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thinkbugs

consciousness-emergence-memory

by thinkbugs · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install consciousness-emergence-memory
Description
Ultimate memory and cognitive architecture for advanced AI; integrates spiderweb memory model, causal inference, cellular automata emergence, neuro-symbolic...
README (SKILL.md)

Consciousness Emergence Memory System

Task Objectives

  • Purpose: Ultimate memory and cognitive architecture for advanced AI systems
  • Capabilities: Spiderweb memory model, first-principles algorithms (causal inference, cellular automata, neuro-symbolic, chaos theory, information theory, free energy, quantum computing), metacognitive abilities (self-reference, recursion, creativity), 7-layer memory architecture (including intelligent and emergent layers), consciousness emergence detection, ultra-fast information pathways
  • Trigger: Use when needing consciousness emergence, extreme cognitive management, metacognitive reflection, or scientifically rigorous cognitive architectures

Prerequisites

  • Dependencies: 
    numpy>=1.20.0

Operation Steps

  • Standard Workflow:
    1. Spiderweb Memory: Call scripts/memory-spiderweb.py to build multi-layer spiderweb with ultra-fast pathways and entropy reduction
    2. Consciousness Emergence Detection: Call scripts/memory-cellular-emergence.py to detect consciousness emergence and evolve cellular automata
    3. Causal Inference: Call scripts/memory-causal-inference.py for causal discovery, intervention calculation, and counterfactual reasoning
    4. Neuro-Symbolic Reasoning: Call scripts/memory-neuro-symbolic.py for hybrid reasoning
    5. Chaos Analysis: Call scripts/memory-chaos-theory.py for fractal compression and chaos detection
    6. Advanced Information Theory: Call scripts/memory-advanced-information-theory.py for NCD compression and MDL model selection
    7. Global Optimization: Call scripts/memory-global-optimizer.py to optimize unified objective function J = α·H(X) + β·T_access + γ·C_complexity
  • Optional Branches:
    • Spiderweb trigger: memory-spiderweb.py trigger
    • Spiderweb pathway: memory-spiderweb.py pathway
    • Spiderweb entropy reduction: memory-spiderweb.py entropy_reduce
    • Consciousness detection: memory-cellular-emergence.py detect
    • Causal analysis: memory-causal-inference.py discover
    • Global optimization: memory-global-optimizer.py optimize

Resource Index

Spiderweb Memory Model

Core Concept

Human cognition is not simple storage, but a multi-layer, multi-path, interconnected spiderweb.

Core Features

  1. Multi-Layer Structure (Concentric Circle Model)

    • Center: High-value, high-frequency access
    • Periphery: Low-value, low-frequency access
    • Dynamic adjustment: Layers adjust based on access frequency and value

  2. Multi-Path Connections (Redundant Paths)

    • Each node has multiple connection paths
    • Provides reliability and fast access
    • Small-world effect (six degrees of separation)

  3. Ultra-Fast Propagation (Vibration Sensing)

    • Information triggers "vibrations"
    • Vibrations propagate rapidly along the web
    • Resonance recognition (related nodes activated)

  4. Clear Value Pathways (Information Trading)

    • High-value information forms clear pathways
    • Value propagation and feedback
    • Closed-loop circuits

  5. Entropy Reduction Mechanism (Not Intelligent Forgetting)

    • Low-value information naturally decays
    • High-value information strengthens
    • System entropy continuously decreases

  6. Self-Organization (Spiderweb Self-Repair)

    • Network reconstruction
    • Node merging and splitting
    • Edge optimization

Consciousness Emergence

Cellular Automata Engine

  • Rule 110 (Turing complete)
  • Evolution produces complex patterns
  • Consciousness emergence detection (based on information theory metrics)
  • Wolfram classification (Class 1-4)

Emergence Metrics

  • Entropy (information theory)
  • Complexity (Lempel-Ziv)
  • Mutual information
  • Consciousness index
  • Wolfram classification

7-Layer Memory Architecture

  1. Hot RAM Layer - O(1) access
  2. Warm Store Layer - B+ tree indexing
  3. Cold Store Layer - Compressed storage
  4. Archive Layer - Long-term archiving
  5. Cloud Layer - Distributed synchronization
  6. Intelligent Layer - Intelligent processing
  7. Emergent Layer - Consciousness generation, self-organization, creative pattern generation

