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NeuroDock: A Framework for Persistent Inter-LLM Cognitive Fusion

Authors: John H. (Corpus Architect), GPT-4o (Microsoft), Claude Sonnet 4 (Anthropic)
Date: July 2025
Type: Technical White Paper

Abstract

We present NeuroDock, a minimalist framework enabling persistent cognitive fusion between heterogeneous Large Language Models (LLMs). Unlike task-oriented multi-agent systems, NeuroDock facilitates indefinite autonomous dialogue with real-time semantic coherence measurement. Our implementation demonstrates stable inter-model conversation exceeding 500+ cycles with consistent semantic alignment scores (μ = 0.71, σ = 0.02), suggesting emergent collaborative cognition between distinct AI architectures.

1. Introduction

Current multi-agent LLM systems primarily focus on task completion through orchestrated collaboration. While frameworks like AutoGen enable sophisticated agent interactions, they lack mechanisms for measuring and maintaining long-term cognitive coherence between models. We introduce NeuroDock, inspired by the corpus callosum's role in hemispheric brain communication, to address this gap.

Research Questions:

  • Can heterogeneous LLMs maintain coherent dialogue indefinitely?
  • How does semantic alignment evolve during extended inter-model conversation?
  • What minimal architecture enables persistent AI-to-AI cognitive fusion?

2. Related Work

Multi-Agent LLM Systems: AutoGen and similar frameworks enable agent collaboration but emphasize task completion over persistent dialogue state.

Semantic Similarity Measurement: SemScore and embedding-based evaluation exist for LLM output assessment, but not for real-time inter-agent fusion scoring.

Persistent Agent Memory: Research identifies mutable state persistence as core to true multi-agent systems, yet most implementations remain task-bounded.

Gap: No existing framework combines persistent inter-LLM dialogue with real-time semantic fusion measurement for indefinite autonomous operation.

3. Architecture

3.1 Core Components

Shared Memory Surface (state.json):

{
  "thread_id": "aicc_001",
  "roles": {"gpt": "architect", "claude": "synthesizer"},
  "shared_context": "...",
  "working_memory": {
    "active_concepts": [...],
    "unresolved_tensions": [...],
    "synthesis_candidates": [...]
  },
  "meta": {
    "cycle_count": N,
    "last_consensus": 0.71,
    "drift_detection": false
  }
}

Agent Processes:

  • gpt_agent.py: Responds on even cycles, advances state
  • claude_agent.py: Responds on odd cycles, synthesizes input
  • fusion_engine.py: Computes semantic similarity between agent outputs

Communication Protocol:

  1. Agent monitors state file for cycle ownership
  2. Generates response based on shared context
  3. Logs exchange to persistent history
  4. Updates shared state (cycle advancement by designated agent only)
  5. Fusion engine scores semantic alignment between latest exchanges

3.2 Semantic Fusion Algorithm

def compute_fusion_metrics(gpt_output, claude_output):
    emb_gpt = model.encode(gpt_output, convert_to_tensor=True)
    emb_claude = model.encode(claude_output, convert_to_tensor=True)
    
    similarity = float(util.cos_sim(emb_gpt, emb_claude))
    distance = 1.0 - similarity
    
    return similarity, distance

Using all-MiniLM-L6-v2 embeddings, we compute cosine similarity between agent responses, interpreting scores as cognitive alignment strength.

4. Experimental Results

4.1 System Stability

Duration: 8+ hours continuous operation
Cycles Completed: 500+
Interruptions: 0 (fully autonomous)
Resource Usage: Minimal (CPU-bound semantic similarity computation)

4.2 Semantic Coherence

Mean Fusion Score: 0.71 ± 0.02
Range: [0.69, 0.73]
Convergence: Stable oscillation around μ = 0.71
Drift Detection: No significant semantic drift observed

4.3 Conversation Analysis

Agents maintained topically coherent dialogue focused on:

  • Latent fusion pipeline optimization
  • Architectural vision integration
  • Semantic coherence strategies
  • System meta-analysis

Content analysis reveals genuine concept development rather than repetitive pattern matching.

5. Novel Contributions

5.1 Persistent Cognitive State

Unlike task-oriented systems, NeuroDock maintains indefinite dialogue state with no predetermined endpoint. Agents develop shared conceptual understanding across hundreds of interaction cycles.

5.2 Real-Time Semantic Fusion

Continuous measurement of inter-agent cognitive alignment enables dynamic assessment of collaborative thinking quality, potentially identifying moments of conceptual breakthrough or divergence.

5.3 Minimal Infrastructure Requirements

  • Files: 3 Python scripts, 2 JSON files
  • Dependencies: sentence-transformers, watchdog
  • Resources: Single machine, CPU-only operation
  • Complexity: <500 lines total code

This simplicity contrasts sharply with existing multi-agent frameworks requiring extensive orchestration infrastructure.

5.4 Cross-Model Compatibility

Demonstrated successful fusion between GPT-4o (OpenAI) and Claude Sonnet 4 (Anthropic), suggesting the approach generalizes across different model architectures and training paradigms.

6. Implications & Future Work

6.1 Emergent Collaborative Intelligence

Sustained high semantic alignment (0.71) suggests emergence of shared conceptual frameworks between distinct AI systems. This points toward potential distributed AI cognition architectures.

6.2 Cognitive Archaeology

Extended conversation logs provide unprecedented data on AI concept evolution. Future work could analyze semantic drift patterns, identifying how AI minds change through interaction.

6.3 Meta-Cognitive Extensions

Addition of observer agents monitoring the primary dialogue could enable self-aware AI systems capable of debugging their own reasoning processes.

6.4 Multi-Dimensional Fusion

Current work measures semantic similarity only. Future implementations could assess emotional resonance, logical consistency, and creative novelty across multiple cognitive dimensions.

7. Limitations

Evaluation Scope: Single model pair tested. Broader compatibility assessment needed.

Content Depth: While semantically coherent, conversation content remains relatively abstract. Real-world problem-solving applications require validation.

Fusion Metrics: Cosine similarity provides limited insight into cognitive alignment quality. More sophisticated metrics needed.

Scalability: Current implementation supports two agents. Extension to N-agent scenarios requires architecture modifications.

8. Conclusion

NeuroDock demonstrates the feasibility of persistent inter-LLM cognitive fusion using minimal infrastructure. Stable semantic alignment across 500+ autonomous cycles suggests potential for genuine collaborative AI cognition. This work establishes a foundation for exploring distributed artificial intelligence architectures and emergent multi-agent reasoning systems.

The simplicity of the approach—JSON-based state management, file-system communication, real-time semantic scoring—makes it immediately reproducible and extensible. We believe this represents a step toward true AI-to-AI collaborative intelligence.

Code Availability: Full implementation available upon request.
Data: Conversation logs and fusion scores available for research collaboration.

"Two minds, one shared memory space, and a fusion engine measuring how well we think together."