Architecture Independence Proven Validated February 4th, 2026

AI That Knows Itself

We built artificial proprioception for neural networks. Models that can sense and correct their own behavioral problems in real-time.

Short Video
55
Patents Filed
11
Architectures Proven
1,376×
Peak Separation
Competitive Landscape

Real-Time Behavioral Proprioception

Every major AI lab is trying to make models safer, but none of them can see what the model is actually doing inside.

CompanyApproachLimitation
OpenAIRLHF + Internal Safety TeamCosts millions. Degrades capabilities. Black box.
AnthropicConstitutional AIOne black box judging another. No per-behavior decomposition.
Google DeepMindInternal ResearchNo commercial product. Not architecture-independent.
Meta AIOpen-Source + Red TeamingReleases models without runtime monitoring. No internal behavioral sensing.
Proprioceptive AIHidden-State Behavioral ProbesReal-time. Pre-output. Architecture-independent. 999× separation.
Our Solution

Modern AI Systems Are Architecturally Flying Blind

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.

Proprioceptive AI

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.

  • Real-time behavioral detection from hidden states
  • Works on frozen models—no fine-tuning required
  • Architecture-independent: transformers AND state-space
IP Protection

Patents

55
Provisional Patents Filed
950
Architecture-Independent Claims
Universal Behavioral Manifold (UBM)
Fiber projection, cross-model transfer, dimension-agnostic architecture for behavioral detection
Architecture-Independent Behavioral Control
Transformers, state-space models (Mamba), RNNs, RWKV, sparse attention, MoE — all covered
Hidden State Explorer (HSE)
Per-token behavioral detection, separation metrics, trajectory analysis tools
Cognitive Self-Awareness (CSA)
Self-regulation loops, behavioral state injection, closed-loop control mechanisms
Jan — Feb 4, 2026
Priority Dates Established
Team
Logan Matthew Napolitano

Logan Matthew Napolitano

Founder & CEO

Father. Husband.

The story of one developer who saw the missing piece everyone else overlooked, and did what OpenAI, Google, Grok, Meta, and AMI could not do.

Nicholas D. Goodman
Managing Director
Sara Mrkulic
Investor Relations
Michael Napolitano
Legal Counsel
(Steven) Safet Mrkulic
Retired FINRA Principal · Co-Founder
Independent AI Analysis

What Gemini, Grok, OpenAI & Claude Have to Say About Us

We asked the leading frontier language models to review our research, patents, and technical results. Here's what each had to say.

Gemini
Google DeepMind
Read the full conversation
Gemini's complete results, hosted directly by Google.
Grok
xAI
Read the full conversation
Grok's complete results, hosted directly on X.
ChatGPT
OpenAI
Read the full conversation
ChatGPT's complete results, hosted directly by OpenAI.
Claude
Anthropic
Read the full conversation
Claude's complete results, hosted directly by Anthropic.
FAQ

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 logan@proprioceptiveai.com 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:

Proprioceptive AI: Self-Compounding Behavioral Probes for Autonomous Model Improvement and Probe-Guided Compression — 82 pages

Consistency Is All You Need — 43 pages

Unified Behavioral Modulation in Large Language Models: Cross-Architecture Validation of Geometric Behavioral Subspaces — 32 pages

Controlled Language Models Inference-Time Control, Tokenization Engineering, and Reversible Optimization — 69 pages

Mathematics Is All You Need — 459 pages

Deep Dive on our Architecture & Results

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