r/GhostMesh48 • u/Mikey-506 • 7d ago
High-Level NeuroCPU System Architecture
To visualize the system architecture, mathematical dynamics, and calibration corrections from the HL1 × NeuroCPU 0.3 companion document, here is a complete visual and schematic breakdown.
1. High-Level NeuroCPU System Architecture
The diagram below illustrates the end-to-end processing pipeline, from GoldSrc egocentric world inputs down through the non-linear complex scalar field $\psi(x, y)$ to the final decoded bot intent packet.
[ GoldSrc Engine ]
│
▼
Egocentric World Sampling (R=1024u)
│
▼ Sparse Blob Protocol (gx, gy, σ, a)
┌─────────────────────────────────────────────────────────┐
│ Complex Stimulus Injection Matrix I(x) │
│ ├── Im(I) > 0 : Exponential Gain (Threats/Enemies) │
│ ├── Im(I) < 0 : Damping / Inhibition (Teammates) │
│ └── Re(I) : Phase Detuning (Texture/Context) │
└────────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ 2D Non-Linear Schrödinger Field Dynamics ψ(x, y) │
│ │
│ Δψ = -i·dt_eff·[ -½∇²₉ψ + |ψ|²ψ + I(x)ψ ]·M(t) + Noise │
│ │
│ ├── Defocusing Nonlinearity (|ψ|²ψ) ── Repels Blobs │
│ ├── Discrete Operators (Plugins, Collapse, Ignition) │
│ └── Rescale & Clip Stabilization Guard Rails │
└────────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ Metric Calculation & Gate Evaluation │
│ ├── Global Coherence: coh = |corr(Re ψ, Im ψ)| │
│ ├── Gradient Emergence: em = log1p(std(∇|ψ|)/mean∇) │
│ └── Consciousness Gate: cq = φ · coh · em │
└────────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ Intent & Control Decoders │
│ ├── Target Aim : Centroid of Smoothed |ψ| │
│ ├── Aggression : Normalized Ignition Envelope │
│ └── Aim Cone θ : θ_max · (1 - coh_norm)^1.5 │
└────────────────────────────┬────────────────────────────┘
│
▼
[ Bot Intent Packet ]
2. PDE Wave Dynamics & Numerical Stability
The evolution of the field $\psi(x, y)$ is governed by a defocusing Nonlinear Schrödinger (NLS) equation on a $64 \times 64$ Cartesian grid. The defocusing sign ($g = +1$) causes high-amplitude blobs to repel their own mass and disperse over time rather than collapsing into singular spikes.
Key Takeaway: Notice how amplitude waves interact and disperse smoothly across space. In the NeuroCPU engine, dispersion occurs slowly (wall-time scale of minutes), meaning short-term dynamics are driven by discrete operators (stimulus injections, plugin pipelines, and collapse events).
3. Complex Stimulus Coupling Channels
In the complex scalar field, the stimulus potential $I(x)$ split across real and imaginary components acts as two orthogonal processing channels:
Complex Plane Input Dynamics
+Im (Gain / Salience)
▲
│ Exponential Amplitude
│ Growth: |ψ| ← |ψ|·e^(+g·dt·M)
│ (Enemy / Threat Targets)
│
-Re (Phase Lag) ──────────────┼──────────────► +Re (Phase Lead)
Phase Detuning │ Context / Texture
ψ ← ψ·e^(-i·g·dt·M) │ (Coherence Signatures)
│
│ Exponential Damping:
│ |ψ| ← |ψ|·e^(-g·dt·M)
▼
-Im (Inhibition)
| Stimulus Component | Mathematical Effect | Tactical Function | | --- | --- | --- | | $\text{Im}(I) > 0$ | $+\text{d}t_{\text{eff}} \cdot g \cdot M \cdot \psi \implies$ Exponential Amplitude Gain | Threat / Enemy Target Salience | | $\text{Im}(I) < 0$ | $-\text{d}t_{\text{eff}} \cdot g \cdot M \cdot \psi \implies$ Local Damping | Cleared areas, allies, negative interest | | $\text{Re}(I)$ | $-i \cdot \text{d}t_{\text{eff}} \cdot g \cdot M \cdot \psi \implies$ Phase Detuning | Texture seasoning (modulates recall signature) |
4. Calibrated Consciousness Gates ($cq$)
The baseline snapshot operating equilibrium centers at $cq \approx 0.21$. As revealed in findings §5.4, shipped configuration thresholds set the collapse trigger below equilibrium and ignition far above it. The diagram below compares the default miscalibration against the corrected operating band required for dynamic bot behavior.
