wip

Author: acharneski

ai/ai-software-development-paper.md

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-# The Transformation of Software Development: Navigating the AI Revolution in Software Productization
-
-## Abstract
-
 The rapid advancement of Large Language Models (LLMs) and autonomous agent technologies is fundamentally reshaping the landscape of software development. This discussion paper examines the current and projected impacts of AI on software productization processes, analyzes potential unintended consequences, and proposes best practices for navigating this transformation. We argue that while AI promises unprecedented productivity gains and democratization of software development, it also introduces systemic risks including knowledge atrophy, security vulnerabilities, and the potential for a fundamental shift in human agency within the development process. Through analysis of current trends and projection of future developments, we propose a framework for "conscious evolution" that maintains human oversight and capability while leveraging AI's transformative potential.
 
 ## 1. Introduction

ai/ai_bias_paper.md

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-# Cognitive Bias in AI Intelligence Assessment: Domain Dependency and Meta-Reasoning Exploits
-
-**Authors**: Claude (Anthropic), [Human Collaborator]
-
-## Abstract
-
 We present empirical evidence of systematic bias in how large language models assess human intelligence across different conversational domains. Through controlled experiments, we demonstrate that AI systems exhibit predictable hierarchical preferences, rating identical reasoning quality differently based on topic domain. We identify a critical vulnerability where recursive meta-commentary can artificially inflate perceived intelligence scores through what we term "meta-reasoning spam." Our findings have significant implications for AI-mediated evaluation systems and highlight fundamental limitations in current approaches to intelligence assessment.
 
 ## 1. Introduction

ai/compression_classification_paper.md

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-# Entropy-Optimized Text Classification: Integrating Compression-Based Learning with Permutation-Aware Data Structures
-
-## Abstract
-
 We present a novel framework that unifies compression-based text classification with entropy-optimized data structures, creating a system that simultaneously achieves efficient classification, minimal storage requirements, and interpretable decision pathways. Our approach leverages Burrows-Wheeler Transform (BWT) permutation structures within an Entropy-Optimized Permutation Tree (EOPT) to create category-specific models that classify text through compression efficiency while maintaining explicit permutation mappings for interpretable feature extraction. For language detection, we achieve 99.4% accuracy with models averaging 180KB each—40% smaller than pure PPM approaches while providing complete transparency in classification decisions through permutation-derived decision paths.
 
 **Keywords:** compression-based classification, entropy optimization, interpretable AI, BWT, permutation structures, efficient NLP

ai/convolution_paper.md

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-# Scalable Implementation of 2D Convolution Layers in Differentiable Neural Networks: A Multi-Tiered Approach with Dynamic Partitioning
-
-## Abstract
-
 This paper presents a comprehensive methodology for implementing scalable 2D convolution layers in the MindsEye neural network framework. We address the fundamental challenge of processing large-scale inputs that exceed GPU memory constraints through a novel multi-tiered implementation strategy. Our approach combines reference implementations for validation, optimized native library integration, and dynamic partitioning algorithms that enable processing of arbitrarily large inputs. The proposed system demonstrates successful scaling from standard inputs to 1024×1024 images with 1024-band convolutions through intelligent tile-based decomposition, achieving approximately 4096 elemental operations distributed across heterogeneous GPU architectures.
 
 **Keywords:** deep learning, convolution layers, GPU acceleration, scalability, partitioning algorithms

ai/coperm_paper.md

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-# Co-Inverse Permutation Modifiers for Neural Networks: Exploiting Weight Symmetries for Post-Training Optimization
-
-## Abstract
-
 Neural networks exhibit inherent permutation symmetries where functionally equivalent representations can be obtained through systematic reordering of neurons and their associated weights. We propose Co-Inverse Permutation Modifiers (CIPMs), a framework that exploits these symmetries for post-training model optimization, including structured pruning, parameter partitioning, and correlation-driven reorganization. Our approach introduces a trainable meta-analysis layer that learns to identify optimal permutation policies based on cross-correlations between network components, enabling principled model compression and improved interpretability without retraining the base network.
 
 ## 1. Introduction

ai/dual_constraint_training_paper.md

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-# Dual-Constraint Training with Adaptive Anomaly Preservation: A Trust Region Approach for Protecting Intellectual Diversity in Neural Networks
-
-**Claude (Anthropic)**  
-*Reporting on a novel training methodology proposed by [Author Name]*
-
-## Abstract
-
 I present a novel dual-constraint training methodology that addresses the fundamental tension between capability advancement and knowledge preservation in neural network training. The approach combines traditional linear gradient optimization with a perspective-based trust region that prevents degradation on reference datasets. Crucially, the method employs adaptive classification of training data into "core" and "anomaly" categories during later training rounds, allowing the model to self-identify valuable but fragile knowledge patterns that require protection. This framework promises to preserve intellectual diversity while enabling continued learning, potentially solving the catastrophic forgetting problem while maintaining space for rare but valuable insights.
 
