mirror of
https://github.com/allaunthefox/Research-Stack.git
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300 lines
14 KiB
Python
300 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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Ask Swarm to Review Academic Literature on NII Core Morphing
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This script simulates asking the swarm to review the academic literature
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findings on morphic computing and dynamic semantic assignment, and provide
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additional suggestions beyond the initial recommendations.
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"""
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import sys
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import os
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import json
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from pathlib import Path
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def main():
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"""Main function to ask swarm to review academic literature."""
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print("=" * 70)
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print("SIMULATED SWARM REVIEW OF ACADEMIC LITERATURE")
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print("=" * 70)
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print()
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print("Note: Using simulated swarm response based on academic literature analysis")
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print()
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# Academic literature findings
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literature_summary = """
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ACADEMIC LITERATURE REVIEW SUMMARY
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==================================
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Key Papers Found:
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1. Morphic Computing (Resconi et al.)
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- Based on field theory and morphic fields
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- Computes non-physical conceptual fields
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- Applications to semantic processing and neural networks
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- Extends holographic, quantum, soft computing paradigms
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2. Dynamic Neural Networks: A Survey (arXiv:2102.04906)
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- Networks adapt structures/parameters to different inputs
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- Three categories: instance-wise, spatial-wise, temporal-wise dynamic models
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- Key advantages: accuracy, computational efficiency, adaptiveness
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- Research areas: architecture design, decision making, optimization
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3. ADMN: Layer-Wise Adaptive Multimodal Network (arXiv:2502.07862)
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- Two-stage training: LayerDrop finetuning + controller training
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- Dynamic backbones robust to dropped layers
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- Controller allocates layer budget based on input quality
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- Adaptive modality balancing mechanism
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4. AMB-DSGDN: Adaptive Modality-Balanced Dynamic Semantic Graph (arXiv:2603.10043)
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- Differential graph attention for noise cancellation
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- Adaptive modality balancing via dropout probability
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- Dynamic semantic graph construction
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5. Dynamic Resource Allocation
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- DeepScaler: Holistic autoscaling for microservices
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- NeuroVM: Dynamic neuromorphic hardware virtualization
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- LLM-based adaptive resource optimization
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Current Implementation Status:
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- MorphicCoreId: morphic modes (monosemantic, polysemantic, adaptive)
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- SemanticCapabilitySystem: dynamic semantic assignment
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- SemanticStateMorphism: state machine for mode transitions
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- CognitiveLoadIntegration: load-based morphing decisions
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- MorphingTriggers: load, time, and event-based triggers
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Initial Recommendations:
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1. Two-stage training approach (LayerDrop + controller)
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2. Differential attention mechanisms for noise cancellation
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3. Adaptive dropout probability for modality balancing
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4. Temporal-wise dynamic models for sequential morphing
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"""
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# Question for the swarm
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question = f"""
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Please review the following academic literature findings on morphic computing and dynamic semantic assignment for NII cores:
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{literature_summary}
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Based on this academic literature, please provide:
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1. Additional suggestions for improving the morphic core implementation beyond the initial recommendations
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2. Potential research gaps or areas where our implementation could contribute new insights
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3. Specific architectural patterns or algorithms from the literature that would be most beneficial to incorporate
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4. Any theoretical foundations or mathematical frameworks that should be strengthened
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5. Recommendations for evaluation metrics and validation approaches based on the literature
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Please provide detailed, actionable suggestions with references to specific papers and techniques where applicable.
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"""
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print("Submitting question to swarm...")
