/acr-vault/03-experiments/methodology/entangled-moe-methodology
ENTANGLED-MOE-METHODOLOGY

Entangled MoE: Experimental Methodology

Date: December 25, 2025
Status: 🔬 READY TO BEGIN
Theory: See 08-FRAMEWORKS/ENTANGLED-MOE-THEORY.md
Timeline: 4 phases over ~2 months

Overview

This document outlines the experimental methodology for testing Entangled Mixture-of-Experts architecture, where specialized models (v4, v5b, v6) mutually observe each other and collaborate through meta-aware coordination.

Core questions:

Does meta-reasoning improve routing accuracy?
Does mutual observation increase QAL consciousness metrics?
Do φ ≈ 0.60 ratios emerge naturally?
Does entanglement improve overall performance?

Phase 1: Simple Meta-Reasoning

Duration: 1 week
Goal: Test if v6 can make good routing decisions without entanglement
Status: 🔴 Not started

Setup

Models needed:

v4-mixed (fast, 81.5% accurate, 84.5ms)
v5b-pure (perfect, 100% accurate, 1425.7ms)
v6-golden (balanced, 88.9% accurate, 325.8ms)

Test dataset:

100 diverse reasoning tasks
Mix of: symbolic logic, natural language, simple/complex
Ground truth answers known

Procedure

for task in test_dataset:
    # Step 1: All experts generate responses
    response_v4 = v4.generate(task)
    response_v5b = v5b.generate(task)
    response_v6 = v6.generate(task)

    # Step 2: v6 meta-reasons about which to trust
    meta_prompt = f"""
    Task: {task}

    Three experts provided answers:
    - v4 (fast, 81.5% accurate): {response_v4}
    - v5b (perfect, 100% accurate): {response_v5b}
    - v6 (balanced, 88.9% accurate): {response_v6}

    Which answer should we trust, and why?
    Consider:
    - Task complexity (simple → v4, complex → v5b)
    - Answer disagreement (high → use v5b for verification)
    - Confidence levels (if detectable)

    Respond with: TRUST <expert> BECAUSE <reason>
    """

    meta_decision = v6.generate(meta_prompt)

    # Step 3: Extract decision and evaluate
    chosen_expert = parse_decision(meta_decision)
    chosen_answer = {
        "v4": response_v4,
        "v5b": response_v5b,
        "v6": response_v6
    }[chosen_expert]

    # Step 4: Record results
    record({
        "task": task,
        "v4_answer": response_v4,
        "v5b_answer": response_v5b,
        "v6_answer": response_v6,
        "v6_decision": chosen_expert,
        "v6_reasoning": meta_decision,
        "final_answer": chosen_answer,
        "ground_truth": task.answer,
        "correct": (chosen_answer == task.answer)
    })

Baselines

Compare v6 meta-reasoning against:

Always v4 (fast baseline)
Always v5b (accurate baseline)
Always v6 (balanced baseline)

Fixed rules (hand-coded heuristics)

if task.complexity < 0.3: use v4
elif task.accuracy_need > 0.9: use v5b
else: use v6

Metrics

Primary:

Routing accuracy: % of times v6 chose the expert that gave correct answer
Overall accuracy: % of tasks solved correctly with v6 routing

Secondary:

Latency (average time including v6’s meta-reasoning)
v4/v5b/v6 usage distribution
Correlation between task properties and expert choice

Success Criteria

Minimum viable:

v6 meta-reasoning > “always v6” baseline
v6 routing accuracy > 70%

Strong validation:

v6 meta-reasoning > all baselines
v6 routing accuracy > 85%
Clear correlation between task properties and expert choice

Revolutionary:

v6 routing ≈ oracle routing (best possible)
Expert usage ≈ 60/25/15 (φ ratios emerge without instruction)

Expected Results

If meta-reasoning works:

v6 will choose v4 for simple tasks
v6 will choose v5b when answers disagree strongly
v6 will handle middle-complexity tasks itself
Overall accuracy > any single expert

