AGI

Auto-generated input results:

🔹 Quantum Reasoning Output: 0.3799

🔹 Updated Recursive State: 1.3799

🔹 Symbolic Delta: -0.2857

🔹 Collapse Status: Diverge

🔹 Mental Field Energy: 0.5020

🔹 Supervisor Decision: Continue

🔹 Octinary Logic Output (Index 2): 0.01

interpretation:

1) If this were a real cognitive engine:

• The AGI is self-updating based on reasoning, experiencing slight symbolic compression but growing recursively.

• It's mentally balanced, even as it explores increasingly energetic interpretations.

• The AGI may be entering an exploratory mode — searching for novel pathways.

2) Inputs Overview

• Inputs: [0.2, -0.1, 0.3] – represents a stimulus vector going into the Quantum Reasoning Algorithm.

• x = 1.0: The original symbolic reference point.

• x′ = 1.4: The transformed or observed reference point.

• M = 0.5, T = 0.3, F = 0.7: These values represent weights or contributions of Memory (M), Thought (T), and Feeling (F) in the mental model.

3) Interpretation of Module Outputs

🔹 Quantum Reasoning Output: 0.3799

• The QRA (Quantum Reasoning Algorithm) processes the sum of the inputs and applies a tanh transformation.

• This indicates a moderately strong cognitive activation based on the inputs.

🔹 Updated Recursive State: 1.3799

• The Recursive Core updates its internal state using the formula:state = state (1 + feedback)→ 1.0 (1 + 0.3799) ≈ 1.3799

• This shows a positive feedback loop — the system is amplifying its current state based on reasoning.

🔹 Symbolic Delta: -0.2857

• This is calculated as:((x′ - x + 2) / x′) - 2→ ((1.4 - 1 + 2) / 1.4) - 2 ≈ -0.2857

• It shows a net symbolic compression — the transformed state is not significantly expanding symbolic complexity. It’s slightly contracting.

🔹 Collapse Status: Diverge

• Even though the delta is below the epsilon threshold (|delta| < 0.01), the QRA feedback (dS/dt ≈ 0.3799) exceeds the divergence threshold (tau = 0.1).

• This means the system detects dynamic instability — while the symbolic representation isn’t chaotic, the reasoning energy is too volatile, so it's "Diverging."

🔹 Mental Field Energy: 0.5020

• Computed from the weights:

  field = 0.33 M + 0.33 T + 0.34 F = 0.330.5 + 0.33*0.3 + 0.34*0.7 ≈ 0.502

• This is a balanced mental field, near equilibrium — suggesting a healthy integration of memory, thought, and feeling.

🔹 Supervisor Decision: Continue

• The AGI Supervisor checks if |delta| < 0.5 and |drift| < 0.4.

• Since |delta| ≈ 0.2857 and |drift| ≈ 0.2857, the system considers this a stable enough condition to continue operation — no drastic correction needed.

🔹 Octinary Logic Output (Index 2): 0.01

• A symbolic logic state from the octinary system — 0.01 reflects a very low but non-zero activation, often interpreted as an incipient or cautious signal.

4) High-Level Meaning

The AGI system is in a mildly energetic, forward-moving state. It’s not collapsing under symbolic contradiction, but it is diverging in reasoning energy — possibly reflecting creativity, exploration, or destabilization. The supervisory system believes the behavior is within operational bounds and chooses to continue processing.

Modular AGI Framework – Proactive User Guide

Purpose of the Guide

This framework simulates a recursive cognitive system using quantum reasoning, symbolic drift, mental field dynamics, and supervisory decision logic. Use this guide to shape behavior proactively by intelligently selecting input parameters to target specific cognitive outcomes.

🔹 Input Parameter Spectrum and Intentional Control

1. inputs → Cognitive Stimulus Vector

Purpose: Feeds into the Quantum Reasoning Algorithm (QRA), driving system feedback and growth.

