Reproduce “Towards Next-Generation Architectures of Large Language Models: A Survey” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yao Teng, Guoxuan Chen, Yimeng Chen, Han Shi, Piotr Piękos, Jie Ran, Yutao Zeng, Chuanyang Zheng and 8 more
The paper surveys how large language model decoders can vary computation, transformations, memory, and prediction rather than replace the Transformer with one architecture. It organizes design choices across these components and shows combinations, including DeepSeek-V2 and V3’s sparse mixture-of-experts layers with Multi-head Latent Attention. These releases establish joint use, not causal benefits; the survey is not a matched experimental comparison.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.