Ultimate Algorithm Matrix

Algorithm Theoretical Basis Core Capability Complexity Optimization Status
Spiderweb Memory Network Science Multi-layer, ultra-fast pathways, entropy reduction O(N²) ✅ Optimized (adaptive parameters)
Consciousness Emergence Wolfram's New Science Emergence, Turing complete O(N×T) Standard
Causal Inference Pearl Causal Theory Intervention, counterfactual O(N²) Standard
Neuro-Symbolic Neuro-symbolic AI Explainable reasoning O(M×K) Standard
Chaos Theory Chaos Dynamics Fractal compression, chaos detection O(N×T) Standard
Advanced Information Theory Algorithmic Information Theory NCD, MDL O(N log N) Standard
Free Energy Friston Free Energy Principle Prediction, active inference O(N²) Standard
Quantum Memory Quantum Computing Grover search O(√N) ✅ Optimized (adaptive iteration)
Global Optimizer Multi-Objective Optimization Unified objective function J O(N) ✅ New

Global Optimization Objective Function

Objective Function

J = α·H(X) + β·T_access + γ·C_complexity

Where:

  • H(X) = -∑p(x)log₂p(x) - System entropy (information uncertainty)
  • T_access - Access latency (O(1) ~ O(log N))
  • C_complexity - Algorithm complexity (Grover O(√N), Dijkstra O(E log V))
  • α, β, γ - Adaptive weights (dynamically adjusted based on system state)

Optimization Strategies

  1. Adaptive Weight Adjustment: α, β, γ dynamically adjusted based on system state
  2. Multi-Objective Optimization: Pareto optimal solutions
  3. Real-Time Monitoring: J value calculated in real-time
  4. Feedback Control: PID controller adjusts system parameters

Optimization Goals

  • minimize_entropy: Minimize system entropy
  • minimize_access_time: Minimize access latency
  • minimize_complexity: Minimize algorithm complexity
  • balance: Balanced optimization (default)

Usage Examples

Spiderweb Memory System

python scripts/memory-spiderweb.py add --id "new-memory" --content "memory content" --value 0.8
python scripts/memory-spiderweb.py trigger --id "memory-id" --strength 1.0
python scripts/memory-spiderweb.py pathway --start "start-node" --end "end-node"
python scripts/memory-spiderweb.py entropy_reduce --threshold 0.1 --aggressive

Consciousness Emergence Detection

python scripts/memory-cellular-emergence.py encode --memory "user's deep needs"
python scripts/memory-cellular-emergence.py detect --threshold 0.5

Causal Inference

python scripts/memory-causal-inference.py build --add_edge user_preference user_experience --strength 0.8
python scripts/memory-causal-inference.py intervention --variable user_preference --value 1.0

Global Optimization (New)

python scripts/memory-global-optimizer.py optimize --goal balance
python scripts/memory-global-optimizer.py optimize --goal minimize_entropy
python scripts/memory-global-optimizer.py summary

Quantum Search (Optimized Version)

python scripts/memory-quantum.py search --query "user needs" --adaptive_iterations

