Article

Mechanistic Interpretability for Large Language Model Alignment: Progress, Challenges, and Future Directions

2026-02-03

Abstract excerpt

Large language models (LLMs) have achieved remarkable capabilities across diverse tasks, yet their internal decision-making processes remain largely opaque. Mechanistic interpretability—the systematic study of how neural networks implement algorithms through their learned representations and computational structures—has emerged as a critical research direction for understanding and aligning these models. This pape...

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Literature Corpus work: 42764e12-e2e0-53ab-937a-3d8bd7b79457
DOI: 10.20944/preprints202602.0128.v1

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