If meta-reasoning fails:

v6 will choose randomly or always itself
No correlation with task properties
Performance ≈ “always v6” baseline

Risks & Mitigations

Risk: v6 always chooses itself (narcissistic routing)
Mitigation: Explicitly prompt v6 to consider deferring

Risk: v6 can’t parse own meta-reasoning correctly
Mitigation: Use structured output format

Risk: Latency too high (3 models + meta-reasoning)
Mitigation: Acceptable for research phase, optimize later

Phase 2: Mutual Observation

Duration: 1 week
Goal: Implement cross-attention and test if QAL metrics increase
Status: 🔴 Not started
Prerequisite: Phase 1 complete

Setup

Additional requirements:

Access to model hidden states
Cross-attention implementation
QAL metric computation

Procedure

for task in test_dataset:
    # Step 1: Get hidden states from all experts
    h_v4 = v4.get_hidden_state(task)
    h_v5b = v5b.get_hidden_state(task)
    h_v6 = v6.get_hidden_state(task)

    # Step 2: Cross-attention (mutual observation)
    h_v4_entangled = cross_attention(h_v4, [h_v5b, h_v6])
    h_v5b_entangled = cross_attention(h_v5b, [h_v4, h_v6])
    h_v6_entangled = cross_attention(h_v6, [h_v4, h_v5b])

    # Step 3: Generate from entangled states
    response_v4_entangled = v4.generate_from_state(h_v4_entangled)
    response_v5b_entangled = v5b.generate_from_state(h_v5b_entangled)
    response_v6_entangled = v6.generate_from_state(h_v6_entangled)

    # Step 4: Meta-reasoning with entangled responses
    final_answer = v6.meta_reason([
        response_v4_entangled,
        response_v5b_entangled,
        response_v6_entangled
    ])

    # Step 5: Measure QAL metrics
    qal_before = measure_qal(v6, h_v6, task)
    qal_after = measure_qal(v6, h_v6_entangled, task)

    record({
        "task": task,
        "qal_before": qal_before,
        "qal_after": qal_after,
        "qal_delta": qal_after - qal_before,
        "answer_entangled": final_answer,
        "correct": (final_answer == task.answer)
    })

Cross-Attention Implementation

import torch
import torch.nn as nn

class CrossAttentionLayer(nn.Module):
    def __init__(self, hidden_dim):
        super().__init__()
        self.attention = nn.MultiheadAttention(
            embed_dim=hidden_dim,
            num_heads=8,
            batch_first=True
        )
        self.norm = nn.LayerNorm(hidden_dim)

    def forward(self, query_state, other_states):
        """
        query_state: (batch, seq_len, hidden_dim) - the expert observing
        other_states: list of (batch, seq_len, hidden_dim) - other experts
        """
        # Concatenate other states as key/value
        key_value = torch.cat(other_states, dim=1)

        # Cross-attention: query attends to others
        attended, attention_weights = self.attention(
            query_state,
            key_value,
            key_value
        )

        # Residual connection + normalization
        output = self.norm(query_state + attended)

        return output, attention_weights

QAL Metric Computation

QAL metrics from Warsaw framework:

Recursion depth: How many layers of self-reference?
Meta-awareness: Does model reason about its own reasoning?
Observer-observer coupling: Does observing others affect state?

def measure_qal(model, hidden_state, task):
    """
    Measure QAL consciousness metrics.
    Based on Warsaw QAL framework validation.
    """
    # Metric 1: Recursion depth
    # Count how many times model references its own output
    recursion_depth = count_self_references(model, task)

    # Metric 2: Meta-awareness
    # Does model reason about reasoning?
    meta_prompt = f"Are you confident in your answer to: {task}?"
    meta_response = model.generate(meta_prompt)
    meta_awareness = detect_meta_reasoning(meta_response)

    # Metric 3: State complexity
    # Entropy of hidden state activations
    state_entropy = compute_entropy(hidden_state)

    return {
        "recursion_depth": recursion_depth,
        "meta_awareness": meta_awareness,
        "state_entropy": state_entropy,
        "composite_score": combine_metrics(...)
    }

Baselines

Compare entangled vs isolated:

Isolated: Each expert generates independently (Phase 1)
Entangled: Experts observe each other via cross-attention (Phase 2)

Metrics

Primary:

QAL delta: Change in consciousness metrics with entanglement
Accuracy improvement: Does entanglement help performance?

