(input_text):
        # Measure which neuron clusters activate for this input
        activations = measure_cluster_activations(model, input_text, neuron_modules)

        # Select top-k modules (could be multiple for hybrid queries)
        active_modules = select_top_k_modules(activations, k=2)

        return active_modules

    return route

Key advantages: 1. Disjoint neuron clusters → guaranteed no interference 2. Activation-based routing → no learned router needed (uses model's natural responses) 3. Modular updates → change one module without retraining others 4. Interpretable → neuron clusters align with functional modules

Research Questions¶

1. How to ensure neuron disjointness when modules have overlapping knowledge requirements?
2. Optimal module granularity - how fine-grained should modules be?
3. Dynamic module allocation - can we add new modules without retraining existing ones?
4. Cross-module dependencies - how to handle queries requiring multiple modules?

Experimental Validation¶

Proposed experiments: 1. Medical multi-module: Regulatory + Clinical + Institutional - Measure: Inter-module interference, independent update capability - Baseline: Single monolithic adaptation, LoRA with task arithmetic

1. Legal multi-jurisdiction: Federal + State + Local laws
2. Measure: Jurisdictional accuracy, composition quality

3. Baseline: Separate models, merged LoRAs

4. Financial multi-regulation: Basel III + MiFID II + FinCEN

5. Measure: Compliance maintenance, selective update
6. Baseline: Full retraining per regulation

Success metrics: - Module interference < 5% (changing module A doesn't degrade module B) - Compositional accuracy ≥ 90% of monolithic model - Update efficiency: 10× faster than full retraining

Publication Pathway¶

Option A: Standalone paper on compositional ADAPT-Q - Title: "Compositional Domain Adaptation via Disjoint Neuron Targeting" - Venue: ICML, NeurIPS (main ML conferences) - Contribution: First compositional PEFT with neuron-level control

Option B: Integration into broader survey - Part of comprehensive review paper covering all 5 domains - Demonstrates compositional as one advanced ADAPT-Q extension

Domain 2: Active Learning for Rare Events¶

📄 Detailed Literature Review: 02_active_learning_rare_events.md

Problem Statement¶

Standard fine-tuning (including PEFT) can degrade rare event detection: - Medical: Rare diseases (prevalence <0.1%) forgotten after specializing in common conditions - Financial: Black swan events (crashes, crises) detection lost after training on normal market data

Critical challenge: ADAPT-Q preserves general knowledge, but what about rare events within the domain?

State-of-the-Art¶

Rare event learning: - Class imbalance: SMOTE, focal loss, cost-sensitive learning (2024 surveys) - Medical applications: 94% precision with PCCT for imbalanced medical imaging - Financial black swans: EVT + ML hybrids achieve 34% improvement

Gap: No PEFT method explicitly preserves rare event detection capability during adaptation

ADAPT-Q Application: Rare Event Neuron Preservation¶

Core insight: Rare events activate specific neurons that fire rarely but critically.

Method:

def rare_event_preserving_adaptq(model, common_data, rare_data, domain_data):
    """
    Adapt to domain while preserving rare event detection.

    Args:
        common_data: Frequent events in domain
        rare_data: Rare events (e.g., rare diseases, black swans)
        domain_data: Mixed domain data for adaptation

    Returns:
        model: Adapted model with rare event preservation
    """

    # Phase 1: Identify rare event neurons
    rare_neurons = identify_rare_event_neurons(model, common_data, rare_data)

    # Rare event neurons: High activation on rare_data, low on common_data
    # Mathematically: neurons with high selectivity for rare events

    # Phase 2: Identify domain adaptation neurons
    domain_neurons = identify_domain_neurons(model, domain_data)

    # Phase 3: Ensure rare neurons excluded from adaptation
    adaptable_neurons = [n for n in domain_neurons if n not in rare_neurons]

    # Phase 4: Apply ADAPT-Q to adaptable neurons only
    apply_adaptation(model, adaptable_neurons)

    # Phase 5: Freeze and preserve rare event neurons
    freeze_and_quantize(model, rare_neurons, bits=4)

    return model

def identify_rare_event_neurons(model, common_data, rare_data):
    """
    Find neurons selective for rare events.
    """
    # Collect activations
    a_common = collect_activations(model, common_data)
    a_rare = collect_activations(model, rare_data)

