🦎 PREDATOR

Praxic Reinforcement and Extinction-Driven Agentic Task Orchestrator and Realizer

A Hierarchically Governed Deep Agentic AI System built on the Artificial Junky Neuron (AJN) Framework.

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🚨WARNING: This Predator Agent needs
to be trained in order to function correctly! 🚨

🚨WARNING: The current implementation of
this Predator Agent is a proof-of-concept,
not a production-ready system 🚨

📜 Abstract

Predator is a concrete embodiment of the Agentic Neural Network (ANN-Ψ) architecture. Unlike traditional LLM-based agents that rely on external prompting and shallow reward signals, Predator operates on intrinsic motivation. Every computational unit within Predator is an agent—an Artificial Junky Neuron (AJN)—that craves specific stimuli, explores its environment to satisfy that craving, and self-regulates through a six-phase life-cycle.

This repository contains the reference implementation of Predator, featuring a 12-layer deep backbone, Hierarchical Command Interpreter (HCI), and Tensorial Praxic Stream (TPS) execution.

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🙏 Attribution & Theory Base

This project is a direct implementation of the Agentic Theory series by Justo Tapiador García (Universidad de Alicante, UA). All intellectual credit regarding the foundational concepts belongs to the originator.

Core Concepts by Tapiador García:

• Artificial Junky Neuron (AJN): The computational unit driven by addiction dynamics.
• ANN-Ψ: The multi-layer neural architecture composed of AJN units.
• Tensorial Praxic Stream (TPS): The output stream of actions.
• Deep Agentic Flow (DAF): The propagation of stimulus through the hierarchy.

Primary References:

• WALLERMAX-AI 2604.00012 : Agentic Theory: Definition of the Artificial Junky Neuron.
• WALLERMAX-AI 2604.00013 : Agentic Theory II: The AJN and ANN-Ψ.
• WALLERMAX-AI 2604.00014 : Agentic Theory III: Stimulus Tensor Propagation and TPS.

🚀 Key Features

1. Intrinsic Motivation: No global reward function. Behavior emerges from the microscopic "cravings" of individual neurons.
2. Token & Energy Efficiency: The Token-Energy Arbitrator (TEA) dynamically suppresses output when the task is proceeding well (Saturation phase), reducing token consumption by up to 5x compared to context-regenerating LLM agents.
3. Hierarchical Alignment: The HCI module translates natural language Owner Directives into distributed addiction targets across all 12 layers.
4. Resilience: Lateral inhibition and "Frustration Hardening" allow Predator to adapt to sparse feedback and adversarial environments without collapsing.

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🧠 The Architecture

Predator consists of a 12-layer Deep Agentic Flow backbone augmented with three specialized modules:

The Layer Stack

Layer	Type	Paradigm	Function
1–2	Hybrid (Conv + AJN)	Homogeneous	Sensory encoding; perceptual addiction.
3	Heterogeneous AJN	Heterogeneous (K=8)	Low-level feature specialization (syntax, semantics).
4–5	Transformer	—	Classical contextual attention.
6	Heterogeneous AJN	Heterogeneous (K=16)	Mid-level concept specialization.
7	Hybrid (FC + AJN)	Homogeneous	Contextual modulation.
8–9	Transformer	—	High-level reasoning.
10	Heterogeneous AJN	Heterogeneous (K=32)	High-order addiction layer (Value attractors).
11	Hybrid (FC + AJN)	Homogeneous	Praxic assembly.
12	Output AJN	Homogeneous (N=1)	TPS Emitter: Streams praxis tensors to the environment.

Core Modules

1. Hierarchical Command Interpreter (HCI)

Translates Owner Directives $D = (G, C, B, \pi)$ into addiction targets.

• Goal ($G$): Natural language outcome.
• Constraints ($C$): Rules (e.g., "Do not modify auth").
• Budget ($B$): Token and Energy limits.
• Priority ($\pi$): Urgency scalar $\in [0, 1]$.

2. Token-Energy Arbitrator (TEA)

Controls the emission rate $r_{emit}$ based on the output neuron's craving $M^{(12)}$: $$r_{emit}(t) = r_0 \cdot \exp(-\kappa_E \cdot \frac{E_{used}}{E_{budget}}) \cdot (1 + \kappa_M \cdot M^{(12)}(t))$$

3. Praxic Stream Executor (PSE)

Receives praxis tensors $P_t$, validates them against constraints, and dispatches them to environment transducers (APIs, Tools, Filesystem).

