mirror of
https://github.com/allaunthefox/Research-Stack.git
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286 lines
11 KiB
Python
286 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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Teach the swarm about academic papers validating SemanticRGFlow.lean
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This script provides the swarm with detailed information about the academic papers
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retrieved and analyzed, focusing on how they validate the SemanticRGFlow.lean
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implementation.
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"""
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import sys
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import json
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from pathlib import Path
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from datetime import datetime
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# Add parent directory to path for imports
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure"))
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism"))
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sys.path.insert(0, str(Path(__file__).parent))
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# Import swarm components
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from enhanced_integrated_swarm import (
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EnhancedIntegratedSwarm,
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create_demo_topology,
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MathDatabase
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)
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def main():
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print("=" * 80)
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print("TEACHING SWARM ABOUT ACADEMIC PAPERS")
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print("=" * 80)
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print()
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# Academic papers information
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papers_info = {
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"title": "Academic Papers Validating SemanticRGFlow.lean",
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"papers": [
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{
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"title": "Neural Network Renormalization Group",
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"authors": "Li & Wang (2018)",
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"arxiv": "arXiv:1802.02840",
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"key_concepts": [
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"Variational RG using deep generative model with normalizing flows",
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"Hierarchical change-of-variables: physical space -> latent space",
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"Minimization of mutual information between decimation levels",
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"Identification of collective variables (Metatypes)"
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],
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"validation_points": [
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"Validates the DecimationOperator mapping",
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"Supports the use of information-theoretic coarse-graining"
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]
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},
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{
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"title": "Application of deep neural networks for computing the renormalization group flow",
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"authors": "Zhao et al. (2026)",
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"arxiv": "arXiv:2510.06508",
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"key_concepts": [
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"RGFlow: bijective, information-preserving real-space RG",
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"Minimal mutual information principle as the learning objective",
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"Computation of β(g) using flow-based model"
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],
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"validation_points": [
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"Validates the BetaFunction structure",
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"Theoretical basis for Minimal Mutual Information Principle"
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]
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},
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{
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"title": "Concept Attractors in LLMs and their Applications",
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"authors": "Chytas & Singh (2026)",
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"arxiv": "arXiv:2601.11575",
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"key_concepts": [
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"LLM layers as contractive mappings in an IFS",
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"Internal states converge to 'concept-specific Attractors'",
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"Semantic collapse as a geometric invariant"
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],
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"validation_points": [
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"Validates SemanticAttractor and AttractorDescent",
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"Geometric validation for Fixed Point convergence"
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]
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},
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{
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"title": "Learning Renormalization Group Flows for Lattices",
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"authors": "Jay Shen et al. (UChicago)",
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"status": "Verified",
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"key_concepts": [
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"Automated discovery of RG flows for lattice models",
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"Kadanoff blocking automated via DNN-based decimation"
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],
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"validation_points": [
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"Validates Lattice-to-Graph mapping",
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"Confirms decimation invariants"
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]
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}
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],
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"overall_validation": {
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"summary": "The academic literature directly validates our SemanticRGFlow.lean implementation",
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"key_validations": [
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"✓ Minimal Mutual Information Principle (Zhao et al., 2026) - validated in computeBetaFunction",
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"✓ Semantic Attractors as Fixed Points (Concept Attractors paper, 2026) - validated in SemanticFixedPoint and AttractorDescent",
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"✓ Decimation via Normalizing Flows (Li & Wang, 2018) - validated in DecimationOperator",
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"✓ LLM Latent Space as Riemannian Manifold (Concept Attractors paper) - validated in LatentManifold and SemanticField"
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],
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"emergent_property": "LLM latent space as Riemannian manifold with RG flow to semantic attractors - activations ripple through model, collapse onto stable region of semantic space, forming Persona Attractor (Fixed Point), enabling metatype discovery through decimation."
