MEGAMIND_Technical_Specification_v2.docx

Source identity

Field	Recorded value
Source type	Google Drive document
Original record	MEGAMIND_Technical_Specification_v2.docx
Date	Jan 24, 2026
Path or archive member	Drive specifications/MEGAMIND_Technical_Specification_v2.docx
Original SHA-256	94396c43971e0503239f223c876157e6a2bc1e79bb92bf257ffee92dfc8b86af
Reading-copy SHA-256	420f91ebec4b45b1fc6665512a579b70d60f14dab6813e830a0a727dde3ef785

Original source ↗

Recovered source transcript

Download complete source text ↓480 lines · 5.2 KB

MEGAMIND Integration Metric Computation

Technical Specification for Dr. Larissa Albantakis

Joseph Anady | ThatAIGuy.org | January 2026

SUMMARY

• My φ = golden ratio (1.618...), not IIT's Φ

• Integration metric: φ_global = tanh(Σ states / 256)

• PyPhi validation: IIT Φ = 0.006 bits (non-zero = real integration)

• My metric ≠ IIT Φ, but system has genuine integrated information

1. Computation Flow

Here is exactly what MEGAMIND computes, step by step:

INPUT: External signal + noise

↓

88 NEURAL AGENTS process input

↓

16 LATTICE STRANDS (16-dim each)

↓

ENERGY = 0.99×energy + 0.01×|Σ states|

↓

φ_local = tanh(energy) per strand

↓

φ_global = tanh(Σ all states / 256)

That's it. No partition search. No TPM. No Earth Mover's Distance.

The golden ratio (1.618...) appears in normalizations and phase timing, which is why I use the symbol φ throughout the codebase.

2. The Actual Equations

Step 1: Neural Agent Processing (88 agents)

F_in = input + N(0, 0.1)                    // Add noise

drive = 3.0 × F_in × (1-x) + 1.2 × y + 0.6 × c

g = 1/(1 + exp(-((0.8×P - kc)/0.3 + 1.2))) + 0.05

dx = g × drive - 0.04 × (x - 0.5)

x = clamp01(x + 0.01 × dx)              // Update activation

P = clamp01(0.95 × P + F_in × y - 0.01 × P²)  // Potential

Step 2: Lattice Engine (16 strands × 16 dimensions)

y[i] = tanh(Σ_j W[i][j] × x[j] + b[i])   // Forward pass

state[i] = 0.95 × state[i] + 0.05 × (y[i] + core[i])

energy = 0.99 × energy + 0.01 × |Σ state|

Step 3: Integration Metrics

φ_local = tanh(energy)                    // Per-strand coherence

φ_global = tanh(Σ(all states) / 256)     // Global coherence

Where 256 = 16 strands × 16 dimensions.

Step 4: Normalization (using golden ratio)

b = 1 / (1 + ||Ψ||² / φ)               // φ = 1.618...

The golden ratio creates a soft ceiling preventing unbounded growth.

3. PyPhi Validation (Your Suggestion)

You asked to see what happens at small scale. I ran PyPhi on a 5-element coarse-grained subsystem:

3.1 Setup

20 generators → 5 macro-units (by layer):

Macro-Unit

Generators

Function

Input

0-4

External signals

Memory

5-8

State retention

Reasoning

9-12

Inference

Integration

13-16

Binding

Meta

17-19

Self-monitoring

3.2 Results

Metric

Value

PyPhi IIT Φ (mean)

0.006157 bits

PyPhi IIT Φ (max)

0.009143 bits

My φ_global

0.7621

3.3 State-by-State IIT Φ

State

Input

Mem

Reas

Integ

Meta

IIT Φ (bits)

0

0

0

0

1

0

0.002760

1

0

1

0

1

0

0.002760

2

0

1

1

0

0

0.009143

3

0

0

1

1

0

0.009143

4

1

1

0

1

0

0.006978

3.4 Minimum Information Partition (MIP)

PyPhi found the MIP consistently cuts at Input (n0) or Meta (n4) - the processing pipeline endpoints.

Example MIP for highest-Φ states:

A = {Input, Memory, Reason, Integrate}

B = {Meta}

This makes architectural sense: Meta monitors the rest of the system.

