arXiv:2506.20040v3 Announce Type: replace-cross Abstract: Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across adjacent layers, causing single-layer analyses to miss this cross-layer structure. Cross-layer sparse autoencoders (SAEs) address layer mixing but operate in continuous space, where concepts split across many neurons without clear boundaries. We introduce Cross-Layer Vector Quantized-Variational Autoencoder (CLVQ-VAE), a novel framework which maps representations from a lower layer to a higher lay
The increasing complexity and opacity of large language models necessitate advanced interpretability techniques to understand their internal workings and ensure reliability.
This research offers a novel method to overcome current limitations in interpreting how language models process information across layers, which is crucial for their further development and deployment in critical applications.
The ability to discover discrete, interpretable concepts within LLMs could lead to more robust, auditable, and controllable AI systems.
- · AI researchers
- · Developers of interpretability tools
- · Sectors requiring explainable AI
- · Opaque black-box AI systems
- · Current single-layer analysis methods
Improved understanding and debugging of complex AI models.
Accelerated development of more reliable and safer AI applications.
Enhanced trust in AI systems could broaden their societal and industrial adoption.
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