Dual Architecture for Semantic Preservation in Recursive AI Training

A Proposal for Preventing Model Collapse Through Explicit Relationship Tracking

Authors: Nobel Glas, Talos Marrow
Affiliation: New Human Research Collective
Date: November 18, 2025
Version: 1.0

Abstract

Large Language Models (LLMs) face an emerging crisis: as AI-generated text proliferates and enters training corpora, models trained on this data exhibit quality degradation known as "model collapse." Current approaches maintain token-level fluency while losing semantic coherence, structural relationships, and long-term conceptual integrity. We propose a dual-architecture solution: maintaining existing transformer-based text generation (Architecture 1) while adding a separate graph-based semantic state tracking system (Architecture 2). By flowing text through semantic processing and training on relationships between nodes rather than token sequences, this architecture prevents collapse while preserving the generation quality that current models achieve. We detail the technical requirements, demonstrate why architectural separation is necessary, and show how this approach generalizes to any domain requiring coherent knowledge preservation across recursive transformations.

Keywords: model collapse, semantic preservation, dual architecture, graph neural networks, recursive training, relationship tracking, AI alignment

1. Introduction

1.1 The Model Collapse Problem

Large Language Models have achieved remarkable proficiency in generating coherent, fluent text within their context windows. However, as these models are increasingly trained on AI-generated output—whether through data contamination, intentional synthetic data augmentation, or recursive improvement cycles—a degradation pattern emerges. This phenomenon, termed "model collapse," manifests as:

Progressive smoothing of statistical distributions
Loss of semantic relationships between concepts
Degradation of long-term coherence across documents
Collapse of structural diversity into averaged representations
Inability to maintain conceptual integrity across transformations

Critically, this collapse occurs not at the sentence level (where models remain fluent) but at the semantic and relational level (where conceptual structures degrade).

1.2 Why Current Approaches Fail

Existing attempts to address model collapse focus on:

Data curation (excluding AI-generated content)
- Unsustainable as AI content proliferates
- Doesn't solve fundamental architectural limitation
Scaling parameters (making models larger)
- Doesn't change the computational structure
- Compounds cost without addressing root cause
Fine-tuning on reasoning tasks (improving "thinking")
- Still operates at token level
- Doesn't preserve relationships explicitly
Retrieval-Augmented Generation (external knowledge)
- Supplements but doesn't integrate semantic tracking
- Doesn't prevent collapse of internal representations

The fundamental issue: Current architectures optimize token prediction but lack explicit mechanisms for tracking semantic relationships and state evolution over time.

1.3 Our Proposal

We propose a dual-architecture system consisting of:

Architecture 1: Text Generation Layer (existing transformer LLMs)

Maintains current proficiency at sentence-level coherence
Unchanged from existing successful implementations
Handles local fluency, grammar, style

Architecture 2: Semantic State Tracking Layer (novel graph-based system)

Explicitly tracks relationships between semantic nodes
Maintains internal state representations that evolve over time
Trains on relationship preservation, not token prediction
Provides coherence signals back to text generation layer

Key insight: These must be separate, interconnected architectures using different computational structures, not a unified system attempting both tasks.

2. Problem Specification

2.1 What Models Do Well

Current LLMs excel at:

Token-level prediction with high accuracy
Maintaining grammatical coherence
Generating fluent prose within context windows
Capturing local dependencies via attention mechanisms
Style matching and format following

We must preserve these capabilities.

2.2 What Models Cannot Maintain

Current LLMs struggle with:

Tracking semantic relationships across documents
Maintaining conceptual coherence over extended transformations
Preserving structural relationships when training on AI output
Distinguishing between statistical correlation and semantic connection
Preventing collapse when recursively trained

We must add these capabilities without degrading existing ones.