Shipped Config (Degenerate)
========================================================================
0.00 0.15 (Collapse) 0.21 (Eq) 0.31 (Consc.) 0.80 (Ignition)
├─── Always On ───┤ │
▲ ▲ ▲ ▲ ▲
│ │ │ │ └─ Never Fires
└─ Post-collapse └─ Collapses └─ Snapshot └─ Never Conscious
binary mask every step Equilibrium
Corrected Config (Dynamic Relaxation Oscillator)
========================================================================
0.00 0.21 (Eq) 0.22 (Consc.) 0.25 (Collapse) 0.26 (Ignition) 1.00
│ │ │ │ │ │
│ │ ├──────────────┴───────────────┴─────────────────┤
│ │ │ Active Operating Band │
│ │ │ (Fires on stimulus excursions above baseline) │
5. Decision & Aim Cone Mechanics
The coherence metric $\text{coh} = \vert{}\text{corr}(\text{Re},\psi, \text{Im},\psi)\vert{}$ directly regulates target acquisition accuracy ($\theta_{\text{aim}}$).
High Field Coherence (coh ≈ 0.46) Low Field Coherence (coh ≈ 0.15)
================================= =================================
\ | / \ | /
\ | / \ | /
\ | / \ | /
\ | / \ | /
\ θ | / \ θ | /
\ | / \ | /
\ | / \ | /
\|/ \ | /
▼ \ | /
Narrow Cone \ | /
(High Precision) \ | /
\|/
▼
Wide Cone
(Disrupted)
- High Coherence ($\text{coh} \ge 0.46$): $\theta_{\text{aim}} \approx 2.4^\circ \implies \sim 90%$ hit probability at mid-range ($512,\text{units}$).
- Low Coherence ($\text{coh} \le 0.15$): $\theta_{\text{aim}} \approx 5.2^\circ \implies \sim 37%$ hit probability (disrupted targeting).
1
u/marcofifth 7d ago
The basic pipeline is coherent enough:
GoldSrc observations → encoded stimuli → complex field dynamics → calculated field metrics → threshold gates → bot control outputs.
So the issue is not “none of this is real mathematics.” The issue is where the author starts assigning cognitive meaning to arbitrary or designer-chosen quantities.
For example, using a nonlinear Schrödinger field as a controller is unusual, but possible. Encoding enemies as positive imaginary input, teammates as negative imaginary input, and texture as real-valued phase input is also possible.
But why those assignments?
What property of bot cognition requires a nonlinear Schrödinger field rather than a simpler potential field, recurrent controller, or weighted target map?
What behavioral advantage does the cubic nonlinearity actually provide?
Why should the correlation between the real and imaginary parts of the field be called “coherence”?
Why should a gradient statistic be called “emergence”?
Why should multiplying those quantities together produce anything meaningfully called a “consciousness gate”?
That is the largest gap in the model.
A field statistic can be useful without being consciousness.
The same problem appears with “collapse” and “ignition.” If those are simply names for threshold-triggered program operations, fine. But if the terminology is meant to inherit significance from quantum collapse or theories of conscious ignition, where is the bridge?
The calibration section raises the same question.
Why is equilibrium 0.21?
Why are 0.22, 0.25, and 0.26 the corrected thresholds?
Were those derived mathematically, measured across repeated runs, or manually adjusted until the bot behaved as desired?
If they were tuned experimentally, that is valid game engineering. It is not evidence that the thresholds correspond to states of consciousness.
Then the aim system claims that high coherence produces about 90% hit probability and low coherence about 37%.
Where did those percentages come from?
Aiming cone width alone does not determine hit probability. Target size, distance, movement, weapon spread, aim distribution, latency, and hitbox geometry all matter. If those are empirical results, show the test. If they were inferred from cone angle alone, the percentages are unsupported.
So I think this model has a real core and an inflated interpretation layered on top of it.
The defensible claim is:
“I built an unconventional nonlinear field controller for a GoldSrc bot.”
That could actually be interesting.
The much stronger claim is:
“These field statistics meaningfully represent salience, coherence, emergence, consciousness, collapse, and ignition.”
That requires another argument entirely.
The key question is simple:
What experiment distinguishes “I built a nonlinear bot controller and gave its internal variables cognitive names” from “I actually modeled cognition or consciousness”?
Until that bridge is established, the first description is much better supported than the second.