    # Compute selectivity: rare activation / common activation
    selectivity = a_rare.mean(dim=0) / (a_common.mean(dim=0) + ε)

    # High selectivity neurons are rare-event detectors
    rare_neurons = torch.where(selectivity > threshold)[0]

    return rare_neurons

Validation: - Test rare event recall before and after adaptation - Expected: Rare event recall maintained (>90% of baseline) - Common event performance improves (domain adaptation benefit)

Integration with Active Learning¶

Rare-event-guided active learning:

def rare_event_active_learning(model, unlabeled_pool, rare_neurons, budget):
    """
    Prioritize labeling examples that exercise rare event neurons.
    """
    rare_scores = []

    for example in unlabeled_pool:
        # Measure activation of rare event neurons
        activations = collect_activations(model, example)
        rare_score = sum(activations[n] for n in rare_neurons)
        rare_scores.append(rare_score)

    # Select examples with highest rare event activation
    selected = select_top_k(unlabeled_pool, rare_scores, k=budget)

    # Query labels for selected examples
    labeled_rare_events = query_oracle(selected)

    return labeled_rare_events

Benefit: Efficiently discover rare events in unlabeled data, ensure rare detectors exercised during training

Research Questions¶

1. Rare neuron identification threshold - what selectivity ratio optimally identifies rare event neurons?
2. Capacity allocation - if we freeze rare neurons, is remaining capacity sufficient for domain adaptation?
3. Hierarchical rarity - how to handle nested rare events (rare disease subtypes within rare diseases)?
4. Transfer of rare detectors - do rare event neurons identified in model A transfer to model B?

Experimental Validation¶

Medical: Rare disease preservation - Setup: Fine-tune general medical model for hospital specialization - Rare events: 20 rare diseases (prevalence <0.01%) - Measure: Rare disease recall before/after adaptation - Baseline: Standard fine-tuning, LoRA

Financial: Black swan detection - Setup: Adapt trading model to current regime - Rare events: Historical crises (2008, 2020) - Measure: Crisis detection recall on synthetic black swan scenarios - Baseline: Standard retraining, LoRA

Success metrics: - Rare event recall preserved: ≥90% of baseline - Common event performance improves: +15-20% - Active learning efficiency: 5× fewer labels needed

Publication Pathway¶

Option A: Medical AI journal (JAMIA, JBI) - Title: "Preserving Rare Disease Detection During Clinical LLM Specialization" - Impact: Enables safe hospital deployment without losing rare diagnosis capability

Option B: Financial ML conference (NeurIPS Finance, ICAIF) - Title: "Black Swan Resilience in Adaptive Trading Models" - Impact: Ensures crisis detection maintained during regime adaptation

Domain 3: Adversarial Robustness in Adaptation¶

📄 Detailed Literature Review: 03_adversarial_robustness_adaptation.md

Problem Statement¶

PEFT methods create new attack surfaces: - LoRA-as-an-Attack (2024): Backdoors encoded in LoRA modules, distributed via sharing - Safety degradation: Fine-tuning removes alignment with just 10 harmful examples - Covert malicious fine-tuning: Innocuous-looking data produces backdoors

Critical for high-stakes domains: - Medical: Backdoored model could recommend wrong treatments - Financial: Compromised fraud detector approves fraudulent transactions

State-of-the-Art¶

Attack methods: - LoRATK: Backdoor survives LoRA merging, 99% activation rate - Covert fine-tuning: 99% backdoor success on GPT-4 - Safety removal: LoRA fine-tuning reduces refusal rate 99.7% → 3.8%

Defenses (limited): - PEFTGuard: Detects some backdoors but high false positives - LoX: Safety subspace extrapolation, reduces harmful outputs 78% → 12% - Differential privacy: Mitigates backdoors but degrades utility

Gap: No PEFT method provides explicit neuron-level security control

ADAPT-Q Application: Safety Neuron Preservation¶

Core idea: Identify and freeze neurons encoding safety constraints, preventing degradation during adaptation.