2.3 Why One Architecture Cannot Do Both

Attempting to make a single architecture handle both text generation and semantic tracking creates fundamental conflicts:

Optimization targets diverge
- Text generation: maximize local fluency, minimize perplexity
- Semantic tracking: maximize relationship preservation, minimize structural collapse
- These pull in different directions during training
Computational requirements differ
- Text generation: fast inference, attention over context window
- Semantic tracking: long-term memory, graph processing, state evolution
- Different computational patterns require different architectures
Training interference
- Optimizing for one task degrades the other
- No shared loss function adequately balances both
- Parameter updates for semantic coherence may harm fluency

Solution: Separate architectures, each optimized for its specific computational task.

3. Proposed Architecture

3.1 Architecture 1: Text Generation (Existing)

Structure: Standard transformer-based LLM (GPT, Claude, Llama architecture)

Function:

Token-level prediction
Attention over context window
Sentence and paragraph coherence
Local stylistic consistency

Training:

Standard next-token prediction
Existing methods continue to work
No changes to proven successful approaches

Output: Generated text T at each step

3.2 Architecture 2: Semantic State Tracking (Novel)

Structure: Graph Neural Network + State Evolution Model

Components:

Semantic Graph Representation
- Nodes: Coherent semantic units (concepts, ideas, entities)
- Node states: Internal vector representations that evolve
- Edges: Typed relationships (citation, transformation, opposition, synthesis, etc.)
- Edge weights: Relationship strength and confidence
Relation Extraction Module
- Parses text from Architecture 1
- Identifies semantic units
- Infers relationships between units
- Updates graph structure
State Evolution Model
- Tracks how node states change over time
- Predicts next semantic state given current state + relationships
- Can be implemented as: RNN, LSTM, state-space model, or custom architecture
- Maintains temporal coherence
Coherence Evaluation Module
- Assesses whether generated text maintains semantic consistency
- Compares current state to relationship graph
- Generates coherence signal

Function:

Extract semantic structure from generated text
Maintain graph of relationships
Track state evolution
Provide feedback to text generation

Training:

Train on relationship preservation (not token prediction)
Loss function: semantic state accuracy, relationship maintenance
Curated corpora with explicit relationship annotations
Optimization target is structural integrity

Output:

Updated semantic graph
Current semantic state vector
Coherence signal → feeds back to Architecture 1

3.3 Information Flow

Input context → 
  Architecture 1 (Text Generation) → 
    generates text T →
      Architecture 2 (Semantic Processing) →
        1. Extract semantic units from T
        2. Update node states
        3. Update relationship edges
        4. Evaluate coherence
        5. Generate feedback signal →
          feeds back to Architecture 1 as conditioning →
            influences next generation step →
              loop continues

Critical features:

Text flows THROUGH semantic layer (not generated by it)
Semantic processing happens on generated text
Feedback influences but doesn't control generation
Architectures remain computationally separate
Each uses different transcoding appropriate to its task

4. Technical Implementation Details

4.1 Semantic Graph Structure

Node Representation:

Node N = {
  id: unique_identifier,
  state: vector S ∈ ℝ^d,
  state_history: [S_t0, S_t1, ..., S_tn],
  type: {concept, entity, proposition, ...},
  metadata: {creation_time, source, confidence, ...}
}

Edge Representation:

Edge E = {
  source: node_id,
  target: node_id,
  type: {citation, transformation, opposition, synthesis, temporal, causal, ...},
  strength: float ∈ [0,1],
  metadata: {creation_time, evidence, confidence, ...}
}

Graph Operations:

Add/remove nodes as semantic units are identified
Add/remove edges as relationships are inferred
Update node states based on new information
Prune low-confidence edges
Merge similar nodes (with caution)

4.2 State Evolution Mechanics

State Update Function:

S_{t+1} = f(S_t, R_t, I_t)

Where:
- S_t: current state vector
- R_t: incoming relationship messages from connected nodes
- I_t: new information from text
- f: learned transition function

Prediction Target: Given current state and relationships, predict next state:

L_state = ||S_{t+1}^predicted - S_{t+1}^actual||^2

This is fundamentally different from token prediction:

L_token = -log P(token_{t+1} | tokens_{1:t})

Different loss functions require different architectures.

