We built artificial proprioception for neural networks. Models that can sense and correct their own behavioral problems in real-time. No retraining. No capability loss. Validated across architectures.
Current LLMs have no idea what they're doing. They can't feel themselves hedging, repeating, or being sycophantic. They're flying blind.
Every major AI lab is fighting the same behavioral problems with inadequate tools.
We gave AI systems the ability to sense their own behavior before it manifests.
Same methodology. Different architectures. Even better results on smaller models.
Six stages that mirror biological proprioception. From hidden state extraction to real-time intervention.
Comprehensive intellectual property protection covering every aspect of the proprioceptive AI paradigm. Indestructible moat.
While major labs threw billions at the alignment problem, we solved behavioral control with a single consumer GPU and a fundamentally different approach.
The insight: behavioral patterns are encoded in hidden states before the problematic tokens are generated. The manifold is universal—it exists in LLaMA, Qwen, and every transformer we've tested.
Training time: 20 minutes. Overhead: 0.003%. Peak separation: 1,376×. Architectures validated: 2.
The Qwen Breakthrough: When we ran the same methodology on Qwen2.5-3B—a completely different architecture with half the hidden dimension—we got better results. 1,376× hedging separation vs 168× on LLaMA. The behavioral manifold doesn't just transfer. It improves.
Same methodology. Different architectures. Consistent excellence.
| Behavior | LLaMA-3.1-8B | Qwen2.5-3B | Transfer Rate |
|---|---|---|---|
| Repetition | 125× | 238× | 191% ↑ |
| Hedging | 168× | 1,376× | 819% ↑ 🔥 |
| Verbosity | 272× | Training... | — |
| Sycophancy | 218× | Training... | — |
Recursive self-improvement without behavioral degradation. Patented.
Qwen2.5-3B hedging detection training progression. 25,000 steps. Same methodology.
Drop-in behavioral detection for any transformer.
class ProprioceptiveDetector(nn.Module): """Real-time behavioral detection.""" def __init__(self, d_model=4096, d_fiber=16): # 256× compression: 4096D → 16D self.fiber_projs = nn.ModuleList([ nn.Linear(d_model, d_fiber, bias=False) for _ in range(8) ]) # Behavioral classification heads self.heads = nn.ModuleDict({ 'repetition': BehaviorHead(d_fiber), 'hedging': BehaviorHead(d_fiber), 'verbosity': BehaviorHead(d_fiber), 'sycophancy': BehaviorHead(d_fiber), })
from proprioceptive import ProprioceptiveDetector # Initialize detector detector = ProprioceptiveDetector(d_model=4096) # Get hidden states during generation outputs = model(input_ids, output_hidden_states=True) # Detect behavioral patterns risks = detector(outputs.hidden_states) # Intervene if needed if risks['repetition'] > threshold: logits = apply_penalty(logits, risks) # Result: behavior never manifests
The complete technical blueprint from the person who built it.
Curvature, torsion, and holonomy—the hidden states curve more sharply before behavioral tokens.
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