CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs
Mechanistic interpretability seeks to understand how Large Language Models (LLMs) represent and process information. Recent approaches based on dictionary learning and transcoders enable representing model computation in terms of sparse, interpretable features and their interactions, giving rise to feature attribution graphs. However, these graphs are often large and redundant, limiting their interpretability in practice. Cross-Layer Transcoders (CLTs) address this issue by sharing features across layers while preserving layer-specific decoding, yielding more compact representations, but remain difficult to train and analyze at scale. We introduce an open-source library for end-to-end tra...
| Authors | Florent Draye, Vedant Palit, Abir Harrasse et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 March 2026 |
| Last updated at source | 21 July 2026 |
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