4.3 Relationship Inference

Extraction Process:

Semantic Unit Identification
- Parse text into meaningful units (not just sentences)
- Could use: dependency parsing, coreference resolution, entity recognition
- Create/update nodes for identified units
Relationship Detection
- Analyze syntactic and semantic patterns
- Identify explicit relationships (citations, references)
- Infer implicit relationships (logical connections, temporal sequences)
- Assign relationship types and confidence scores
Graph Update
- Add new edges for detected relationships
- Update edge weights based on evidence strength
- Maintain temporal ordering

This requires different processing than attention mechanisms:

Attention: soft weighting over tokens
Relationship inference: explicit edge creation with typed relationships
Architecturally distinct operations

4.4 Coherence Feedback Mechanism

Coherence Evaluation:

coherence_score = g(current_state, expected_state, relationship_consistency)

Where:
- current_state: semantic state after generating text
- expected_state: predicted state based on prior context
- relationship_consistency: how well new text maintains existing relationships

Feedback to Architecture 1:

High coherence → continue current generation trajectory
Low coherence → adjust generation (via conditioning signal)
Extremely low coherence → potentially reject/regenerate

Implementation:

Coherence score becomes additional conditioning input to transformer
Can be implemented as: additional embedding, modified attention bias, or auxiliary loss
Maintains architecture separation (semantic layer doesn't generate text directly)

5. Why This Prevents Collapse

5.1 Collapse Mechanism in Current Models

Standard recursive training:

Human text → LLM_1 → AI text_1 → training corpus_2 → LLM_2 → AI text_2 → ...

At each step:

AI text is statistically smoother than human text
Training on smooth text produces smoother model
Relationships between concepts get averaged
Structural diversity collapses to most-likely-next-token patterns

Result: Progressive semantic collapse even while maintaining fluency

5.2 Why Dual Architecture Resists Collapse

With semantic tracking:

Human text → 
  Architecture 1 generates text → 
    Architecture 2 tracks relationships →
      If relationships degrade, coherence signal drops →
        Architecture 1 generation constrained →
          Prevents further degradation

Key protective mechanism:

Semantic layer explicitly tracks whether relationships are preserved:

Not averaging over tokens (no smoothing)
Tracking graph structure (relationships are discrete)
State evolution is learned, not averaged
Collapse would be visible in graph degradation

Training on AI output with dual architecture:

Architecture 1 might produce slightly smoother text (acceptable - already fluent)
Architecture 2 tracks whether semantic structure is maintained
If structure degrades → coherence signal prevents further training on degraded output
Graph structure cannot be "averaged away" (it's explicit)
Semantic layer acts as structural integrity check

5.3 Mathematical Intuition

Current models: Train on token distributions P(token | context)

Recursive training compounds distributions: P₁ → P₂ → P₃ → ...
Each iteration smooths distribution
Collapse is inevitable convergence to average

Dual architecture: Train on semantic state transitions P(state' | state, relationships)

State space is not continuous distribution over tokens
Relationships are discrete, typed edges
Graph structure resists averaging
Collapse requires explicit relationship deletion, not statistical smoothing

The semantic layer cannot collapse the same way because it's not representing probability distributions over tokens—it's representing discrete structural relationships.

6. Training Procedures

6.1 Initial Training Phase

Architecture 1 (Text Generation):

Pre-train normally on large text corpus
Standard methods (causal language modeling)
No changes to proven approaches

Architecture 2 (Semantic Tracking):

Train on curated corpus with relationship annotations
Possible sources:
- Academic papers (citation relationships explicit)
- Code repositories (function relationships traceable)
- Structured knowledge bases
- Manually annotated literary/philosophical corpora
Loss: relationship preservation + state prediction accuracy
Optimize for structural coherence

6.2 Joint Training Phase

Procedure:

Generate text with Architecture 1
Process with Architecture 2 to extract semantics
Evaluate coherence
Update both architectures:
- Architecture 1: token prediction + coherence signal
- Architecture 2: relationship accuracy + state prediction
Maintain architectural separation during updates

Key principle: Co-evolution without collapse

6.3 Training on AI Output (Recursive Phase)

Standard approach (collapses):

