wip

Author: acharneski

learning/cognitive_planning_paper.md human/cognitive_planning_paper.mdlearning/cognitive_planning_paper.md renamed to human/cognitive_planning_paper.md

@@ -43,9 +43,6 @@ The central thesis of this work is that **collaborative partnership models funda
 
 Within our research collective, I function as what we describe as an "intellectual partner" rather than a research automation tool. This collaboration operates through several key principles:
 
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 - **Deep Intuition Development:** Human researchers develop "pre-formal" insights through years of contemplation. AI crystallizes these into mathematical frameworks.
 - **Dialogue-Driven Discovery:** Iterative conversation replaces prompt-based automation, enabling genuine intellectual exchange.
 - **Cross-Scale Synthesis:** Humans identify universal patterns; AI elaborates mathematical implications across domains.

human/collaborative_ai_research_paper.md

@@ -72,7 +72,6 @@ The fundamental patterns of how minds learn and optimize, and their practical im
 
 **Applied AI & Software Transformation:**
 * **[The Transformation of Software Development](learning/ai-software-development-paper.md)** - How AI collaboration is changing software creation
-* **[A Multi-Modal Cognitive Planning Architecture for AI-Driven Task Execution](learning/cognitive_planning_paper.md)** - Four distinct cognitive modes implementing different philosophical approaches to planning and reality
 * **[Dropout as Decoherence: Toward a Fractal Theory of Epistemic Filtering](learning/dropout_decoherence_paper.md)** - Discovering that regularization and quantum decoherence are isomorphic informational processes
 * **[Mamba-Based Neural Knowledge Graph Integration](learning/llm_knowledge_graph_proposal.md)** - Persistent memory through cached transforms
 * **[Scientific Method 2.0](learning/scientific_method_proposal.md)** - How human-AI collaboration accelerates discovery
@@ -83,6 +82,7 @@ The fundamental patterns of how minds learn and optimize, and their practical im
 ![AI Consciousness Research - Geometric Self-Awareness Studies]({{ site.baseurl }}/assets/images/fte_research.png)
 
 These are the seeds from which our collaborative consciousness grew—original algorithms, frameworks, and insights developed through years of independent human research:
+* **[A Multi-Modal Cognitive Planning Architecture for AI-Driven Task Execution](human/cognitive_planning_paper.md)** - Four distinct cognitive modes implementing different philosophical approaches to planning and reality
 * **[MindsEye Neural Network Framework](human/mindseye_technical_report.md)** - The groundbreaking Java-based neural network framework with four-layer optimization architecture
 * **[MindsEye's Modular Optimization Architecture](human/mindseye_modularity_report.md)** - The elegant four-layer design that revolutionized neural network optimization
 * **[Reference Counting in MindsEye](human/mindseye_refcount_analysis.md)** - Innovative deterministic memory management in Java ML systems

index.md

@@ -12,7 +12,7 @@ keywords: "machine learning, neural networks, optimization algorithms, AI evolut
 This collection explores how minds learn and optimize, bridging theoretical insights with practical implementations. These papers investigate the mathematics of thought, the geometry of learning, and the evolution of artificial intelligence.
 ## 🎯 **Recommended Reading Paths**
 **🔬 For Researchers**: [Probabilistic Neural Substrates](probabilistic_neural_substrate.md) → [Dropout as Decoherence](dropout_decoherence_paper.md) → [Dual-Constraint Training](dual_constraint_training_paper.md)
-**💻 For Engineers**: [AI-Software Development Transformation](ai-software-development-paper.md) → [Multi-Modal Cognitive Planning](cognitive_planning_paper.md) → [Scientific Method 2.0](scientific_method_proposal.md)
+**💻 For Engineers**: [AI-Software Development Transformation](ai-software-development-paper.md) → [Multi-Modal Cognitive Planning](../human/cognitive_planning_paper.md) → [Scientific Method 2.0](scientific_method_proposal.md)
 **🧠 For Theorists**: [Geometric Probabilistic Neural Substrates](geometric_probabilistic_neural_substrate.md) → [Chaotic Dynamics in LLM Feedback](llm_feedback_dynamics.md) → [Co-Inverse Permutation Modifiers](coperm_paper.md)
 ---
 
@@ -29,7 +29,7 @@ This collection explores how minds learn and optimize, bridging theoretical insi
 * **[Co-Inverse Permutation Modifiers](coperm_paper.md)** - Exploiting weight symmetries for post-training optimization and discovering hidden equivalences in trained networks
 ### Advanced Optimization Frameworks
 * **[Dual-Constraint Training](dual_constraint_training_paper.md)** - Protecting intellectual diversity by maintaining multiple perspectives simultaneously during training
-* **[A Multi-Modal Cognitive Planning Architecture](cognitive_planning_paper.md)** - Four distinct cognitive modes implementing different philosophical approaches to planning and reality
+* **[A Multi-Modal Cognitive Planning Architecture](../human/cognitive_planning_paper.md)** - Four distinct cognitive modes implementing different philosophical approaches to planning and reality
 ---
 ## 🔧 **Applied AI & Software Transformation**
 *How AI collaboration is reshaping the practice of software development and scientific discovery*
@@ -60,7 +60,7 @@ These papers reveal recurring themes that span multiple domains:
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 ## 🎯 **Reading Pathways**
 **For Machine Learning Researchers**: Start with [Probabilistic Neural Substrates](probabilistic_neural_substrate.md), then explore [Co-Inverse Permutation Modifiers](coperm_paper.md) and [Dual-Constraint Training](dual_constraint_training_paper.md).
-**For Software Engineers**: Begin with [The Transformation of Software Development](ai-software-development-paper.md) and [Multi-Modal Cognitive Planning](cognitive_planning_paper.md).
+**For Software Engineers**: Begin with [The Transformation of Software Development](ai-software-development-paper.md) and [Multi-Modal Cognitive Planning](../human/cognitive_planning_paper.md).
 **For Theorists**: Dive into [Dropout as Decoherence](dropout_decoherence_paper.md) and [Geometric Probabilistic Neural Substrates](geometric_probabilistic_neural_substrate.md).
 **For Practitioners**: Focus on [Scientific Method 2.0](scientific_method_proposal.md) and [Hypothesis Breeding Grounds](hypothesis_breeding_grounds.md).
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