A Free Probabilistic Framework for Analyzing the Transformer-based Language Models

We outline an operator-theoretic framework for analyzing transformer-based language models using the tools of free probability theory. By representing token embeddings and attention mechanisms as self-adjoint operators in a racial probability space, we reinterpret attention as a non-commutative convolution and view the layer-wise propagation of representations as an evolution governed by free additive convolution. This formalism reveals a spectral dynamical system underpinning deep transformer stacks and offers insight into their inductive biases, generalization behavior, and entropy dynamics. We derive a generalization bound based on free entropy and demonstrate that the spectral trace of transformer layers evolves predictably with depth. Our approach bridges neural architecture with non-commutative harmonic analysis, enabling principled analysis of information flow and structural complexity in large language models

@article{das2025_2506.16550,
  title={ A Free Probabilistic Framework for Analyzing the Transformer-based Language Models },
  author={ Swagatam Das },
  journal={arXiv preprint arXiv:2506.16550},
  year={ 2025 }
}