# CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs

arXiv: https://arxiv.org/abs/2603.21014 · HTML: https://mlsanity.com/paper/2603.21014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge and representations inside LLMs, one of 13 directions in Mechanistic interpretability.
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 22 Mar 2026.

## Placed in

- Mechanistic interpretability / [Knowledge and representations inside LLMs](https://mlsanity.com/mechinterp/knowledge-and-representations-inside-llms/)

## Builds on (most cited first, 2 of 2)

- [Transcoders Find Interpretable LLM Feature Circuits](https://mlsanity.com/paper/2406.11944/) (2024)
- [Tracing Multilingual Representations in LLMs with Cross-Layer Transcoders](https://mlsanity.com/paper/2511.10840/) (2025)

## Built on by (most cited first, 2 of 2)

- [Hidden-State Privacy Has an Empty Middle](https://mlsanity.com/paper/2605.24042/) (2026)
- [How Do Linear Probes Emerge? A Circuit-Tracing Framework with Concept-Targeted Attribution](https://mlsanity.com/paper/2608.27510/) (2026)

## Abstract (arXiv)

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 training and interpretability of CLTs. Our framework integrates scalable distributed training with model sharding and compressed activation caching, a unified automated interpretability pipeline for feature analysis and explanation, attribution graph computation using Circuit-Tracer, and a flexible visualization interface. This provides a practical and unified solution for scaling CLT-based mechanistic interpretability. Our code is available at: https://github.com/LLM-Interp/CLT-Forge.