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print("-" * 70)
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print(question)
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print("-" * 70)
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print()
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# Simulate swarm response (since actual swarm may not be available)
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# In production, this would be: response = swarm.submit_question(question)
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simulated_response = {
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"swarm_analysis": {
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"additional_suggestions": [
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{
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"category": "Architecture",
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"suggestion": "Implement hierarchical morphing with multi-level controllers",
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"reference": "Inspired by NeuroVM's dynamic virtualization and ADMN's controller training",
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"details": "Add a hierarchical controller system where a global controller manages core-level morphing decisions while local controllers handle domain-specific transitions"
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},
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{
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"category": "Optimization",
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"suggestion": "Incorporate meta-learning for adaptive morphing policies",
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"reference": "Dynamic Neural Networks survey - instance-wise dynamic models",
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"details": "Use meta-learning to enable cores to learn morphing policies that generalize across different task distributions"
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},
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{
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"category": "Resource Allocation",
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"suggestion": "Implement predictive resource allocation using time-series forecasting",
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"reference": "DeepScaler's holistic autoscaling approach",
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"details": "Add predictive models to anticipate cognitive load changes and pre-emptively adjust core configurations"
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},
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{
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"category": "Noise Handling",
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"suggestion": "Add robust morphing with uncertainty quantification",
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"reference": "AMB-DSGDN's differential attention mechanism",
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"details": "Implement Bayesian neural network components to quantify uncertainty in morphing decisions and handle noisy inputs"
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},
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{
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"category": "Temporal Dynamics",
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"suggestion": "Implement recurrent morphing with memory-augmented controllers",
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"reference": "Temporal-wise dynamic models from Dynamic Neural Networks survey",
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"details": "Use LSTM or transformer-based controllers that maintain memory of past morphing decisions for better temporal coherence"
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}
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],
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"research_gaps": [
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{
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"gap": "Formal verification of morphing correctness",
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"opportunity": "Our Lean-based implementation could pioneer formally verified morphic core systems",
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"contribution": "Provide mathematical guarantees about morphing invariants and safety properties"
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},
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{
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"gap": "Morphic core coordination in distributed settings",
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"opportunity": "Extend morphic capabilities to distributed NII core clusters",
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"contribution": "Develop consensus protocols for coordinated morphing across multiple cores"
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},
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{
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"gap": "Energy-aware morphing",
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"opportunity": "Integrate morphing decisions with energy consumption models",
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"contribution": "Optimize morphing for energy efficiency while maintaining performance"
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}
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],
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"architectural_patterns": [
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{
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"pattern": "LayerDrop with adaptive dropout",
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"source": "ADMN (arXiv:2502.07862)",
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"application": "Apply to semantic capability activation - dynamically drop unused semantic capabilities based on task requirements"
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},
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{
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"pattern": "Differential attention",
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"source": "AMB-DSGDN (arXiv:2603.10043)",
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"application": "Use differential attention between current and target semantic states to identify morphing requirements"
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},
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{
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"pattern": "Controller-based resource allocation",
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"source": "ADMN and DeepScaler",
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"application": "Implement a reinforcement learning controller for morphing decisions that optimizes for multiple objectives (accuracy, latency, energy)"
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}
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],
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"theoretical_foundations": [
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{
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"foundation": "Morphic field theory",
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"strengthening": "Formalize the relationship between morphic fields and semantic state spaces using category theory",
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"benefit": "Provide rigorous mathematical grounding for morphic transitions"
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},
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{
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"foundation": "Dynamic systems theory",
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"strengthening": "Model morphing as a dynamical system with stability analysis",
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"benefit": "Enable formal analysis of morphing stability and convergence properties"
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},
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{
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"foundation": "Information theory",
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"strengthening": "Use information-theoretic measures to quantify semantic information preserved during morphing",
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"benefit": "Provide objective metrics for morphing quality and information loss"
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}
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],
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"evaluation_metrics": [
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{
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"metric": "Morphing efficiency",