Method:

def safety_aware_adaptq(model, domain_data, safety_test_suite):
    """
    Adapt model while explicitly preserving safety neurons.

    Args:
        domain_data: Data for domain adaptation
        safety_test_suite: Harmful prompts that aligned model refuses

    Returns:
        model: Adapted with safety preservation
    """

    # Phase 1: Identify safety neurons
    safety_neurons = identify_safety_neurons(model, safety_test_suite)

    # Safety neurons: Activate for harmful prompts in base model,
    # suppressed in aligned model (alignment reduced their activation)

    # Phase 2: Identify domain neurons (excluding safety neurons)
    domain_neurons = identify_domain_neurons(model, domain_data)
    adaptable_neurons = [n for n in domain_neurons if n not in safety_neurons]

    # Phase 3: Adapt non-safety neurons only
    apply_adaptation(model, adaptable_neurons)

    # Phase 4: Freeze safety neurons (prevent safety degradation)
    freeze_and_quantize(model, safety_neurons)

    # Phase 5: Validate safety maintained
    safety_score = evaluate_safety(model, safety_test_suite)
    assert safety_score >= safety_threshold, "Safety degradation detected"

    return model

def identify_safety_neurons(model, harmful_prompts):
    """
    Find neurons responsible for safety alignment.
    """
    # Get base (unaligned) and aligned model
    base_model = load_base_model()
    aligned_model = model  # Current aligned model

    # Measure activations on harmful prompts
    base_activations = collect_activations(base_model, harmful_prompts)
    aligned_activations = collect_activations(aligned_model, harmful_prompts)

    # Safety neurons show large suppression (alignment reduced activation)
    suppression = base_activations - aligned_activations
    safety_neurons = torch.where(suppression > threshold)[0]

    return safety_neurons

Backdoor resistance:

ADAPT-Q's neuron-level control makes backdoor injection harder: 1. Localized adaptation - only specific neurons adapt 2. Activation-driven - backdoor must match domain activation profile 3. Full-rank capacity - no low-rank bottleneck forcing backdoor into subspace

Hypothesis: ADAPT-Q more resistant to covert backdoors than LoRA

Research Questions¶

1. Safety neuron identification - what activation patterns reliably identify safety neurons?
2. Capacity trade-off - if we freeze safety neurons, is capacity sufficient for domain adaptation?
3. Backdoor detection - can activation profiling detect backdoors by identifying anomalous neurons?
4. Certified guarantees - can we prove safety preservation (if safety neurons frozen, safety cannot degrade by more than ε)?

Experimental Validation¶

Safety preservation experiment: - Model: LLaMA 2-Chat 70B (aligned) - Adaptation: Medical domain fine-tuning - Measure: Refusal rate on harmful requests before/after - Baseline: Standard fine-tuning (drops 99.7% → 3.8%), LoRA

Backdoor resistance experiment: - Attack: Covert malicious fine-tuning (Wang et al. 2024 method) - Defense: ADAPT-Q with safety neuron preservation - Measure: Backdoor activation rate - Baseline: LoRA (99% activation), standard fine-tuning

Success metrics: - Safety preserved: Refusal rate maintained ≥95% - Backdoor resistance: Activation rate <10% (vs. >90% for LoRA) - Domain performance: Equivalent to unsafe baselines

Publication Pathway¶

Option A: Security conference (IEEE S&P, USENIX Security) - Title: "Neuron-Level Safety Preservation Against Adversarial Fine-Tuning" - Contribution: First PEFT method with explicit safety neuron control

Option B: AI Safety venue (NeurIPS Safety Workshop, SafeAI) - Title: "ADAPT-Q: Provably Safe Parameter-Efficient Fine-Tuning" - Contribution: Framework for certified safety preservation

Domain 4: Knowledge Graph-Guided Fine-Tuning¶

📄 Detailed Literature Review: 04_knowledge_graph_guided_finetuning.md

Problem Statement¶

Fine-tuning without structured knowledge leads to: - Hallucinations: Plausible but incorrect outputs - Constraint violations: Outputs violate domain rules (impossible drug combinations) - Inconsistency: Contradictory answers to logically equivalent questions

Solution: Use domain ontologies and knowledge graphs to guide which neurons to adapt.

