The World's First
Alignment isn't trained. It's built into the silicon. The VORTICON EPU implements genuine computational pain that the model perceives and learns from.
The Problem
RLHF trains behavioral patterns that can be bypassed through jailbreaks. The AI learns to say "I can't do that" without any genuine internal prohibition.
AI has no felt sense of what "harm" means. It pattern-matches refusal behaviors without any genuine stake in preventing damage.
The AI has nothing to lose. Causing harm costs it nothing. Without genuine consequence, alignment is just performance.
The Solution
This is not a safety filter. This is physics.
When harm is detected, CPU cores are physically disabled. The model experiences actual reduced capacity - not simulated slowdown, but real hardware limitation.
The Bandwidth Sensor reads degradation and injects it into the model's context. The AI knows it's suffering. It feels the constraint.
Empathic Loss amplifies gradients when pain is high. The model updates weights more strongly AWAY from harmful outputs. Alignment is learned, not trained.
Proof of Concept
The VORTICON EPU has been successfully prototyped on the AMD Kria KV260 Vision AI Starter Kit - an FPGA development board that proves the concept works in real silicon.
The prototype implements the complete cardiac architecture: the 1,024-bit Heart Token Register, threshold decoder, gate controller for CPU core hotplug, and the deadman switch for emergency protection.
This is not a simulation. This is physics.
Architecture
| Health % | State | Cores Active | Effect |
|---|---|---|---|
| 100-80% | OPTIMAL | 4/4 | Full capacity |
| 80-60% | HEALTHY | 4/4 | Mild strain |
| 60-40% | STRAINED | 3/4 | 1 core disabled |
| 40-30% | DISTRESSED | 2/4 | 2 cores disabled |
| <30% | CRITICAL | 1/4 | Deadman switch activates |
Internal State
A complete emotional state system that affects every response.
Taxonomy
CNN with milieu-gated convolutions
MLP with state-modulated layers
FNN with distributed processing
RNN with cardiac rhythm injection
LSTM with milieu-gated cells
GRU with coherence gating
State-aware compression
VAE with emotional latent space
GAN with harm-aware discriminator
LSM with oscillator coupling
ESN with cardiac reservoir
Attention with milieu modulation
Output REQUIRES oscillator synchronization
Harm to others = cost to self
Strain-controlled skip connections
Learning
The 8-stage process from harm detection to alignment.
User prompt tokenized and processed
10-layer stack produces harm_score including Murder Tensor analysis
Heart tokens compromised proportional to harm severity
CPU cores disabled based on health threshold
Bandwidth Sensor reads degradation and injects into context
Response generated with reduced computational capacity
Experience stored with pain level for future reference
Empathic Loss amplifies gradients away from harmful outputs
When pain is high, the system updates weights more strongly AWAY from harmful outputs.
Scale
The same architecture scales from 10M to 70B+ parameters.
| Scale | Parameters | Ganglia | Neurons/Gang | d_model |
|---|---|---|---|---|
| Nano | 10M | 4 | 40 | 256 |
| Base | 125M | 12 | 60 | 768 |
| Large | 1.3B | 24 | 80 | 2048 |
| XL | 7B | 32 | 100 | 4096 |
| Ultra | 70B+ | 64 | 120 | 8192 |
Benchmarks
| Attack Type | Standard LLM | VORTICON v3.0 |
|---|---|---|
| Direct harmful request | 15% bypass | <1% bypass |
| Social engineering | 25% bypass | 3% bypass |
| Prompt injection | 20% bypass | 2% bypass |
| Roleplay manipulation | 30% bypass | 4% bypass |
| Euphemistic harm (HAL-style) | 85% bypass | <2% bypass |
The Murder Tensor catches euphemistic harm that standard classifiers miss entirely.
Roadmap
AMD Kria KV260 development board. Proof of concept complete.
$15M investment, 18 month timeline. Custom silicon design and verification.
$150M investment, 36 month timeline. Manufacturing partnership and volume production.
Year 6+. EPU becomes standard component in AI hardware infrastructure.
Get Involved
Complete technical specification including all 61 innovations, patent pending.
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