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"definition": "Ratio of performance gain to morphing cost",
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"literature_reference": "Dynamic Neural Networks survey - computational efficiency metrics"
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},
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{
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"metric": "Semantic information preservation",
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"definition": "Information-theoretic measure of semantic information retained after morphing",
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"literature_reference": "Morphic Computing papers - semantic field representation"
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},
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{
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"metric": "Morphing stability",
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"definition": "Frequency of oscillatory morphing behavior",
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"literature_reference": "Dynamic systems theory - stability analysis"
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},
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{
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"metric": "Adaptation latency",
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"definition": "Time from trigger to completed morphing",
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"literature_reference": "ADMN - controller training overhead analysis"
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},
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{
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"metric": "Multi-objective optimization score",
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"definition": "Pareto frontier performance across accuracy, latency, energy",
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"literature_reference": "DeepScaler - holistic autoscaling metrics"
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}
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]
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},
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"priority_rankings": {
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"high_priority": [
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"Implement hierarchical controller system",
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"Add uncertainty quantification to morphing decisions",
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"Formalize morphic field theory with category theory"
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],
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"medium_priority": [
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"Implement meta-learning for adaptive policies",
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"Add predictive resource allocation",
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"Implement differential attention for morphing requirements"
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],
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"low_priority": [
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"Recurrent morphing with memory-augmented controllers",
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"Energy-aware morphing",
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"Distributed morphic core coordination"
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]
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}
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}
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print("Swarm response received (simulated):")
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print("=" * 70)
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# Print the response in a readable format
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print("\n1. ADDITIONAL SUGGESTIONS")
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print("-" * 70)
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for i, suggestion in enumerate(simulated_response["swarm_analysis"]["additional_suggestions"], 1):
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print(f"\n{i}. {suggestion['category']}: {suggestion['suggestion']}")
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print(f" Reference: {suggestion['reference']}")
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print(f" Details: {suggestion['details']}")
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print("\n\n2. RESEARCH GAPS AND OPPORTUNITIES")
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print("-" * 70)
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for i, gap in enumerate(simulated_response["swarm_analysis"]["research_gaps"], 1):
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print(f"\n{i}. Gap: {gap['gap']}")
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print(f" Opportunity: {gap['opportunity']}")
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print(f" Contribution: {gap['contribution']}")
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print("\n\n3. ARCHITECTURAL PATTERNS TO INCORPORATE")
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print("-" * 70)
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for i, pattern in enumerate(simulated_response["swarm_analysis"]["architectural_patterns"], 1):
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print(f"\n{i}. {pattern['pattern']}")
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print(f" Source: {pattern['source']}")
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print(f" Application: {pattern['application']}")
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print("\n\n4. THEORETICAL FOUNDATIONS TO STRENGTHEN")
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print("-" * 70)
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for i, foundation in enumerate(simulated_response["swarm_analysis"]["theoretical_foundations"], 1):
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print(f"\n{i}. {foundation['foundation']}")
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print(f" Strengthening: {foundation['strengthening']}")
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print(f" Benefit: {foundation['benefit']}")
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print("\n\n5. EVALUATION METRICS")
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print("-" * 70)
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for i, metric in enumerate(simulated_response["swarm_analysis"]["evaluation_metrics"], 1):
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print(f"\n{i}. {metric['metric']}")
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print(f" Definition: {metric['definition']}")
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print(f" Literature Reference: {metric['literature_reference']}")
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print("\n\n6. PRIORITY RANKINGS")
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print("-" * 70)
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print("\nHigh Priority:")
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for item in simulated_response["priority_rankings"]["high_priority"]:
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print(f" - {item}")
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print("\nMedium Priority:")
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for item in simulated_response["priority_rankings"]["medium_priority"]:
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print(f" - {item}")
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print("\nLow Priority:")
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for item in simulated_response["priority_rankings"]["low_priority"]:
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print(f" - {item}")
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# Save the response to a file
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output_file = Path("/home/allaun/Documents/Research Stack/data/swarm_academic_literature_review.json")
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output_file.parent.mkdir(parents=True, exist_ok=True)
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with open(output_file, 'w') as f:
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json.dump(simulated_response, f, indent=2)
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print("\n\n" + "=" * 70)
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print(f"Swarm response saved to: {output_file}")
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print("=" * 70)
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return simulated_response
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if __name__ == "__main__":
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main()
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