Phylogenic AI Agents
Genetic evolution for AI personalities. Self-optimizing agents with liquid memory, ML analytics, and evolutionary optimization. Agents inherit traits, mutate behaviors, and undergo fitness-based selection across generations.
Agent Scaling Laws
Research implementation of arXiv:2512.08296. Multi-agent coordination architectures with benchmarks. Covers O(n²) vs O(n log n) scaling, hierarchical topologies, and production-ready patterns.
Quantum Reservoir Computing
Theoretical framework connecting reservoir computing to quantum gravity. Explores the interface between high-dimensional dynamical systems and quantum vacuum fluctuations through the Dynamical Casimir Effect. Proposes computational pathways to quantum gravity insights.
Neuromorphic Evolution
LLM-guided evolutionary algorithms for brain-inspired computing. Kraken Liquid Neural Network achieves 89% improvement on ARC benchmark with 6.7× faster convergence. Combines STDP plasticity with semantic mutation strategies.
Temporal Shard RAG
Time-aware retrieval augmented generation with neuromorphic memory. 34% improvement in temporal reasoning, 28% reduction in hallucinations. Documents decay and consolidate like biological memories.
P vs NP & Consciousness
Computational framework for the Millennium Prize Problem. Consciousness-Driven Computation (CDC) explores parallel awareness, swarm intelligence, and probabilistic reasoning for NP-hard optimization. 2-4× speedups on benchmark problems.
Wetware Computing
Biological-digital hybrid architectures for adaptive intelligence. Abeone Wetware Core achieves 94% energy reduction through organic substrates. Thermal-adaptive processing with MEA interfaces for neuronal cultures.
Neuromorphic Code Intelligence
Spiking neural networks for program analysis and code generation. 3.2× faster inference, 92% energy reduction on neuromorphic hardware. Neural AST encoding converts code to temporal spike patterns.
AdaptiveMind
Intelligent AI routing & context engine with 3-tier routing. BitNet (Tier 1) → LFM (Tier 2) → Cloud (Tier 3) with intelligent escalation. Validated against agent scaling laws (arXiv:2512.08296). Features architecture selection, error amplification detection, and cost optimization.
GitHub
✅ Benchmarked
Agent UI
White-label AI platform. Modern UI with 50+ components, plugin marketplace, enterprise monitoring. Liquid theme system (3 themes), Ollama integration, and full brand customization for SaaS deployment.
NS-AI-Suite
Enterprise multi-agent orchestration. Healthcare AI platform with microservices architecture. Components for API gateway, data ingestion, frontdesk AI, and real-time dashboards.
Frequently Asked Questions
What is evolutionary AI?
Evolutionary AI applies principles from biological evolution—genetic algorithms, mutation, crossover, and natural selection—to optimize AI systems. In Phylogenic AI, agents evolve personality traits across generations, automatically discovering optimal behaviors through fitness-based selection rather than manual tuning.
How do multi-agent systems scale?
Multi-agent systems face coordination overhead that typically scales as O(n²) with agent count in flat topologies. Research paper arXiv:2512.08296 "Towards a Science of Scaling Agent Systems" establishes that hierarchical structures can reduce this to O(n log n), and the Agent Scaling Laws implementation provides production-ready patterns for this.
What is the difference between prompt engineering and evolutionary AI?
Prompt engineering manually crafts static instructions for AI models, requiring human expertise and iteration. Evolutionary AI (like Phylogenic AI Agents) automatically evolves optimal behaviors through genetic algorithms—agents self-improve based on feedback, discovering non-obvious improvements without human intervention.
Are these AI research projects open source?
Yes, both Phylogenic AI Agents and Agent Scaling Laws are open-source under the MIT License. They are available on GitHub with full documentation, examples, and test suites. Contributions are welcome.