Update 2025.11.27: Major refactoring into modular architecture (Module A/B/C/D) with unified interface and comprehensive benchmark suite. Update 2025.06.25: Added ...

Abstract: This paper proposes a new soft-sensor approach based on the Sparse Autoencoder (Sparse AE) combined with Long Short-Term Memory (LSTM) networks. To deliver a sparse AE model, the KL ...

Researchers at DeepSeek on Monday released a new experimental model called V3.2-exp, designed to have dramatically lower inference costs when used in long-context operations. DeepSeek announced the ...

Abstract: An innovative attendance system that uses image fusion techniques to combine facial and iris images to improve recognition accuracy. Using a sparse autoencoder (SAE), we efficiently combine ...

A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible.

Large language models (LLMs) have made remarkable progress in recent years. But understanding how they work remains a challenge and scientists at artificial intelligence labs are trying to peer into ...

Autoencoders are a class of neural networks that aim to learn efficient representations of input data by encoding and then reconstructing it. They comprise two main parts: the encoder, which ...

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