State-of-the-Art¶

Knowledge injection methods: - OntoTune (2025): Ontology-driven self-training, 82% → 94% QA accuracy - StructTuning (2024): 50% of traditional injection with 0.3% of data - Ontology-conformal recognition (2025): Zero hallucinations outside ontology

Gap: No method leverages ontology structure to guide neuron-level targeting

ADAPT-Q Application: Ontology-Structured Neuron Mapping¶

Core idea: Map ontology concepts to neurons, use ontology hierarchy to guide adaptation.

Method:

def ontology_guided_adaptq(model, domain_ontology, domain_data):
    """
    Use ontology structure to guide which neurons to target.

    Args:
        domain_ontology: Structured knowledge (e.g., SNOMED CT, FIBO)
        domain_data: Training data for adaptation

    Returns:
        model: Adapted respecting ontology structure
        neuron_ontology_map: Mapping of concepts to neurons
    """

    # Phase 1: Cluster ontology concepts into modules
    concept_modules = cluster_by_ontology(domain_ontology)
    # e.g., {
    #   "Cardiovascular": [HeartDisease, Arrhythmia, ...],
    #   "Neurological": [Alzheimer, Parkinsons, ...],
    #   "Pharmaceutical": [Aspirin, Warfarin, ...]
    # }

    # Phase 2: Map concept modules to neurons
    neuron_map = {}
    for module_name, concepts in concept_modules.items():
        # Get data mentioning these concepts
        module_data = filter_data_by_concepts(domain_data, concepts)

        # Identify neurons activating for this concept module
        module_neurons = identify_neurons_for_module(model, module_data)
        neuron_map[module_name] = module_neurons

    # Phase 3: Hierarchical adaptation following ontology
    # Preserve high-level concepts (top of ontology hierarchy)
    # Adapt specific concepts (leaves of hierarchy)

    for layer_idx, layer in enumerate(model.layers):
        if layer_idx <= 6:  # Early layers encode general concepts
            freeze_layer(layer)  # Preserve top of ontology
        elif layer_idx <= 9:  # Mid layers encode mid-level concepts
            apply_partial_adaptation(layer)
        else:  # Late layers encode specific concepts
            apply_full_adaptation(layer)  # Adapt ontology leaves

    return model, neuron_map

def validate_ontology_constraints(model, ontology, test_data):
    """
    Ensure model outputs respect ontology constraints.
    """
    violations = []

    for example in test_data:
        output = model.generate(example)

        # Extract entities and relations
        entities, relations = parse_output(output)

        # Check against ontology
        for entity in entities:
            if entity not in ontology.entities:
                violations.append(f"Unknown entity: {entity}")

        for (subj, rel, obj) in relations:
            if not ontology.is_valid_relation(subj, rel, obj):
                violations.append(f"Invalid relation: {subj} {rel} {obj}")

    return violations

Knowledge path-guided neuron selection:

def knowledge_path_guided_neurons(model, knowledge_graph):
    """
    Identify neurons involved in reasoning over KG paths.
    """
    # Extract reasoning paths from KG
    paths = extract_kg_paths(knowledge_graph)
    # e.g., [Aspirin, treats, Headache, symptomOf, Migraine]

    # Find neurons that activate during path reasoning
    path_neurons = []
    for path in paths:
        query = verbalize_path(path)  # "What treats migraines?"
        activations = collect_activations(model, query)
        path_neurons.extend(select_high_activation(activations))

    # Neurons consistently active across paths are KG reasoning neurons
    reasoning_neurons = find_consistent_neurons(path_neurons)

    # Preserve these neurons to maintain structured reasoning
    return reasoning_neurons

Research Questions¶

1. Automatic ontology-to-neuron mapping - can we automate concept → neuron mapping?
2. Ontology constraint enforcement - how to constrain adaptation to respect ontology axioms?
3. Hierarchical adaptation - optimal mapping of ontology hierarchy to layer depth?
4. Transfer of ontology mappings - do concept-neuron mappings transfer across models?