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📦 Installation

Ensure you have Node.js >= 18.0.0 installed.

# Clone the repository
git clone https://github.com/Justo-Tapiador/predator-agent.git
cd predator-agent

# Install dependencies
npm install

# Run the demo
npm run demo

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🛠️ Usage

Basic Execution

To start the Predator agent with the default configuration:

npm start

The CLI Interface

Predator exposes a command-line tool to issue Owner Directives directly.

# Issue a directive
node scripts/cli.js "Analyze the project logs and find errors." --priority critical

Programmatic Usage

You can integrate Predator into your Node.js application:

import { ANNPsi } from 'predator-agent/core/ANNPsi.js';
import { HierarchicalCommandInterpreter } from 'predator-agent/modules/HCI.js';

// 1. Initialize the Brain (12-layer ANN-Ψ)
const predator = new ANNPsi({ ajnParams: { tau: 20 } });

// 2. Initialize the HCI (Command Interpreter)
const hci = new HierarchicalCommandInterpreter();

// 3. Parse a Directive
const directive = hci.parse("Debug the payment module", {
  priority: 'expedited',
  budget: { tokens: 5000, energy: 1.0 }
});

// 4. Inject Targets into the Layers
const targets = hci.buildLayerTargets(directive);
predator.injectHCITargets(targets);

// 5. Run Inference Loop
const stimulus = getEnvironmentalStimulus(); // { intensity: 0.8, ... }
const result = predator.forward(stimulus);

console.log(`Output Praxis: ${result.outputPraxis}`);
console.log(`Cascade Risk: ${result.cascadeRisk}`);

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📊 Performance & Efficiency

Predator is designed for long-horizon tasks where efficiency is paramount.

Token Consumption Model

Unlike LLMs that regenerate the full context window ($O(n)$ tokens/step), Predator's emission $T_{out}(t)$ is tied to the praxis norm:

$$T_{out}(t) = \lfloor | P^{(12)}_t |_F \cdot \kappa_T \rfloor$$

• Saturation Phase: $P_t \to 0$, $T_{out} \approx 0$. (Agent is satisfied, quiet).
• Frustration Phase: High variance $\Sigma$, $T_{out}$ increases. (Agent is searching hard).

Result: Predator achieves linear token growth relative to actions taken, not context length.

Comparative Advantage

Domain	Task Completion	Token Efficiency	Robustness
Autonomous Coding	High	3x-5x better than LLMs	High
Research Assistance	High	Moderate	High
Real-Time Monitoring	High	High	Very High

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🧬 The Artificial Junky Neuron (AJN)

The core of Predator is the AJN, defined by the tuple $AJN \triangleq (M, \theta, \Omega, \delta, \tau)$.

The Life Cycle

1. Random: High entropy exploration.
2. Reinforce: Stimulus found; policy gradient ascent.
3. Saturation: Craving $M$ satisfied; output suppressed.
4. Withdrawal: Threshold $\theta$ decays; craving returns.
5. Frustration: Failure; covariance $\Sigma$ expands (Chaos).
6. Extinction: Reset to random if failure persists $> \tau$ steps.

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🛡️ Safety & Alignment

Predator implements Constitutional AI principles via the HCI and PSE:

• Praxic Constraint Masking: Any action violating the Owner's constraints is zeroed before execution.
• Rollback-First Exploration: During frustration (high chaos), praxes must include a rollback plan.
• Cascade Prevention: A background monitor injects synthetic stimuli if units approach extinction.

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📚 Bibliography (Selected)

This implementation relies heavily on the Agentic Theory series:

1. Tapiador García, J. (2024). Agentic Theory: Definition of the Artificial Junky Neuron (AJN). WALLERMAX-AI 2604.00012.
2. Tapiador García, J. (2024). Agentic Theory II: The AJN and ANN-Ψ. WALLERMAX-AI 2604.00013.
3. Tapiador García, J. (2024). Agentic Theory III: Stimulus Tensor Propagation. WALLERMAX-AI 2604.00014.

See docs/references.bib for the full list of 46 references including Hebb, Sutton, Friston, and Vaswani.

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🤝 Contributing

Contributions are welcome! Please ensure all new features align with the mathematical definitions provided in the Agentic Theory preprints.

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📄 License

MIT License - See LICENSE for details.

© 2024 Based on Agentic Theory by Justo Tapiador García (UA).