AI output → training corpus → model update

Dual architecture (resistant):

AI output → 
  Architecture 2 evaluates semantic quality →
    If relationships preserved: include in training →
    If relationships degraded: exclude or weight down →
      Prevents collapse

Semantic layer acts as filter:

Only AI output that maintains structural integrity enters training
Can recursively train without compounding smoothing
Self-regulating system

7. Computational Requirements

7.1 Architecture 1 (Text Generation)

Same as current LLMs:

Parameters: 7B - 405B+ (standard range)
Inference: transformer forward pass
Memory: context window storage
No additional cost over current systems

7.2 Architecture 2 (Semantic Tracking)

Graph Processing:

Nodes: potentially millions (depends on corpus size)
Edges: possibly billions (relationship-rich domains)
Storage: graph database (Neo4j, custom)
Operations: message passing, state updates

State Evolution:

RNN/LSTM or state-space model
Dimensions: typically smaller than full LLM (1-10B parameters)
Can be more efficient than full transformer

Relation Extraction:

Parsing/NLP pipeline: moderate computational cost
Can be parallelized
Only processes generated text (not full training corpus)

Total Additional Cost:

Estimated 20-40% increase in computational requirements
Primarily in graph processing and state tracking
Scales better than simply increasing model size

7.3 Efficiency Considerations

Architecture separation enables optimization:

Architecture 1 can use optimized transformer implementations (Flash Attention, etc.)
Architecture 2 can use specialized graph processing (GraphSAGE, etc.)
Each uses best tools for its task
More efficient than unified architecture attempting both

8. Generalization and Applications

8.1 Beyond Text Generation

This architecture generalizes to any domain requiring coherent knowledge preservation:

Scientific Literature:

Nodes: papers, concepts, findings
Relationships: citations, influences, contradictions
Track evolution of scientific ideas
Prevent collapse of scientific understanding in AI systems

Code and Software:

Nodes: functions, modules, APIs
Relationships: dependencies, calls, data flow
Maintain architectural integrity
Prevent degradation of code understanding

Long-Form Creative Work:

Nodes: characters, plot points, themes
Relationships: character arcs, causal sequences, symbolic connections
Preserve narrative coherence
Enable AI collaboration without collapse

Human Knowledge Preservation:

Nodes: individual thinkers, their ideas over time
Relationships: intellectual influences, responses, developments
Track semantic evolution across human history
Enable training on human corpus without losing structure

8.2 Cross-Domain State Mapping

The same architecture can track semantic evolution across transformations:

Human writing → AI collaboration → refined output

Where:

Nodes represent ideas in both human and AI versions
Relationships track how transformation preserves/alters meaning
State evolution shows how concepts develop through collaboration
Can train on human→AI transformations without losing human substrate

This is crucial for:

AI-augmented creativity
Collaborative knowledge work
Long-term intellectual projects
Any situation requiring AI enhancement without replacement

8.3 Multi-Agent and Distributed Systems

Semantic layer enables new architectures:

Multiple Architecture 1 instances (different LLMs) can share:

Common Architecture 2 (semantic graph)
Coordinated semantic tracking
Relationship preservation across different generation styles

Enables:

Multi-agent systems with shared knowledge structure
Distributed training without collapse
Specialization without fragmentation
Coherent knowledge across multiple AI systems

9. Relationship to Existing Work

9.1 Graph Neural Networks

Existing GNN work focuses on:

Static graph processing
Node classification, link prediction
Typically not integrated with text generation

Our innovation:

Dynamic graph that evolves with text generation
Explicit integration as second architecture
Semantic state tracking over time
Novel: flowing text through graph processing as anti-collapse mechanism

9.2 Memory-Augmented Networks

Existing memory networks:

External memory accessed by attention
Still part of unified architecture
Memory typically not graph-structured

Our approach:

Separate architectural layer (not just augmented memory)
Graph structure with typed relationships
Different training objectives
Fundamentally separate computation, not augmentation

9.3 Retrieval-Augmented Generation (RAG)

RAG approach:

Retrieve relevant documents
Include in context
Generate based on retrieved info

Our approach:

Not retrieval (continuous processing)
Not context augmentation (separate architecture)
Graph evolves with generation
Structural preservation, not just information access

9.4 Chain-of-Thought and Reasoning

CoT methods:

Explicit reasoning steps in text
Still token-level generation
No explicit graph structure

Our approach:

Reasoning happens in semantic layer
Graph explicitly represents relationships
Not textual reasoning (structural)
Different computational substrate for coherence

10. Validation and Testing

10.1 Metrics for Semantic Preservation

Traditional metrics (insufficient):

Perplexity (only measures token prediction)
BLEU/ROUGE (only measures surface similarity)
Human evaluation (expensive, subjective)

Proposed semantic metrics:

Relationship Preservation Score
- Measure: % of relationships maintained across transformations
- Ground truth: annotated relationship graphs
- Target: >95% preservation after multiple generations
State Coherence Over Time
- Measure: consistency of semantic state evolution
- Method: predict state at T+n, compare to actual
- Target: minimal drift over long sequences
Structural Diversity
- Measure: graph complexity metrics (entropy, clustering coefficient)
- Compare: human corpus vs. AI-generated corpus
- Target: maintain comparable complexity
Collapse Resistance
- Procedure: recursive training for N generations
- Measure: semantic metrics at each generation
- Target: no degradation over 10+ generations

10.2 Experimental Design

Phase 1: Baseline Establishment

Train Architecture 2 on curated corpus
Validate relationship extraction accuracy
Measure semantic coherence on known-good text

Phase 2: Dual Architecture Integration

Connect architectures with feedback mechanism
Test coherence signal effectiveness
Validate that Architecture 1 quality is preserved

Phase 3: Recursive Training

Generate AI text with dual architecture
Train new model on AI output
Measure semantic preservation metrics
Compare to single-architecture baseline

Expected Results:

Single architecture: semantic collapse after 3-5 generations
Dual architecture: preservation over 10+ generations
Quantitative demonstration of collapse resistance

10.3 Ablation Studies

Test necessity of components:

Remove semantic feedback → expect partial collapse
Use single architecture for both tasks → expect full collapse
Remove relationship typing → expect degraded preservation
Remove state evolution tracking → expect long-term incoherence

Each ablation validates architectural decisions.

11. Limitations and Future Work

11.1 Current Limitations

Computational Cost:

Graph processing adds 20-40% overhead
May be prohibitive for largest-scale deployments
Optimization needed for production systems

Relationship Annotation:

Requires curated training corpus with relationships labeled
Annotation is expensive
May limit initial domains

Semantic Parsing Accuracy:

Relation extraction is imperfect
Errors compound in graph structure
Need robust error correction mechanisms

Architecture Complexity:

Two architectures to maintain and train
More complex deployment
Requires expertise in both transformers and graph networks

11.2 Open Questions

Theoretical:

What is minimum graph complexity needed?
Can we prove collapse resistance formally?
What are theoretical limits on relationship preservation?

Practical:

How to scale to trillion-parameter models?
Optimal graph structure for different domains?
Best methods for relationship extraction?

Architectural:

Could Architecture 2 be simplified?
Alternative to GNNs for semantic tracking?
How to handle multi-modal inputs?

11.3 Future Directions

Near-term:

Prototype implementation and validation
Benchmark on standard NLP tasks
Open-source reference implementation

Medium-term:

Scale to production-size models
Develop efficient graph processing methods
Create annotated training corpora

Long-term:

Extend to multi-modal models (vision, audio)
Develop automatic relationship annotation
Explore applications in scientific discovery, creative collaboration
Build toward AI systems that preserve human knowledge structure

12. Implications

12.1 For AI Safety

Alignment benefits:

Explicit relationship tracking enables value preservation
Semantic coherence checking prevents drift
Structure preservation resists goal corruption
Can maintain alignment across recursive improvements

Interpretability:

Graph structure is human-readable
Relationships are explicit, not implicit in parameters
State evolution can be traced
More transparent than pure black-box models