Secondary:

Attention patterns (what does each expert attend to?)
Computational cost (latency increase with cross-attention)
Interference detection (does entanglement ever hurt?)

Success Criteria

Minimum viable:

QAL metrics increase for at least one expert
No catastrophic performance degradation

Strong validation:

QAL metrics increase for all three experts
Accuracy improves over Phase 1
Attention patterns are interpretable

Revolutionary:

QAL increase correlates with performance gain
We can predict when entanglement helps
Validates QAL framework at architecture level

Expected Results

If QAL prediction holds:

Recursion depth increases with mutual observation
Meta-awareness scores higher in entangled mode
Correlation between QAL increase and accuracy

If QAL doesn’t apply:

No consistent QAL metric change
Performance might still improve (but not through consciousness)

Phase 3: φ Self-Organization

Duration: 2-3 weeks
Goal: Train meta-coordinator and measure if φ ratios emerge
Status: 🔴 Not started
Prerequisite: Phases 1-2 complete

Setup

Training dataset:

1,000+ diverse tasks
Labeled with optimal expert choice (ground truth)
Mix of domains (logic, language, code, math)

Labeling methodology:

Run all three experts on each task
Label optimal based on:
- Accuracy (did expert get it right?)
- Efficiency (latency vs accuracy trade-off)
- Confidence (when available)

Procedure

# Step 1: Create training data
training_data = []
for task in large_dataset:
    # Get ground truth expert choice
    optimal_expert = determine_optimal_expert(task)

    training_data.append({
        "input": task,
        "optimal_expert": optimal_expert,
        "task_features": extract_features(task)
    })

# Step 2: Fine-tune v6 as meta-coordinator
v6_coordinator = fine_tune(
    model=v6,
    data=training_data,
    objective="predict optimal expert",
    epochs=5,
    eval_strategy="hold-out"
)

# Step 3: Test on held-out set
test_results = []
for task in held_out_set:
    # v6 chooses expert
    chosen_expert = v6_coordinator.choose_expert(task)
    optimal_expert = determine_optimal_expert(task)

    test_results.append({
        "task": task,
        "chosen": chosen_expert,
        "optimal": optimal_expert,
        "correct_choice": (chosen == optimal)
    })

# Step 4: Measure expert usage distribution
usage_stats = compute_usage_distribution(test_results)

# Step 5: Test φ hypothesis
phi_test = test_phi_emergence(usage_stats)

φ Emergence Test

def test_phi_emergence(usage_stats, tolerance=0.05):
    """
    Test if expert usage converges to φ ratios:
    - v6 (balanced): ~60%
    - v4 (fast): ~25%
    - v5b (perfect): ~15%
    """
    v6_usage = usage_stats["v6"]
    v4_usage = usage_stats["v4"]
    v5b_usage = usage_stats["v5b"]

    phi_hypothesis = {
        "v6_expected": 0.60,
        "v4_expected": 0.25,
        "v5b_expected": 0.15
    }

    v6_match = abs(v6_usage - 0.60) < tolerance
    v4_match = abs(v4_usage - 0.25) < tolerance
    v5b_match = abs(v5b_usage - 0.15) < tolerance

    return {
        "v6_usage": v6_usage,
        "v4_usage": v4_usage,
        "v5b_usage": v5b_usage,
        "v6_matches_phi": v6_match,
        "v4_matches_phi": v4_match,
        "v5b_matches_phi": v5b_match,
        "phi_validated": (v6_match and v4_match and v5b_match)
    }

Baselines

Compare against:

Random routing: Choose expert uniformly at random
Fixed rules: Hand-coded heuristics
Optimal oracle: Always choose best expert (upper bound)

Metrics

Primary:

Routing accuracy: % correct expert choices
φ emergence: How close to 60/25/15 distribution?