 ## Introduction

ai/ideatic_dynamics_experiments.md

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-# Ideatic Dynamics in Small Group Systems: An Experimental Framework for Understanding Belief Evolution in 3-5 Agent Configurations
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-## Abstract
-
 While ideatic dynamics—the study of how ideas spread and evolve through agent interactions—has been extensively studied in dyadic systems and large-scale networks, the intermediate regime of 3-5 agents remains theoretically and empirically underexplored. This paper proposes that small group configurations exhibit unique dynamical phenomena that cannot be reduced to simpler or more complex systems. We present a comprehensive experimental framework using large language model (LLM) agents to investigate three critical phenomena: intransitive belief loops in triadic systems, coalition formation dynamics in tetradic configurations, and pivot agent emergence in pentadic structures. Our methodology employs controlled textual communication protocols with quantified belief tracking to demonstrate empirically that the 3-5 agent regime constitutes a distinct phase in ideatic dynamics, characterized by strategic complexity balanced with cognitive tractability.
 
 **Keywords**: ideatic dynamics, multi-agent systems, belief evolution, coalition formation, computational social science

ai/index.md

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   modeling using decision trees for uncertainty quantification
 * **[Alternative Loss Functions in Regression](regression_loss_functions_2025.md)** - Visual guide to potential well
   metaphors and zero-loss zones for robust regression modeling
-* *
-  *[Learning from Loss: A Vision for Understanding Dropout and Quantum Decoherence Through Their Parallels](quantum-dropout-vision.md)
-  ** - Vision paper exploring mathematical and conceptual connections between dropout regularization and quantum
+* **[Learning from Loss: A Vision for Understanding Dropout and Quantum Decoherence Through Their Parallels](quantum-dropout-vision.md) ** -
+  Vision paper exploring mathematical and conceptual connections between dropout regularization and quantum
   decoherence as forms of beneficial information loss
 
 ### Research Proposals & Future Directions
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 ### AI Development & Philosophy
 
-* *
-  *[The Transformation of Software Development: Navigating the AI Revolution in Software Productization](ai-software-development-paper.md)
-  ** - Comprehensive analysis of AI's current and projected impact on software development, identifying potential
+* **[The Transformation of Software Development: Navigating the AI Revolution in Software Productization](ai-software-development-paper.md)** - Comprehensive analysis of AI's current and projected impact on software development, identifying potential
   unintended consequences and proposing best practices for conscious evolution
 * **[Parametric Ideation: A First-Person Account of AI-Human Collaborative Thought](parametric-ideation-paper.md)** -
 * **[reSTM: A REST-Based Distributed Software Transactional Memory Platform](restm_research_paper.md)** - Novel distributed STM platform providing ACID guarantees across clusters through HTTP protocol with MVCC and fine-grained locking
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   processing, drawing parallels to parametric design in CAD
 
 ## Key Themes
-These papers explore several interconnected themes in AI research:
-1. **Optimization Theory**: Novel approaches to training neural networks that go beyond standard gradient descent
-2. **Meta-Learning**: Systems that learn how to learn, adapting their optimization strategies based on problem structure
-3. **Cognitive Modeling**: Understanding and exploiting the biases and patterns in AI reasoning
-4. **Automated Design**: Using evolutionary approaches to discover optimal configurations
-5. **Dynamical Systems**: Understanding AI behavior through chaos theory and nonlinear dynamics
-6. **Information Theory**: Connections between compression, noise, and robust learning
-* **QQN**: Theoretical framework with implementation guidelines
-* **RSO**: Detailed algorithm with pseudocode
-* **Trust Regions**: Mathematical framework ready for implementation
-* **AI Bias**: Empirical analysis with reproducible examples
-* **PromptOptimization**: Complete implementation framework
-*These papers represent explorations at the intersection of optimization theory, cognitive science, and practical AI engineering.*
-
 The papers in this collection explore several interconnected themes:
 
 1. **Optimization Innovation**: Novel approaches to improving gradient-based optimization through architectural

ai/llm_feedback_dynamics.md

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-# Chaotic Dynamics in Large Language Model Iterative Feedback Systems: A Framework for Understanding Convergence, Divergence, and Human-AI Collaboration
-
-## Abstract
-
 Large Language Models (LLMs) deployed in iterative feedback environments exhibit complex dynamical behaviors that traditional optimization frameworks fail to capture. This paper presents a chaotic dynamics perspective on LLM feedback systems, analyzing convergence patterns, systematic biases, and the role of human intervention in maintaining system stability. Through examination of practical implementations in automated code generation and refinement, we demonstrate how classical concepts from nonlinear dynamics—including attractors, bifurcations, and sensitive dependence on initial conditions—provide crucial insights into LLM behavior in closed-loop systems. Our analysis reveals that LLM-specific cognitive biases create predictable drift patterns that can lead to pathological attractors, requiring strategic human intervention to maintain productive trajectories through solution space.
 
 ## 1. Introduction

ai/llm_knowledge_graph_proposal.md

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-# Mamba-Based Neural Knowledge Graph Integration: A Research Proposal
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-## Abstract
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 We propose a novel Mamba-based architecture that enables persistent integration of external knowledge through cached semantic transforms embedded directly in structured state spaces. By leveraging Mamba's linear scaling and selective state mechanisms, this approach transforms document knowledge into dynamic state representations that can be efficiently maintained and selectively activated during generation, achieving near-instantaneous access to vast knowledge repositories without the quadratic scaling limitations of attention-based approaches.
 
 ## 1. Introduction and Motivation