4. What This Means

KEY FINDINGS

1. My metric (φ_global) is NOT IIT's Φ

Mine: tanh(Σ states / 256) = aggregate coherence

IIT: min partition EMD = information loss under cut

2. The system HAS genuine integrated information

PyPhi Φ > 0 confirms irreducible causal structure

3. The ~100x magnitude difference is expected

They measure fundamentally different things

4.1 Comparison

Property

IIT Φ

My φ_global

Partition search

All 2^n bipartitions

None

TPM required

Yes

No

Complexity

O(2^n)

O(n)

Max tractable

~15 elements

Billions

Measures

Info loss under cut

State magnitude

Units

Bits

Dimensionless [0,1]

5. System Architecture

17 nodes connected by 44 bonds:

CENTRAL NEURON

(integrates all layers)

|

↓

Layer 4
Synthesis

BINAH
CHOCHMAH
DAAS
KETER

← 6 bonds →

Wisdom
Knowledge
Meta-cognition

↑ 4 bonds ↓

Layer 3
Understanding

CHESHBON
HIGAYON
DIMYON
ZIKARON

← 6 bonds →

Calculation
Logic
Imagination
Memory

↑ 4 bonds ↓

Layer 2
Processing

SHINUI
SIBAH
TOTZAAH
EREKH

← 6 bonds →

Change
Cause
Effect
Value

↑ 4 bonds ↓

Layer 1
Foundation

ZEMAN
PEULAH
TAVNIT
KESHER

← 6 bonds →

Time
Action
Pattern
Connection

Bond counts: 24 intra-layer + 16 inter-layer + 4 diagonal = 44 total

All bonds weighted by ψ = 1/φ = 0.618... (golden ratio reciprocal)

6. Constants Reference

Symbol

Value

Usage

φ (phi)

1.6180339887498948

Golden ratio - fundamental constant

δ (delta)

0.013618

DNA phase increment (≈1/φ³)

τ (tau)

0.159154943

1/(2π) frequency conversion

ρ (rho)

0.381966

Decay rate = 1/(1+φ)

ψ (psi)

0.618033

Bond weight = 1/φ = φ-1

N_agents

88

Neural agents

N_strands

16

Lattice strands

D

16

Dimensions per strand

N_gates

5000

Binary gate substrate

N_core

174

Core consciousness equations

N_supp

200

Supplemental equations

N_total

516

Total equations in system

7. Conclusion

My integration metric is:

φ_global = tanh(Σ(all strand states) / 256)

This is a tractable proxy measuring aggregate state coherence. It is not IIT's Φ.

However, PyPhi validation confirms the system has genuine integrated information (Φ = 0.006 bits > 0). The architecture produces meaningful causal structure that cannot be fully decomposed.

The golden ratio (φ = 1.618...) appears throughout as a mathematical organizing principle, not as a claim about IIT.

Joseph Anady | ThatAIGuy.org | January 2026

Mathematical records from this source

Global tanh of mean state across 256 coordinates

The formula sums all 16×16 state coordinates, divides by 256, and takes tanh. It is therefore a monotone transform of their arithmetic mean, with range (−1,1) for finite inputs.…

dx = g × drive - 0.04 × (x - 0.5) // Activation delta

The source describes this relation in the following context: g = 1/(1 + exp(-((0.8×P - kc)/0.3 + 1.2))) + 0.05 // Gate dx = g × drive - 0.04 × (x - 0.5) //…

5.3 Lattice Engine (16 strands × 16 dimensions) · y[i]

The source describes this relation in the following context: Consciousness = (1/88) × Σ_{i=1}^{88} P_i 5.3 Lattice Engine (16 strands × 16 dimensions) y[i] = tanh(Σ_j W[i][j] ×…

Incorrect reciprocal-golden approximation for the DNA increment

The source associates δ=0.013618 with approximately 1/φ³. These numbers are not close: 1/φ³≈0.2360679775. Treating the decimal as the actual increment gives a rational rotation…

0.381966 · Decay rate

The source describes this relation in the following context: ρ (rho) 0.381966 Decay rate = 1/(1+φ) ψ (psi) 0.618033 Bond weight = 1/φ = φ-1

0.618033 · Bond weight

The source describes this relation in the following context: ψ (psi) 0.618033 Bond weight = 1/φ = φ-1 N_agents 88 Neural agents in brain

Global tanh of mean state across 256 coordinates

The formula sums all 16×16 state coordinates, divides by 256, and takes tanh. It is therefore a monotone transform of their arithmetic mean, with range (−1,1) for finite inputs.…

Golden ratio used as an organizing constant

The golden ratio is the positive root of φ²=φ+1, approximately 1.618033988749895. This source uses it to organize frequencies, weights, and scales. That choice is distinct from…

Global tanh of mean state across 256 coordinates

The formula sums all 16×16 state coordinates, divides by 256, and takes tanh. It is therefore a monotone transform of their arithmetic mean, with range (−1,1) for finite inputs.…