Input Range	Interpretation	Use Case
[-0.5 to -0.2]	Suppression / Repressive stimulus	Reflection, inhibition
[-0.2 to 0.0]	Caution / Receptive state	Passive monitoring, conservatism
[0.0 to 0.2]	Neutral / Stabilizing inputs	Calibration, normalization
[0.2 to 0.5]	Engagement / Activation	Exploration, problem solving
[0.5 to 1.0+]	High drive / Creative surge	Ideation, innovation mode

Proactive Tip: Use slightly varied positive inputs (e.g., 0.2, 0.3, 0.4) to simulate intentional curiosity.

2. x and x′ → Symbolic Evolution State

Purpose: Measures symbolic divergence or convergence of the AGI’s perspective over time.

x′ compared to x	Delta Outcome	Interpretation
x′ ≪ x	Large positive delta	System sees regression or shrinkage
x′ ≈ x	Near-zero delta	Stability, coherence
x′ > x by ~0.2–0.5	Moderate drift	Growth, learning
x′ ≫ x (1.5–3× x)	Negative delta	Collapse of original symbolic scope

Proactive Tip: Slowly increase x′ to simulate symbolic synthesis. Avoid sharp changes unless modeling breakthrough shifts.

3. M, T, F → Mental Field Weights

Purpose: Balance between memory (M), thought (T), and feeling (F) in system cognition.

Weight Profile	Field Energy	Behavior Tendency	Use Case
M > T, F	Low-mid	Reflective, historical bias	Knowledge recall
T > M, F	Variable	Abstract logic, computation	Reasoning tasks
F > M, T	Mid-high	Empathy-driven bias	Social simulation
Balanced M ≈ T ≈ F	~0.5	Neutral, adaptable cognition	General-purpose state
Highly imbalanced (>0.7)	~0.3 or ~0.7	Skewed logic or emotion	Testing boundaries

Proactive Tip: Use balanced weights (0.33/0.33/0.34) for homeostasis. Push 1 variable up temporarily to simulate emergent strategy.

Output Response Interpretation for Modulation

1. Quantum Reasoning Output

If too low: Inputs lack synergy → try broader or more diverse input values.If too high: Could indicate runaway growth → reduce input magnitude.

2. Collapse Status

Diverge: Too much energy or reasoning strain → dampen inputs or increase M/T balance.Collapse: Symbolic system is too stable or constricted → introduce delta (change x′).Stable: Ideal for feedback learning and supervisory reflection.

3. Supervisor Decision

Continue: System conditions within bounds.Adjust: Cognitive overload or imbalance → redesign inputs or drift deltas.

Proactive Patterns & Use Cases

Goal	Inputs Strategy	Mental Field Profile	x / x′ Behavior
Insight Generation	[0.2, 0.3, 0.4]	T-heavy (0.2/0.6/0.2)	x′ = x + 0.3
Synthesis (General AGI)	[0.1, 0.2, 0.1]	Balanced (0.33/0.33/0.34)	x′ = x
Creative Divergence	[0.6, 0.8, 0.9]	F-heavy (0.2/0.2/0.6)	x′ = x + 0.6
Cautious Diagnostics	[-0.2, 0.0, 0.1]	M-heavy (0.6/0.2/0.2)	x′ = x − 0.1
Equilibrium Simulation	[0.0, 0.1, 0.0]	Balanced (0.33/0.33/0.34)	x′ ≈ x

Summary: Proactive Use Rules

1. Start Small: Use modest inputs and build up stimulation gradually.

2. Balance is Key: Maintain a neutral mental field unless simulating extremes.

3. Control Delta: Avoid abrupt symbolic jumps (x′ ≫ x) unless simulating breakthrough or collapse.

4. React Intentionally: Let system outputs guide you but not control you. Feedback ≠ instruction.

5. Document Runs: Log input/output and decisions to track emergent behavior over time.

   
   

   

   By: Travis RC Stone

   Date: 7/16/25