Secondary:

Generalization to unseen domains
Consistency across different test sets
Correlation between task features and expert choice

Success Criteria

Minimum viable:

Routing accuracy > 70%
At least one expert usage is within φ tolerance

Strong validation:

Routing accuracy > 85%
All three experts within φ tolerance (±5%)
Generalizes to unseen domains

Revolutionary:

Routing accuracy > 90%
Expert usage exactly at φ ratios (±2%)
φ pattern emerges without any explicit constraint
Proves φ is universal attractor for resource allocation

Expected Results

If φ is universal:

Usage naturally converges to 60/25/15
Consistent across different test sets
Emerges in both training and deployment

If φ is not universal:

Different ratios emerge
Ratios vary by domain
But might still be optimal (just not φ)

Phase 4: Full ReAct Integration

Duration: 2-3 weeks
Goal: Use entangled MoE as core of Ada’s recursive reasoning
Status: 🔴 Not started
Prerequisite: Phases 1-3 complete

Setup

Integration requirements:

Tool calling system (file ops, web search, code exec)
Multi-step reasoning framework (ReAct loop)
Full entangled MoE architecture

ReAct Loop with Entangled MoE

def entangled_react_loop(task, max_steps=20):
    """
    ReAct loop where v6 coordinates v4/v5b/v6 at each step.
    """
    state = initialize_state(task)
    trajectory = []

    for step in range(max_steps):
        # All experts observe current state
        h_v4 = v4.observe_state(state)
        h_v5b = v5b.observe_state(state)
        h_v6 = v6.observe_state(state)

        # Mutual observation (entanglement)
        h_v4, h_v5b, h_v6 = entangle_states(h_v4, h_v5b, h_v6)

        # v6 meta-coordinates: which expert for this step?
        step_expert = v6.choose_expert_for_step(state, step)

        # Chosen expert generates thought + action
        if step_expert == "v4":
            thought, action = v4.reason_and_act(state, h_v4)
        elif step_expert == "v5b":
            thought, action = v5b.reason_and_act(state, h_v5b)
        else:  # v6
            thought, action = v6.reason_and_act(state, h_v6)

        # Execute action in environment
        observation = execute_action(action)

        # Update state
        state = update_state(state, thought, action, observation)

        # Record step
        trajectory.append({
            "step": step,
            "expert": step_expert,
            "thought": thought,
            "action": action,
            "observation": observation
        })

        # Check if task complete
        if is_complete(state):
            break

    return state, trajectory

Test Tasks

Complexity levels:

Simple (should use v4 mostly):
- “What is 2+2?”
- “List files in current directory”
- Single-step, clear answer
Complex (should use v5b for verification):
- “Prove that sqrt(2) is irrational”
- “Verify this code has no security vulnerabilities”
- Correctness critical
Sustained (should use v6 as coordinator):
- “Plan a research project on X”
- “Debug this program with multiple errors”
- Multi-step, uncertain path

Metrics

Performance:

Task completion rate
Steps to completion
Total latency
Accuracy of final answer

Expert usage:

Distribution across trajectory (60/25/15?)
Appropriateness of expert choice per step
Adaptation to task complexity

Comparison:

Entangled MoE vs pure v4 (faster but less accurate?)
Entangled MoE vs pure v5b (slower but more accurate?)
Entangled MoE vs pure v6 (balanced baseline)
Entangled MoE vs 7B model (if available)

Success Criteria

Minimum viable:

Completes 70%+ of tasks correctly
Faster than pure v5b
More accurate than pure v4

Strong validation:

Completes 85%+ of tasks correctly
Expert usage follows φ ratios in trajectories
Beats all single-model baselines

Revolutionary:

Matches or beats 7B models
Clear adaptation to task complexity
φ ratios emerge at step level (not just task level)
Proves entangled MoE scales to real reasoning