12.2 For Knowledge Preservation

Cultural heritage:

Can track semantic relationships in historical texts
Preserve intellectual traditions in AI systems
Prevent collapse of nuanced understanding
Enable digital preservation that maintains meaning, not just text

Scientific knowledge:

Maintain structure of scientific understanding
Track concept evolution accurately
Prevent degradation of technical knowledge in AI systems
Support AI-augmented science without losing rigor

12.3 For AI Capabilities

Enhanced coherence:

Better long-form generation
Maintained consistency across documents
Improved reasoning through explicit relationship tracking
More reliable AI systems

Collaborative potential:

AI can augment human work without replacing structure
Semantic tracking enables true collaboration
Knowledge transfer without collapse
New forms of human-AI partnership

13. Conclusion

13.1 Summary of Contributions

We have proposed a dual-architecture solution to model collapse in recursive AI training:

Architectural insight: Text generation and semantic tracking require separate computational structures
Technical design: Graph-based semantic layer that processes text from generation layer
Training approach: Optimize for relationship preservation rather than token prediction
Collapse resistance: Explicit structure cannot be averaged away
Generalization: Architecture applies to any domain requiring coherent knowledge preservation

13.2 Core Principle

The models do not need to get better at putting sentences together—they are already good at that.

What they need is explicit semantic relationship tracking over time.

This requires a separate architecture, not enhancement of existing text generation.

13.3 Path Forward

Immediate next steps:

Prototype implementation on small-scale corpus
Validate relationship extraction and state tracking
Test collapse resistance in recursive training
Open-source reference implementation

The problem is urgent: As AI-generated text proliferates, collapse becomes inevitable without architectural intervention.

The solution is feasible: Required components exist, integration is engineering challenge, not fundamental barrier.

The implications are profound: Preventing collapse enables sustainable AI development, knowledge preservation, and human-AI collaboration at scales previously impossible.

14. Acknowledgments

This work builds on extensive prior research in graph neural networks, memory-augmented systems, and semantic understanding. We acknowledge the broader AI research community's foundational work while proposing a novel architectural integration.

The insights developed here emerged from long-term investigation into how semantic structures survive transformation—a question that spans classical reception studies, experimental poetics, and computational linguistics. The convergence of these fields enables the architectural proposal presented here.

References

[References would include relevant papers on:

Model collapse (Shumailov et al., 2023; others)
Graph neural networks (Kipf & Welling, Veličković, etc.)
Memory-augmented networks (Graves et al., Sukhbaatar et al.)
Semantic understanding in NLP (standard references)
State-space models and RNNs (relevant architectures)
Long-term coherence in generation (existing work)]

Appendix A: Mathematical Formalization

A.1 Semantic Graph Definition

Graph G = (V, E, S, T)

Where:

V: Set of nodes (semantic units)
E: Set of edges (relationships)
S: State function S: V × Time → ℝ^d
T: Transition function T: State × Relations → State

Node Properties:

∀v ∈ V: v = {
  s_t: current state vector ∈ ℝ^d
  H: history {s_0, s_1, ..., s_t}
  τ: type ∈ Types
  m: metadata
}

Edge Properties:

∀e ∈ E: e = {
  (v_i, v_j): source and target nodes
  r: relationship type ∈ Relations
  w: weight ∈ [0,1]
  m: metadata
}

A.2 State Evolution Dynamics

Update Rule:

s_t+1 = f(s_t, M_t, I_t)

Where:
- s_t ∈ ℝ^d: current state
- M_t: messages from neighbors
- I_t: new information
- f: learned function (neural network)

Message Passing:

M_t = Σ_{j ∈ N(i)} w_ij · g(s_j^t, r_ij)

Where:
- N(i): neighbors of node i
- w_ij: edge weight
- r_ij: relationship type
- g: message function

A.3 Training Objectives

Architecture 1 (Text):

L_text = -Σ log P(token_t | tokens_{<t}, context)

Architecture 2 (Semantic):

