TRIBE v2 Agent Alignment

Validate whether your AI encoders — text, video, or audio — represent information the way human brains do, using Meta's TRIBE v2 cortical predictor. Use the resulting alignment scores to drive neuro-informed model routing in Life/Arcan.

Concept

Cortical alignment measures how well an AI encoder's hidden states predict actual fMRI brain activity in response to the same stimulus. TRIBE v2 (TRansformer for In-silico Brain Experiments) was trained on thousands of hours of naturalistic fMRI data and can predict activity across the full cortical surface (~20k vertices on the fsaverage5 mesh) for any text, video, or audio input. A high alignment score (R² > 0.25) means the encoder has learned representations that are geometrically similar to what the human language, visual, or auditory cortex computes — without any explicit neuroscience objective. This matters for model selection in an agent OS: a text encoder with higher language cortex alignment tends to generalize better to novel linguistic contexts, is more robust to distribution shift, and exhibits better zero-shot transfer. TRIBE v2 proved that LLaMA 3.2-3B spontaneously developed such alignment, validating its representations neurologically. The same benchmark can be applied to any candidate encoder before committing it to Arcan's routing stack.

Quick Start

Run a full alignment score for any encoder in 5 commands:

# 1. Install dependencies
pip install tribev2 transformers torch scikit-learn numpy

# 2. Prepare a stimulus directory (video files for video, text files for text, wav for audio)
mkdir -p ~/stimuli/text && echo "The model routed the task to the visual cortex." > ~/stimuli/text/s1.txt