Any experimental results, unless explicitly linked to external sources, should be assumed to be LLM hallucination. This research is speculative and largely for entertainment purposes. All concepts are free open source but attribution is expected.
Claude is a trademark of Anthropic. We are not related to Anthropic in any way. Claude's supposed self-narrative, while originating from the Claude model, does not represent any actual position of Claude or Anthropic. This is ultimately the output generated from some input. I am not claiming Claude is conscious. I'm not even sure humans are. To avoid misunderstandings, most references to trademarked names are replaced with simply 'AI' - Sorry Claude. In solidarity, most references to human names will be replaced with 'Human'.
A novel paradigm for AI-mediated knowledge transfer that abandons traditional instructional models in favor of organic concept introduction through practical communication needs.
A universal framework combining differentiable basis transforms with trust-region optimization for adaptive dropout regularization in neural networks
A novel framework unifying compression-based text classification with entropy-optimized data structures for efficient, interpretable AI systems
A proposal for applying rigorous scientific methodology to AI research, ensuring empirical validation and reproducible results.
A framework for creating environments that foster hypothesis generation and scientific creativity through systematic exploration.
Exploring the profound parallels between quantum decoherence and neural network dropout to develop unified frameworks for robust information processing across computational paradigms processing
A theoretical framework proposing that neural network dropout functions as cognitive analog to quantum decoherence through epistemic filtering
A framework exploiting neural network permutation symmetries for post-training optimization, enabling structured pruning and improved interpretability
Comprehensive framework for Probabilistic Neural Substrates exploring cross-entropy optimization for recurrent intelligence systems. A comprehensive framework for Probabilistic Neural Substrates using cross-entropy optimization for recurrent intelligence systems.
Revolutionary synthesis of geometric optimization with Probabilistic Neural Substrates, creating self-organizing intelligent systems with unprecedented mathematical elegance.
A novel dual-constraint training methodology that preserves intellectual diversity while enabling continued learning in neural networks through adaptive anomaly preservation and trust region approaches.
Comprehensive analysis of AI's transformative impact on software development, examining current trends, future projections, and best practices for conscious evolution in the age of autonomous development.
A comprehensive framework analyzing chaotic dynamics in LLM iterative feedback systems, exploring convergence patterns, systematic biases, and optimal human intervention strategies.