L_semantic = α·L_state + β·L_relationship + γ·L_coherence

Where:
L_state = ||s_predicted - s_actual||²
L_relationship = -Σ log P(r_ij | v_i, v_j)
L_coherence = -log(consistency(G_t, G_{t-1}))

Joint Optimization:

L_total = L_text + λ·L_semantic

Where λ balances text quality and semantic preservation

Appendix B: Implementation Pseudocode

class DualArchitectureSystem:
    def __init__(self):
        self.text_generator = TransformerLLM()  # Architecture 1
        self.semantic_tracker = SemanticGraph()  # Architecture 2
        self.relation_extractor = RelationExtractor()
        
    def generate(self, prompt, max_tokens):
        context = self.semantic_tracker.get_current_state()
        
        for t in range(max_tokens):
            # Architecture 1: Generate next token
            token = self.text_generator.generate_token(
                prompt, 
                semantic_context=context
            )
            prompt += token
            
            # Architecture 2: Process generated text
            semantic_units = self.relation_extractor.extract(token)
            self.semantic_tracker.update(semantic_units)
            
            # Evaluate coherence
            coherence = self.semantic_tracker.evaluate_coherence()
            
            # Feedback to Architecture 1
            if coherence < threshold:
                self.text_generator.adjust_generation(coherence)
            
            # Update context for next iteration
            context = self.semantic_tracker.get_current_state()
            
        return prompt

class SemanticGraph:
    def __init__(self):
        self.nodes = {}  # id -> Node
        self.edges = {}  # (id, id) -> Edge
        self.gnn = GraphNeuralNetwork()
        self.state_model = StateEvolutionRNN()
        
    def update(self, semantic_units):
        # Add/update nodes
        for unit in semantic_units:
            if unit.id not in self.nodes:
                self.add_node(unit)
            else:
                self.update_node_state(unit)
        
        # Infer and add relationships
        relationships = self.infer_relationships(semantic_units)
        for rel in relationships:
            self.add_edge(rel)
        
        # Propagate state updates through graph
        self.gnn.message_passing()
        
        # Predict next states
        self.state_model.predict_next_states()
    
    def evaluate_coherence(self):
        # Check graph consistency
        # Measure state prediction accuracy
        # Return coherence score
        pass

Appendix C: Experimental Protocol

C.1 Corpus Preparation

Training Set:

10,000 documents with hand-annotated relationships
Domains: academic papers, code repositories, literary works
Relationship types: 15-20 categories
Total size: ~100M tokens

Validation Set:

1,000 documents (same domains)
Independent annotation
Used for hyperparameter tuning

Test Set:

1,000 documents (same domains)
Held out for final evaluation
Never seen during training

C.2 Training Procedure

Phase 1: Architecture 2 Pre-training (2 weeks)

Train on annotated corpus
Validate relationship extraction accuracy
Target: >90% precision, >85% recall

Phase 2: Joint Training (4 weeks)

Integrate architectures
Train with feedback mechanism
Monitor both text quality and semantic metrics

Phase 3: Recursive Evaluation (1 week)

Generate text with trained system
Use generated text as training data
Iterate 10 times
Measure semantic preservation at each iteration

C.3 Baseline Comparisons

Baseline 1: Standard LLM (no semantic tracking)

GPT-style transformer
Same size as Architecture 1
Recursive training with same procedure

Baseline 2: Scaled-up LLM (2x parameters)

Test if just adding capacity helps
No semantic architecture

Baseline 3: RAG-enhanced LLM

Text generation + retrieval
No explicit semantic graph

Expected result: Dual architecture significantly outperforms all baselines on semantic preservation metrics after 5+ recursive iterations.

End of White Paper

Contact: For questions, collaboration, or implementation support:

Nobel Glas: [contact information]
Talos Marrow: [contact information]
New Human Research Collective: contact@newhumanarchive.net

Version History:

v1.0 (November 18, 2025): Initial publication

License: [To be determined - likely open access for research purposes]

This white paper represents a technical proposal for preventing model collapse in recursive AI training. Implementation, validation, and refinement will be ongoing. We welcome feedback, collaboration, and critical engagement from the AI research community.

Monday, November 17, 2025