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Mechanistic interpretability (often shortened to "Mech Interp" or "MI") is a subfield of interpretability that seeks to reverse‑engineer neural networks, generally perceived as a black box, into human‑understandable components or "circuits", revealing the causal pathways by which models process information.^[1] The object of study generally includes but is not limited to vision models and Transformer-based large language models (LLMs).

^ "Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases". transformer-circuits.pub. Retrieved 2025-05-03.