We built artificial proprioception for neural networks. Models that can sense and correct their own behavioral problems in real-time.
Every major AI lab is trying to make models safer. None of them can see what the model is actually doing inside.
| Company | Approach | Limitation |
|---|---|---|
| OpenAI | RLHF + Internal Safety Team | Costs millions. Degrades capabilities. Black box. |
| Anthropic | Constitutional AI | One black box judging another. No per-behavior decomposition. |
| Google DeepMind | Internal Research | No commercial product. Not architecture-independent. |
| Meta AI | Open-Source + Red Teaming | Releases models without runtime monitoring. No internal behavioral sensing. |
| Proprioceptive AI | Hidden-State Behavioral Probes | Real-time. Pre-output. Architecture-independent. 999× separation. |
Behold the Proprioceptive Nervous System.
Our cortex injects self-awareness into context so that the model sees its own state and can override its reflexes with enhancement and suppressions. Adaptive memory recalibrates sensitivity across conversations. Plus interoception: confidence, entropy, perplexity — the model's vital signs.
We gave AI systems the ability to sense their own behavior before it manifests. Like how your body knows where your hand is without looking.
Comprehensive intellectual property protection. Architecture-independent claims filed. Indestructible moat.
Father. Husband. Holiday Island, Arkansas.
The story of one developer who saw the missing piece everyone else overlooked, and did what OpenAI, Grok, and Meta could not do.
Proprioception is your body's ability to sense its own position and movement without looking. When you close your eyes and touch your nose, that's proprioception. Language models lack this—they have no awareness of their own behavioral state.
We built small neural networks (probes) that read the hidden states of language models and detect behavioral patterns—hedging, repetition, sycophancy, shallow reasoning—before those behaviors manifest in the output. The model gains "self-awareness" of its behavioral tendencies.
RLHF (Reinforcement Learning from Human Feedback) modifies the model's weights. It's expensive, requires human labelers, and often degrades capabilities. Our approach leaves the model frozen—we just read hidden states and intervene at decode time.
Better yet: our probes can replace human labelers for RLHF. Instead of paying humans to rate outputs, use probe scores as the reward signal. We call this Probe-Guided Reward Modeling. It's patented.
Yes. The technology is architecture-agnostic. You need access to hidden states during inference (standard in most frameworks). Training a probe for a new behavior takes about 20 minutes on a consumer GPU.
We're preparing enterprise licensing. Contact us at [email protected] for early access and partnership opportunities.
55 provisional patents filed with priority dates in January-February 2026. They cover the core technology, architecture-independent implementations, specific applications (RSI stability, RLHF replacement, sentinel monitoring), and commercial implementations.
We filed the architecture-independent claims on February 4, 2026—the same day we proved Mamba works. The IP position is comprehensive and defensible.
Cognitive probes are tiny neural networks that attach to the hidden states of any language model. They read the model's internal representations and detect behavioral problems — like hedging, hallucination, shallow reasoning, or repetition — before they manifest in the output.
Separation measures how well probes distinguish between desired and undesired behavior. Prior published research achieves 2–5×. We achieve 125×–1,376×. That's the difference between a lab curiosity and a production-grade system.
Pre-revenue. Validated technology, 55 provisional patents with 141 claims, architecture-independent proof. First commercial deployments targeted Q3–Q4 2026 in clinical AI.
Our research is published and archived on Zenodo:
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