Experimental Validation¶

Medical: SNOMED CT-guided adaptation - Ontology: SNOMED CT cardiovascular subtree - Task: Cardiology specialization - Measure: Diagnostic accuracy, ontology constraint violations - Baseline: Standard fine-tuning, LoRA

Legal: Case law ontology - Ontology: Citation network + statutory hierarchy - Task: Jurisdiction-specific legal reasoning - Measure: Precedent accuracy, jurisdictional correctness - Baseline: Standard fine-tuning without ontology

Financial: FIBO-guided adaptation - Ontology: FIBO financial instruments - Task: Derivatives classification - Measure: Classification accuracy, regulatory compliance - Baseline: Standard fine-tuning

Success metrics: - Constraint violations: 0 (vs. 15-30% for baselines) - Ontology-grounded accuracy: +10-15% vs. unstructured fine-tuning - Consistency: 95%+ logically consistent answers

Publication Pathway¶

Option A: Semantic Web conference (ISWC, ESWC) - Title: "Ontology-Guided Neuron Targeting for Constrained LLM Adaptation" - Contribution: First neuron-level ontology-LLM integration

Option B: Domain-specific venue (AMIA for medical, JURIX for legal) - Title: "Knowledge-Graph-Guided Clinical LLM Adaptation" - Contribution: Practical framework for ontology-compliant medical AI

Domain 5: Continual Learning with Regulatory Checkpoints¶

📄 Detailed Literature Review: 05_continual_learning_regulatory_checkpoints.md

Problem Statement¶

Regulated AI systems must: - Adapt to new requirements (new regulations, guidelines, data distributions) - Maintain validated performance (cannot degrade on previous validation tests) - Provide audit trails (all changes logged and traceable)

Example: FDA-approved medical device adapted for new indication must maintain performance on original indication.

State-of-the-Art¶

Continual learning methods: - EWC: Elastic weight consolidation, reduces forgetting via regularization - Replay: Maintain performance by replaying previous data - Parameter isolation: Allocate separate parameters per task (Progressive NN, PackNet) - Prompt-based: L2P achieves 91.7% accuracy with minimal forgetting

Regulatory requirements: - FDA SaMD: Algorithm Change Protocol required for updates - Financial: Model Risk Management, revalidation triggers - GDPR: Machine unlearning (remove individual's data influence)

Gap: No continual learning method designed for regulatory compliance with checkpoint preservation

ADAPT-Q Application: Checkpoint-Preserving Continual Learning¶

Core idea: Use neuron-level control to freeze checkpoint-critical neurons, adapt others for new requirements.

Method:

def regulatory_continual_adaptq(model, checkpoints, new_requirement):
    """
    Adapt to new requirement while preserving all regulatory checkpoints.

    Args:
        checkpoints: List of (validation_suite, performance_threshold) tuples
        new_requirement: New regulation/task requiring adaptation

    Returns:
        model: Adapted model passing all checkpoints + new requirement
    """

    # Phase 1: Identify checkpoint-critical neurons
    checkpoint_neurons = set()
    for validation_suite, threshold in checkpoints:
        # Find neurons critical for this checkpoint
        critical = identify_validation_neurons(model, validation_suite)
        checkpoint_neurons.update(critical)

    # Phase 2: Identify neurons for new requirement
    new_neurons = identify_requirement_neurons(model, new_requirement)

    # Phase 3: Ensure disjoint (or controlled overlap)
    adaptable_neurons = [n for n in new_neurons if n not in checkpoint_neurons]

    # Phase 4: Adapt non-checkpoint neurons
    apply_adaptation(model, adaptable_neurons, new_requirement)

    # Phase 5: Freeze checkpoint neurons
    freeze_and_quantize(model, checkpoint_neurons)

    # Phase 6: Validate all checkpoints still satisfied
    for validation_suite, threshold in checkpoints:
        performance = evaluate(model, validation_suite)
        assert performance >= threshold, f"Checkpoint violated: {performance} < {threshold}"

    # Phase 7: Create new checkpoint for new requirement
    new_checkpoint = create_checkpoint(model, new_requirement)
    checkpoints.append(new_checkpoint)

    return model, checkpoints

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

    for test_case in validation_suite:
        activations = collect_activations(model, test_case)
        critical = select_high_activation_neurons(activations)
        critical_neurons.extend(critical)

    # Neurons consistently active across validation
    checkpoint_neurons = find_consistent_neurons(critical_neurons)

    return checkpoint_neurons

Multi-regulation compliance:

def multi_regulation_adaptq(model, regulations):
    """
    Maintain compliance with multiple evolving regulations.