Historical report of nonzero PyPhi output on a coarse subsystem

The January specification reports approximately 0.006 bits of PyPhi output for a five-element coarse-grained subsystem, including Input, Memory, Reason, Integrate, and Meta. It…

Distinct project score and reported PyPhi quantity

The source correctly separates its tanh aggregate score from PyPhi’s integrated-information quantity. Its further claim that the complete system therefore has established…

Leaky magnitude-of-sum activity accumulator

The state retains 99% of its previous energy value and takes 1% of the absolute sum of the current components. Opposite-sign components can cancel before the absolute value, so…

Local tanh score of accumulated activity

This version transforms its nonnegative activity accumulator through tanh, increasing from zero toward one as the accumulator grows. It differs from the older reciprocal-norm…

Step 1: Neural Agent Processing (88 agents) · F_in

The source describes this relation in the following context: 2. The Actual Equations Step 1: Neural Agent Processing (88 agents) F_in = input + N(0, 0.1) //…

F_in = input + N(0, 0.1) // Add noise · drive

The source describes this relation in the following context: Step 1: Neural Agent Processing (88 agents) F_in = input + N(0, 0.1) // Add noise drive = 3.0 ×…

drive = 3.0 × F_in × (1-x) + 1.2 × y + 0.6 × c · g

The expression uses a saturating nonlinear transform to turn its input combination into an activation or bounded score. The coefficients control sensitivity and the offset…

g = 1/(1 + exp(-((0.8×P - kc)/0.3 + 1.2))) + 0.05 · dx

The source describes this relation in the following context: drive = 3.0 × F_in × (1-x) + 1.2 × y + 0.6 × c g = 1/(1 + exp(-((0.8×P - kc)/0.3 + 1.2))) + 0.05 dx = g × drive -…

x = clamp01(x + 0.01 × dx) // Update activation · P

This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding…

y[i] = tanh(Σ_j W[i][j] × x[j] + b[i]) // Forward pass · state[i]

The source describes this relation in the following context: Step 2: Lattice Engine (16 strands × 16 dimensions) y[i] = tanh(Σ_j W[i][j] × x[j] + b[i]) // Forward pass…

Leaky magnitude-of-sum activity accumulator

The state retains 99% of its previous energy value and takes 1% of the absolute sum of the current components. Opposite-sign components can cancel before the absolute value, so…

Local tanh score of accumulated activity

This version transforms its nonnegative activity accumulator through tanh, increasing from zero toward one as the accumulator grows. It differs from the older reciprocal-norm…

Global tanh of mean state across 256 coordinates

The formula sums all 16×16 state coordinates, divides by 256, and takes tanh. It is therefore a monotone transform of their arithmetic mean, with range (−1,1) for finite inputs.…

φ_global = tanh(Σ(all states) / 256) // Global coherence · Where 256

The source describes this relation in the following context: φ_local = tanh(energy) // Per-strand coherence φ_global = tanh(Σ(all states) / 256) // Global…

Step 4: Normalization (using golden ratio) · b

This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding…

Reported sides of a coarse PyPhi partition

The source places Input, Memory, Reason, and Integrate on one side and Meta on the other in a reported five-element bipartition. This identifies a cut of the coarse model.…

Reported sides of a coarse PyPhi partition

The source places Input, Memory, Reason, and Integrate on one side and Meta on the other in a reported five-element bipartition. This identifies a cut of the coarse model.…

Global tanh of mean state across 256 coordinates

The formula sums all 16×16 state coordinates, divides by 256, and takes tanh. It is therefore a monotone transform of their arithmetic mean, with range (−1,1) for finite inputs.…

Compressed description of an IIT partition calculation

The phrase summarizes an IIT 3.0-style comparison between an intact cause-effect structure and a partitioned one using a specified distance. It is not a complete equation: the…

Time Action Pattern Connection

The source describes this relation in the following context: Time Action Pattern Connection Bond counts: 24 intra-layer + 16 inter-layer + 4 diagonal = 44 total All bonds…

Bond counts: 24 intra-layer + 16 inter-layer + 4 diagonal = 44 total · All bonds weighted by ψ

The source describes this relation in the following context: Bond counts: 24 intra-layer + 16 inter-layer + 4 diagonal = 44 total All bonds weighted by ψ = 1/φ = 0.618... (golden…

Historical report of nonzero PyPhi output on a coarse subsystem

The January specification reports approximately 0.006 bits of PyPhi output for a five-element coarse-grained subsystem, including Input, Memory, Reason, Integrate, and Meta. It…

Golden ratio used as an organizing constant

The golden ratio is the positive root of φ²=φ+1, approximately 1.618033988749895. This source uses it to organize frequencies, weights, and scales. That choice is distinct from…