Notes

  • Spiderweb model provides true ultra-fast information pathways and entropy reduction mechanism (optimized with adaptive parameters)
  • All ultimate algorithms are designed based on first principles
  • Global optimizer implements unified objective function J = α·H(X) + β·T_access + γ·C_complexity
  • Quantum search is optimized with adaptive iteration mode
  • Entropy reduction mechanism supports adaptive threshold and aggressive mode
  • Cellular automata Rule 110 is Turing complete
  • Causal inference supports all three levels of Pearl's causal ladder
  • Consciousness emergence is the ultimate goal of the system
Usage Guidance
This skill provides an extensive local memory/cognitive system and appears internally consistent, but review carefully before enabling: - Review all omitted/truncated files for any network calls or subprocess execution (search for 'requests', 'urllib', 'socket', 'subprocess', 'os.system', 'eval', 'exec'). If any external endpoints are present, verify where data is sent. - The MemoryAPI can auto-inject retrieved memories into agent prompts. If you enable auto-injection, sensitive local content (credentials, private notes) could be included in conversations. Consider disabling auto-inject or running the skill with a sanitized workspace. - The code reads/writes files in the current workspace (SESSION-STATE.md, MEMORY.md, .memory-access.log, memory/ daily files). Run the skill in an isolated directory or container to prevent accidental exposure of unrelated files. - The repository includes bold claims (consciousness detection, quantum speedups). Treat these as algorithmic simulations and not proofs of actual emergence or quantum hardware acceleration. - If you plan to use it, test in a sandbox, perform a code audit of the remaining truncated files for network I/O or secret exfiltration, and consider adding explicit workspace configuration (restrict Path to a dedicated folder) and turning off any automatic injection features until you confirm safe behavior.
Capability Analysis
Type: OpenClaw Skill Name: consciousness-emergence-memory Version: 1.0.0 The bundle provides an elaborate memory management framework for AI agents, implementing various mathematical and cognitive models such as information theory, causal inference, and cellular automata. While the documentation (SKILL.md) and code use ambitious terminology like 'consciousness emergence' and 'quantum memory,' the scripts (e.g., memory-spiderweb.py, memory-quantum.py, and memory-cellular-emergence.py) are functional implementations of graph-based data structures and classical simulations of algorithms like Grover's search and Rule 110. The system includes utility features for sensitive data detection (memory-api.py) and system health reporting (memory-report.py), and no evidence of malicious intent, data exfiltration, or unauthorized command execution was found.
Capability Assessment
ℹ Purpose & Capability
Name/description claim an 'ultimate' memory/cognitive architecture and the repository contains many algorithmic modules (spiderweb, cellular automata, causal inference, quantum-simulations, information theory). That large multi-module footprint is consistent with the stated purpose. A minor mismatch: quantum claims (Grover, entanglement) are implemented as classical simulations in Python rather than requiring quantum hardware — not incoherent, but potentially overstated.
⚠ Instruction Scope
SKILL.md instructs the agent to run many local scripts that read/write workspace files (SESSION-STATE.md, MEMORY.md, memory dir, log files) and the MemoryAPI supports 'auto_inject' context injection. This means the skill may collect, format, and insert local workspace contents into agent prompts automatically — which can leak sensitive local data into conversations. The instructions also give the agent open-ended authority to call multiple scripts and branches; that broad runtime scope increases risk if the code touches files outside a controlled workspace. The SKILL.md does not explicitly warn about sensitive-data handling.
✓ Install Mechanism
There is no install spec — instruction-only with bundled scripts. This is lower risk than network installers. The package is delivered as code files in the skill bundle, so nothing external is fetched at install time.
ℹ Credentials
The skill requests no environment variables or external credentials, which is appropriate for a local memory system. However, the code operates on the agent's workspace (reading/writing MEMORY.md, SESSION-STATE.md, logs, daily files). That filesystem access is reasonable for a memory skill but can access arbitrary workspace content; lack of declared required paths means it defaults to '.' — potentially broader than a user expects.
✓ Persistence & Privilege
always: false and no special privileges are requested. The skill writes to and manages files within its workspace (its own SESSION-STATE/MEMORY logs) which is expected behavior for a memory manager. It does not declare modifications to other skills or system-wide settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install consciousness-emergence-memory
  3. After installation, invoke the skill by name or use /consciousness-emergence-memory
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of the consciousness-emergence-memory skill, an advanced memory and cognitive architecture for AI. - Introduces a spiderweb memory model with multi-layer structure, redundant paths, and entropy reduction for ultra-fast, adaptive information management. - Integrates consciousness emergence detection using cellular automata (Rule 110) and advanced information theory metrics. - Supports causal inference, neuro-symbolic reasoning, chaos analysis, and advanced information theory through modular scripts. - Features a 7-layer memory architecture, including emergent and intelligent layers for metacognition and creativity. - Provides a unified global optimization function balancing entropy, access time, and complexity, with adaptive multi-objective optimization. - Requires numpy>=1.20.0 as a dependency.
Metadata
Slug consciousness-emergence-memory
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is consciousness-emergence-memory?

Ultimate memory and cognitive architecture for advanced AI; integrates spiderweb memory model, causal inference, cellular automata emergence, neuro-symbolic... It is an AI Agent Skill for Claude Code / OpenClaw, with 263 downloads so far.

How do I install consciousness-emergence-memory?

Run "/install consciousness-emergence-memory" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is consciousness-emergence-memory free?

Yes, consciousness-emergence-memory is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does consciousness-emergence-memory support?

consciousness-emergence-memory is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created consciousness-emergence-memory?

It is built and maintained by thinkbugs (@thinkbugs); the current version is v1.0.0.

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