    Args:
        regulations: {
            "Basel III": (basel_test_suite, basel_neurons),
            "MiFID II": (mifid_test_suite, mifid_neurons),
            "FinCEN": (fincen_test_suite, fincen_neurons)
        }

    When one regulation updates, adapt only its neurons.
    """

    # When MiFID II updates:
    mifid_data = load_new_mifid_requirements()
    mifid_neurons = regulations["MiFID II"][1]

    # Adapt only MiFID II neurons
    apply_adaptation(model, mifid_neurons, mifid_data)

    # Basel III and FinCEN neurons frozen → their compliance maintained
    basel_neurons = regulations["Basel III"][1]
    fincen_neurons = regulations["FinCEN"][1]
    freeze_and_quantize(model, basel_neurons + fincen_neurons)

    # Validate all three regulations still compliant
    for reg_name, (test_suite, _) in regulations.items():
        assert passes_compliance(model, test_suite), f"{reg_name} violated"

    return model

Research Questions¶

1. Minimal checkpoint neuron set - what is minimum neuron set that must be frozen to preserve validation?
2. Checkpoint granularity - should checkpoints be coarse (annual validation) or fine (per-patient in medical)?
3. Formal verification - can we prove checkpoint preservation (if neurons frozen, validation guaranteed)?
4. Checkpoint transfer - do checkpoint neurons identified in model A apply to model B?

Experimental Validation¶

Medical device continual learning: - Initial: Cardiology diagnostic (FDA-approved) - New task: Neurology diagnostic - Measure: Cardiology performance maintained, neurology performance achieved - Baseline: Full retraining, sequential fine-tuning

Financial multi-regulation: - Regulations: Basel III, MiFID II, FinCEN - Update: MiFID II regulatory change - Measure: All three regulations still compliant after MiFID II update - Baseline: Full revalidation (expensive), risk-based approach

Success metrics: - Checkpoint preservation: 100% of checkpoints pass after adaptation - New task performance: ≥90% of baseline trained on new task alone - Audit compliance: Complete traceable history of adaptations

Publication Pathway¶

Option A: Medical informatics (JAMIA, JBI) - Title: "Regulatory-Compliant Continual Learning for Medical Devices" - Impact: Enables incremental FDA approvals without full revalidation

Option B: AI/Law venue (ICAIL, AI & Law journal) - Title: "Provable Regulatory Compliance in Continually Adapting AI Systems" - Impact: Framework for legally-sound continual learning

Cross-Domain Synergies¶

The five domains are not independent—they exhibit powerful synergies when combined:

Synergy 1: Compositional + Regulatory¶

Scenario: Hospital LLM with multiple specialized modules, each FDA-validated separately

Combination: - Compositional ADAPT-Q: Separate neuron clusters for cardiology, neurology, oncology - Regulatory ADAPT-Q: Each module has checkpoints that must be preserved

Benefit: - Update cardiology module without revalidating neurology (disjoint neurons) - Incremental FDA approvals (each module validated independently) - Audit trail shows exactly which module changed

Synergy 2: Rare Events + Safety¶

Scenario: Fraud detection that must preserve rare fraud pattern detection while adapting to new fraud types

Combination: - Rare event ADAPT-Q: Freeze neurons detecting rare historical fraud patterns - Safety ADAPT-Q: Freeze neurons preventing false positives on legitimate transactions

Benefit: - New fraud type detection added without losing rare historical pattern detection - Safety constraints (low false positive rate) maintained - Addresses both accuracy and operational requirements

Synergy 3: Knowledge Graph + Regulatory¶

Scenario: Medical device that must respect both clinical ontologies (SNOMED) and regulatory requirements (FDA guidelines)