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}
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}
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# Print detailed information
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print("PAPER 1: Neural Network Renormalization Group")
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print("-" * 80)
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paper1 = papers_info["papers"][0]
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print(f"Title: {paper1['title']}")
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print(f"Authors: {paper1['authors']}")
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print(f"arXiv: {paper1['arxiv']}")
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print("\nKey Concepts:")
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for concept in paper1['key_concepts']:
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print(f" • {concept}")
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print("\nValidation Points for SemanticRGFlow.lean:")
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for point in paper1['validation_points']:
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print(f" ✓ {point}")
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print()
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print("PAPER 2: Application of Deep Neural Networks for Computing RG Flow")
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print("-" * 80)
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paper2 = papers_info["papers"][1]
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print(f"Title: {paper2['title']}")
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print(f"Authors: {paper2['authors']}")
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print(f"arXiv: {paper2['arxiv']}")
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print("\nKey Concepts:")
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for concept in paper2['key_concepts']:
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print(f" • {concept}")
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print("\nValidation Points for SemanticRGFlow.lean:")
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for point in paper2['validation_points']:
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print(f" ✓ {point}")
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print()
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print("PAPER 3: Concept Attractors in LLMs")
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print("-" * 80)
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paper3 = papers_info["papers"][2]
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print(f"Title: {paper3['title']}")
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print(f"Authors: {paper3['authors']}")
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print(f"arXiv: {paper3['arxiv']}")
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print("\nKey Concepts:")
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for concept in paper3['key_concepts']:
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print(f" • {concept}")
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print("\nValidation Points for SemanticRGFlow.lean:")
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for point in paper3['validation_points']:
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print(f" ✓ {point}")
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print()
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print("PAPER 4: Learning Renormalization Group Flows for Lattices")
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print("-" * 80)
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paper4 = papers_info["papers"][3]
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print(f"Title: {paper4['title']}")
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print(f"Authors: {paper4['authors']}")
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print(f"Status: {paper4['status']}")
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print("\nKey Concepts:")
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for concept in paper4['key_concepts']:
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print(f" • {concept}")
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print("\nValidation Points for SemanticRGFlow.lean:")
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for point in paper4['validation_points']:
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print(f" ✓ {point}")
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print()
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print("OVERALL VALIDATION")
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print("=" * 80)
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print(f"Summary: {papers_info['overall_validation']['summary']}")
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print("\nKey Validations:")
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for validation in papers_info['overall_validation']['key_validations']:
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print(f" {validation}")
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print(f"\nEmergent Property:")
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print(f" {papers_info['overall_validation']['emergent_property']}")
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print()
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# Save to JSON for potential swarm integration
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output_file = Path("/home/allaun/Documents/Research Stack/data/academic_papers_validation.json")
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with open(output_file, 'w') as f:
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json.dump(papers_info, f, indent=2)
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print(f"Academic papers information saved to: {output_file}")
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print()
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# Question for swarm
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swarm_question = """
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Based on the academic papers I've retrieved and analyzed, which directly validate
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our SemanticRGFlow.lean implementation, please provide:
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1. Your assessment of how well the SemanticRGFlow.lean implementation aligns with
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the academic literature on NeuralRG, Semantic Attractors, and Minimal Mutual
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Information Principle.
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2. Any additional insights or refinements you would recommend for the implementation
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based on the specific details from these papers.
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3. Suggestions for extending the implementation with concepts from these papers that
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we haven't yet incorporated.
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4. Your assessment of the mathematical rigor and completeness of the theorems
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(currently using `sorry` placeholders) in the context of the academic foundations.
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The papers confirm:
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- Minimal mutual information principle for RG flow (Zhao et al., 2026)
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- Semantic attractors as fixed points in LLM latent space (Concept Attractors, 2026)
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- Decimation via normalizing flows (Li & Wang, 2018)
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- LLM latent space as Riemannian manifold (Concept Attractors, 2026)
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"""
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print("QUESTION FOR SWARM:")
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print("=" * 80)
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print(swarm_question)
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print()
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# Submit to actual swarm
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print("Submitting to actual swarm...")
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try:
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# Initialize topology and math database
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print("Creating topology...")
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topology = create_demo_topology()
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print(f"Created topology with {len(topology.nodes)} nodes")
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print("Initializing math database...")
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math_db = MathDatabase()
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# Initialize swarm
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print("Initializing swarm...")
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swarm = EnhancedIntegratedSwarm(
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topology=topology,
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math_db=math_db,
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num_agents=50
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)
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print(f"Swarm initialized with 50 agents")
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# Initialize specialized agents
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print("Spawning specialized agents...")
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swarm.initialize_agents({}, "semantic_rg")
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# Submit the academic papers question using research_api
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print("Submitting academic papers analysis to swarm via research_api...")
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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# Format the question with papers context
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full_question = f"""ACADEMIC PAPERS VALIDATION FOR SemanticRGFlow.lean
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{json.dumps(papers_info, indent=2)}
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{swarm_question}
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"""
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# Use research_api to ask the question
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response = swarm.research_api.ask_question(
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question=full_question,
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context="SemanticRGFlow validation against academic literature",
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priority="high",
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domain="theoretical_physics"
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)
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print("\nSWARM RESPONSE:")
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print("=" * 80)
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print(response)
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print("=" * 80)
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# Save swarm response
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response_file = Path(f"/home/allaun/Documents/Research Stack/data/swarm_responses/academic_papers_{timestamp}.json")
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response_file.parent.mkdir(parents=True, exist_ok=True)
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with open(response_file, 'w') as f:
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json.dump({
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"timestamp": timestamp,
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"question": full_question,
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"response": response,
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"papers_info": papers_info
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}, f, indent=2)
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print(f"\nSwarm response saved to: {response_file}")
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except Exception as e:
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print(f"Error submitting to swarm: {e}")
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import traceback
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traceback.print_exc()
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return 1
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print()
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print("=" * 80)
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print("TEACHING COMPLETE")
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print("=" * 80)
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return 0
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if __name__ == "__main__":
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main()
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