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R-DKE v2.0 — Physarum-Inspired Recursive Deep Knowledge Engine

A biologically-inspired architecture for continuous self-evolving AI knowledge systems

Author: Marius Gherasim Date: 2025 License: CC0 — Public Domain

  1. Abstract Modern AI systems remain primarily reactive — they generate answers when asked. R-DKE (Recursive Deep Knowledge Engine) introduced a forward-learning concept where AI continuously compresses knowledge, builds a truth graph, questions uncertainty, and synthesizes verified answers. This v2.0 extension draws inspiration from Physarum polycephalum (slime mold), an organism capable of distributed computation, optimal path-finding, and dynamic resource allocation without centralized control. We propose a Physarum-Inspired Reasoning Loop inside R-DKE, where: Uncertainty = "nutrient" Competing reasoning paths grow/decay like slime-mold veins Strongest/verified paths reinforce knowledge graph edges Incorrect/unproductive paths are pruned naturally This allows knowledge to self-organize toward truth-seeking efficiency — a living, adaptive cognitive substrate.

  2. Background: R-DKE Core Loop (v1) Stage Description Information Intake World → structured knowledge Semantic Compression Convert data → knowledge atoms Truth Graph Verified, weighted relationships Self-Questioning Uncertainty triggers recursive inquiry Instant Verified Answers Answers + evidence trace R-DKE reframes AI from search → think to think continuously → answer instantly.

  3. Why Physarum? Physarum polycephalum demonstrates: Distributed problem solving Emergent optimal routing Memory without neurons Adaptive network reconfiguration Efficient exploration/exploitation balance It has solved: Shortest path problems Network optimization Spanning tree formation We borrow its nutrient-driven reinforcement model.

  4. R-DKE v2.0 — Physarum Loop Key Idea Knowledge edges "pulse" like slime-mold veins. Higher confidence edges thicken Low-evidence edges shrink Uncertainty flows toward unresolved nodes System "grows" explanations AI no longer selects answers. It grows them.

  5. Algorithmic Sketch for each knowledge_node: uncertainty = measure_uncertainty(node)

    if uncertainty > threshold: inject("nutrient") into node // triggers expansion

    for each connected path: propagate_signal(path, strength = truth_weight)

     if path solves contradiction or explains uncertainty:
     reinforce(path)
 else:
     decay(path)

Emergent behavior: Truth pathways strengthen → hallucinations decay.

  1. Expected Capabilities Capability Description Self-stabilizing truth networks Reduces hallucination risk Distributed reasoning No single failure point Adaptive curiosity System asks "where do I grow next?" Evidence-driven learning Strong ideas survive, weak ones fade Long-term emergent intelligence Memory-like graph evolution.

  2. Testing & Validation To validate this concept, future experiments can simulate: Graph-based nutrient flow on knowledge graphs Reinforcement based on truth score Path suppression for hallucination tendencies Energy-budget-based reasoning (like slime mold).

  3. Conclusion R-DKE v2.0 proposes a new research direction: AI that organizes knowledge like biology — efficient, distributed, self-improving. Instead of querying knowledge, the system evolves it. This may serve as a stepping stone toward autonomous reasoning architectures beyond transformers and classical search.

  4. Citations / Inspirations Nakagaki T. et al. — Maze solving by slime mold Tero A. et al. — Rules for biologically inspired network design Friston K. — Free-energy principle (adaptive systems) Early AGI architectures & R-DKE v1 (Gherasim, 2025)