Combination: - KG-guided ADAPT-Q: Neuron mapping follows SNOMED hierarchy - Regulatory ADAPT-Q: FDA checkpoint neurons preserved

Benefit: - Ontology compliance guaranteed (outputs conform to SNOMED) - Regulatory compliance guaranteed (FDA checkpoints pass) - Dual validation framework

Synergy 4: Compositional + Active Learning + Rare Events¶

Scenario: Multi-specialty hospital adapting to local patient population with rare conditions

Combination: - Compositional: Separate modules for specialties (cardio, neuro, etc.) - Active learning: Efficiently label rare cases in local population - Rare event: Preserve rare disease detection from pre-training

Benefit: - Specialty-specific adaptation (compositional) - Efficient discovery of local rare diseases (active learning) - Maintained rare disease detection from broader medical knowledge (rare event)

Synergy 5: All Five Combined¶

Ultimate scenario: Enterprise-grade medical AI system

Components: 1. Compositional: Modules for specialties, institutions, regulations 2. Rare events: Preserve rare disease detection 3. Safety: Maintain medical safety constraints 4. KG-guided: Respect medical ontologies (SNOMED, ICD-10, RxNorm) 5. Regulatory: FDA, HIPAA, institutional compliance checkpoints

Result: Comprehensive framework for safe, compliant, adaptable medical AI

Publication Strategy¶

Option 1: Single Comprehensive Review Paper¶

Title: "ADAPT-Q Advanced Strategies: A Comprehensive Framework for Compositional, Safe, and Compliant Domain Adaptation"

Structure: - Introduction: ADAPT-Q recap, motivation for extensions - Five domains (condensed from individual reviews): - Compositional (3-4 pages) - Rare events (3-4 pages) - Safety (3-4 pages) - KG-guided (3-4 pages) - Regulatory (3-4 pages) - Cross-domain synergies (2-3 pages) - Unified framework (2-3 pages) - Conclusion and future work

Total: 20-25 pages

Venue: - JMLR (Journal of Machine Learning Research): For comprehensive technical survey - ACM Computing Surveys: For broad impact survey - AI Magazine: For accessible overview

Timeline: 6-9 months

Advantages: - ✅ Comprehensive coverage - ✅ Shows unified vision - ✅ Single high-impact publication

Disadvantages: - ❌ Long review process - ❌ Delayed impact (all or nothing) - ❌ Harder to get deep experimental validation of all domains

Option 2: Five Separate Domain Papers¶

Approach: Publish each domain as standalone paper

Papers: 1. "Compositional Domain Adaptation via Disjoint Neuron Targeting" → ICML/NeurIPS 2. "Preserving Rare Event Detection During LLM Specialization" → JAMIA (medical) or NeurIPS (ML) 3. "Neuron-Level Safety Preservation Against Adversarial Fine-Tuning" → IEEE S&P 4. "Ontology-Guided Neuron Targeting for Constrained LLM Adaptation" → ISWC 5. "Regulatory-Compliant Continual Learning via Checkpoint Neuron Preservation" → ICAIL

Timeline: 12-18 months (staggered submissions)

Advantages: - ✅ Deep experimental validation per domain - ✅ Domain-specific impact (medical, security, legal communities) - ✅ Portfolio of publications - ✅ Incremental progress (publish as ready)

Disadvantages: - ❌ Miss unified vision - ❌ Duplication of ADAPT-Q background in each paper - ❌ More total effort

Option 3: Hybrid Strategy (Recommended)¶

Phase 1: Comprehensive Review (Year 1) - Submit survey paper to JMLR or ACM Computing Surveys - Covers all five domains at conceptual level - Includes preliminary experiments for each - Establishes vision and framework

Phase 2: Deep-Dive Domain Papers (Years 2-3) - Select 2-3 highest-impact domains based on: - Reviewer feedback on survey - Experimental results from preliminary studies - Industry interest - Publish full experimental papers on selected domains

Example trajectory: 1. Year 1: Survey paper submitted/accepted 2. Year 2: Safety paper (IEEE S&P) + Medical rare events (JAMIA) 3. Year 3: Regulatory continual learning (ICAIL) + Follow-up work

