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The author describes building an experimental Loop Language Model with exit gates, sparse/compressed attention, and diffusion-based optimization, trained on limited hardware with preliminary results showing potential efficiency gains.
Sebastian Raschka reviews recent innovations in LLM architectures focused on long-context efficiency, including KV sharing, compressed convolutional attention, and layer-wise attention budgeting from models like Gemma 4, ZAYA1, Laguna XS.2, and DeepSeek V4.