Conditional Scale Entropy

Conditional Scale Entropy: A Wavelet-Derived Tool for Mechanistic Interpretability of Metaphor Processing in Transformers

This paper introduces Conditional Scale Entropy (CSE), a wavelet-derived measure of how transformer computation engages across frequency scales at each layer, and applies it to study metaphor processing in decoder-only language models. The authors prove CSE is invariant to update magnitude, isolating structural computation patterns from intensity. Across architectures ranging from GPT-2 (124M) to LLaMA-2 7B and GPT-oss 20B, metaphorical tokens consistently produce higher spectral breadth than literal tokens in early-to-mid layers, with the effect surviving permutation correction and specificity controls. The work establishes multi-scale coordination as a consistent mechanistic signature of metaphorical language processing and positions CSE as a general interpretability tool for cross-depth structure in transformers.