tribe-v2-agent-alignment

To benchmark cortical alignment, evaluate AI encoders by comparing their hidden state representations against TRIBE v2 fMRI-based brain predictions using ridge regression probes to compute R-squared alignment scores.

Neuro-informed model routing integrates cortical alignment scores into the Arcan agent OS to dynamically route tasks to the AI encoder that best matches human brain processing.

Yes, this benchmarking supports evaluating text, video, and audio encoders against fMRI-based brain predictions to quantify how well their representations align with human cortical processing.

Yes, you need PyTorch, scikit-learn, transformers, and the tribev2 package to execute the ridge regression probes and compute the R-squared alignment scores for your encoders.

Comparing against TRIBE v2 fMRI predictions solves the challenge of selecting biologically plausible AI encoders, ensuring model representations align with human cortical processing to improve robustness and generalization.

Validate fine-tuned models by measuring their cortical alignment using TRIBE v2 fMRI predictions, verifying that the updated encoder representations maintain biological plausibility and performance before deployment.