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## News
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- 2025.4.29 We introduce **Low-Rank Sparse Attention (Lorsa)** to attack attention superposition, extracting tens of thousands of true attention units from LLM attention layers. Link: [Towards Understanding the Nature of Attention with Low-Rank Sparse Decomposition](https://arxiv.org/abs/2504.20938)
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- 2024.10.29 We introduce **Llama Scope**, our first contribution to the open-source Sparse Autoencoder ecosystem. Stay tuned! Link: [Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders](http://arxiv.org/abs/2410.20526)
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- 2024.10.9 Transformers and Mambas are mechanistically similar in both feature and circuit level. Can we follow this line and find **universal motifs and fundamental differences between language model architectures**? Link: [Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures](https://arxiv.org/pdf/2410.06672)
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