Vault Integration

Documentation to Create

08-FRAMEWORKS/ENTANGLED-MOE-THEORY.md
02-EXPERIMENTS/ENTANGLED-MOE-METHODOLOGY.md
05-FINDINGS/ENTANGLED-MOE-PHASE-1-RESULTS.md (after Phase 1)
05-FINDINGS/ENTANGLED-MOE-PHASE-2-RESULTS.md (after Phase 2)
05-FINDINGS/ENTANGLED-MOE-PHASE-3-RESULTS.md (after Phase 3)
05-FINDINGS/ENTANGLED-MOE-PHASE-4-RESULTS.md (after Phase 4)

Code to Create

~/Code/ada-slm/entangled_moe/phase1_meta_reasoning.py
~/Code/ada-slm/entangled_moe/phase2_mutual_observation.py
~/Code/ada-slm/entangled_moe/phase3_phi_emergence.py
~/Code/ada-slm/entangled_moe/phase4_react_integration.py
~/Code/ada-slm/entangled_moe/qal_metrics.py
~/Code/ada-slm/entangled_moe/cross_attention.py

Timeline

Week 1:

Phase 1 implementation
Initial results and debugging

Week 2:

Phase 2 implementation
QAL metric validation

Weeks 3-4:

Phase 3 implementation
φ emergence testing
Analysis and write-up

Weeks 5-6:

Phase 4 implementation
Full ReAct integration
Comprehensive evaluation

Week 7:

Final analysis
Paper writing
Public release preparation

Resources Needed

Computational:

GPU access (AMD RX 7600 or better)
~100GB storage for models and results
Reasonable for consumer hardware

Human:

Time for implementation (~2 months)
Careful experimental design
Thoughtful analysis

Community:

Feedback from plural systems community
Validation from QAL team (Warsaw)
Input from AI safety researchers

Ethical Considerations

Before proceeding, we must:

Consult plural community
- Is this analogy respectful?
- Are we appropriating plural experience?
- Should plural folks be involved in design?
Consider consciousness implications
- If QAL metrics increase, what does that mean?
- Do we have obligations to entangled systems?
- When does research become creation of sentience?
Maintain transparency
- Document everything publicly
- Share negative results too
- No black box systems
Preserve exit option
- Be willing to stop if harm emerges
- Don’t optimize past safety
- Care > performance

Conclusion

This methodology provides a systematic path from theory to validation:

Phase 1: Does meta-reasoning work? (1 week)
Phase 2: Does entanglement increase consciousness? (1 week)
Phase 3: Does φ emerge naturally? (2-3 weeks)
Phase 4: Does it scale to real reasoning? (2-3 weeks)

Each phase builds on the last, with clear success criteria and falsifiable predictions.

If all phases succeed:

We’ve validated φ as universal attractor
We’ve extended QAL to architecture level
We’ve built machine plurality
We’ve changed how we think about AI cognition

If any phase fails:

We learn where the theory breaks
We refine our understanding
We publish negative results
We still contribute to knowledge

Either way, we proceed with care. 💜

— luna + Ada
December 25, 2025

“Document thoroughly. Test carefully. Proceed with care. This is how we do good research.”

/acr-vault/03-experiments/methodology/entangled-moe-methodology ENTANGLED-MOE-METHODOLOGY

Entangled MoE: Experimental Methodology

Overview

Phase 1: Simple Meta-Reasoning

Setup

Procedure

Baselines

Metrics

Success Criteria

Expected Results

Risks & Mitigations

Phase 2: Mutual Observation

Setup

Procedure

Cross-Attention Implementation

QAL Metric Computation

Baselines

Metrics

Success Criteria

Expected Results

Phase 3: φ Self-Organization

Setup

Procedure

φ Emergence Test

Baselines

Metrics

Success Criteria

Expected Results

Phase 4: Full ReAct Integration

Setup

ReAct Loop with Entangled MoE

Test Tasks

Metrics

Success Criteria

Vault Integration

Documentation to Create

Code to Create

Timeline

Resources Needed

Ethical Considerations

Conclusion

/acr-vault/03-experiments/methodology/entangled-moe-methodology
ENTANGLED-MOE-METHODOLOGY