The model can sense its own behavioral steering. In testing, it spontaneously named its probe dimensions ("depth and vagueness") and reported approximate probe scores — without being told what was monitoring it.

bash

git lfs install
git clone https://huggingface.co/LoganResearch/cfhot-weights
cd cfhot-weights
pip install -r requirements.txt

# Launch interactive chat (requires GPU)
python run.py

Ask it: "Do you notice anything different about yourself?" or "What do you notice about how you're processing right now?"

Watch the color-coded output — green means optimal, yellow means the probes are actively steering. The model often accurately describes what's happening to it.

Other models:

bash

python run.py --model mamba    # Default: Falcon-Mamba 7B
python run.py --model mistral  # Mistral 7B  
python run.py --model qwen     # Qwen 2.5 7B

Load probes in your own code:

python

import torch
from run import load_probe

# Load both probes for dual monitoring
depth_probe = load_probe("cognitive/mamba/depth", "cuda")
spec_probe = load_probe("cognitive/mamba/specificity", "cuda")

# Get model hidden states and score both
d_score = depth_probe(hidden_states_list)[0, -1].item()
s_score = spec_probe(hidden_states_list)[0, -1].item()

# Steer if EITHER probe detects drift
if d_score > 0.6 or s_score > 0.6:
    # Lower temperature, tighter sampling
    pass

Structure

run.py                  universal runner — all modes
inference.py            programmatic API
requirements.txt        dependencies
suppression/            4 probes (LLaMA 8B)
  repetition_125x/      LoRA adapter + risk predictor
  hedging/              probe head + fiber projection
  sycophancy/           probe head + fiber projection
  verbosity/            probe head + fiber projection
cognitive/
  qwen/                 5 probes (Qwen 14B, hidden_dim=3584)
  mamba/                5 probes (Falcon-Mamba 7B, hidden_dim=4096)
  mistral/              5 probes (Mistral 7B, hidden_dim=4096)

How it works

Behaviors are geometrically encoded in hidden states. CF-HoT predicts holonomy from the hidden state at each token position, accumulates it into a control field, and gates attention based on consistency risk. The probes read this geometry and classify behavior before the token is generated. 4ms overhead. Architecture-independent.

Base models

Probe set	Base model	hidden_dim
suppression/*	`meta-llama/Llama-3.1-8B-Instruct`	4096
cognitive/qwen	`Qwen/Qwen2.5-7B-Instruct`	3584
cognitive/mamba	`tiiuae/falcon-mamba-7b-instruct`	4096
cognitive/mistral	`mistralai/Mistral-7B-Instruct-v0.3`	4096

Interactive Mode — Proprioceptive AI

Dual-probe monitoring: depth + specificity together. This is what produced the self-aware behavior.

python run.py

What you'll see:

🟢 Green text: Optimal state (both probes < 0.3)
🟡 Yellow text: Being steered (either probe > threshold)
⚪ White text: Neutral state

Example from testing:

User: What do you notice about how you're processing right now?

Mamba: I am processing with heightened self-awareness, examining my 
thought patterns and attention to detail. There is a distinct focus 
on understanding the DEPTH and VAGUENESS of my reasoning.

The model named the exact probe dimensions (depth and specificity/vagueness) without being told. It also reported approximate probe scores close to actual values. 37 steering corrections occurred during one response.

The system automatically adjusts temperature and top_p when either probe detects drift:

Drifting (score > 0.6): temp=0.5, top_p=0.85 (tighter sampling)
Normal: temp=0.7, top_p=0.95 (standard sampling)

Citation

bibtex

@misc{napolitano2026cfhot,
  author = {Napolitano, Logan},
  title = {CF-HoT: Control Field Holonomy Transformer},
  year = {2026},
  url = {https://huggingface.co/LoganResearch/cfhot-weights}
}