Technical review of Multi-Head Latent Attention (MLA), a significant advancement in Transformer architectures designed to address the memory and computational bottlenecks of traditional attention mechanisms.

It traces the evolution of attention from its origins in RNNs to the Multi-Head Attention (MHA) of the Transformer, highlighting the KV cache memory limitation in autoregressive models.

The core of MLA is explained through its low-rank factorization and two-stage projection pipeline, which compresses Key and Value representations into a shared latent space, dramatically reducing the KV cache size while preserving model expressiveness.

The document details the mathematical framework including the crucial "weight absorption" trick for efficient inference and the need for Decoupled RoPE to integrate positional information.

Finally, it presents empirical evidence of MLA's superior performance and efficiency through case studies of the DeepSeek model family, discusses implementation challenges and best practices, and explores future directions such as multi-dimensional compression and post-training conversion frameworks like TransMLA.