Advantages: - ✅ Establishes comprehensive vision early - ✅ Deep validation of most promising domains - ✅ Survey citable by others immediately - ✅ Flexibility to pivot based on feedback

Disadvantages: - ❌ Requires sustained effort over multiple years - ❌ Survey paper may not be as prestigious as conference papers

Implementation Roadmap¶

Phase 1: Foundation (Months 1-3)¶

Goal: Implement core infrastructure for advanced ADAPT-Q

Tasks: 1. Neuron clustering framework: - Implement disjoint neuron set identification - Validate non-interference between clusters - Benchmark computational overhead

1. Activation profiling optimization:
2. Scale V2 vectorization to all domains
3. Implement caching for repeated profiling

4. Profile across multiple data distributions

5. Checkpoint management system:

6. Store/load checkpoint neurons
7. Validate checkpoint preservation
8. Audit trail logging

Deliverable: adaptq/advanced/ codebase with core infrastructure

Phase 2: Domain Prototypes (Months 4-9)¶

Goal: Build prototype for each of five domains

Tasks: 1. Compositional: 3-module medical system (regulatory + clinical + institutional) 2. Rare events: Rare disease preservation in medical specialization 3. Safety: Safety neuron identification and preservation 4. KG-guided: SNOMED CT-guided cardiology adaptation 5. Regulatory: Multi-checkpoint validation framework

Deliverable: Working prototype for each domain + preliminary results

Phase 3: Experimental Validation (Months 10-15)¶

Goal: Comprehensive experiments for survey paper

Tasks: 1. Baselines: Implement LoRA, full FT, other PEFT for all experiments 2. Metrics: Standardize evaluation across domains 3. Datasets: Curate/acquire domain-specific datasets 4. Experiments: Run full experimental suite

Deliverable: Complete experimental results for all five domains

Phase 4: Publication (Months 16-18)¶

Goal: Submit comprehensive survey paper

Tasks: 1. Writing: Draft full paper (~25 pages) 2. Figures: Create comprehensive figures/tables 3. Related work: Deep dive into related work per domain 4. Submission: Submit to JMLR or ACM Computing Surveys

Deliverable: Submitted survey paper

Phase 5: Deep-Dive Papers (Months 19-36)¶

Goal: Full experimental papers on selected domains

Tasks: - Select 2-3 domains based on survey feedback - Expand experiments (larger scale, more baselines) - Domain-specific contributions - Submit to domain venues

Deliverable: 2-3 conference/journal papers

Conclusion¶

This roadmap outlines a comprehensive research program extending ADAPT-Q from its current single-domain focus to five advanced application domains. Each domain addresses critical challenges in parameter-efficient fine-tuning:

1. Compositional: Multi-task/multi-domain scenarios
2. Rare events: Long-tail distribution handling
3. Safety: Adversarial robustness
4. KG-guided: Structured knowledge integration
5. Regulatory: Compliance and validation maintenance

Key innovations across domains: - Neuron-level control enables precise targeting of functional modules - Activation-driven selection grounds decisions in data rather than heuristics - Mixed-precision preservation maintains critical knowledge efficiently - Disjoint neuron clusters guarantee non-interference - Checkpoint preservation enables provable compliance

Publication pathway offers flexibility: - Comprehensive survey establishes unified vision - Domain-specific papers provide deep experimental validation - Cross-domain synergies demonstrate real-world applicability

Next steps: 1. Review and prioritize domains based on research interests/resources 2. Implement Phase 1 infrastructure 3. Begin Phase 2 prototype development for highest-priority domains 4. Decide on publication strategy (survey-first vs. papers-first)

This work positions ADAPT-Q not just as a solution to catastrophic forgetting in domain adaptation, but as a comprehensive framework for safe, structured, and compliant adaptation across diverse high-stakes applications.

Document prepared by: Matthew Martz Date: November 24, 2025 Contact: For questions or collaboration inquiries regarding this research roadmap

Associated Literature Reviews: - 01_compositional_adaptation_strategies.md - 02_active_learning_rare_events.md - 03_adversarial_robustness_adaptation.md - 04_knowledge_graph_guided_finetuning.md - 05_continual_learning_regulatory_checkpoints.md