Through the Bottleneck: How Multi-head Latent Attention Separates Content from Position in Language Models
Multi-head Latent Attention (MLA), introduced in DeepSeek-V2, compresses key-value pairs through a shared low-rank bottleneck (cKV), achieving 81% KV-cache reduction during inference. Despite its adoption in massive production models, no prior work has studied what information this bottleneck preserves or discards, nor how it reshapes internal transformer circuits. We present the first comprehensive mechanistic interpretability study of MLA, training a 114M-parameter transformer (pretrained on a web/code/math mixture, fine-tuned on TinyStories) and analyzing its representations through SVD, attention head taxonomy, linear probing, and a disruption-attribution analysis. Our key findings ar...
| Authors | Dhruvil S, Fenil Sojitra, Ravirajsinh Chauhan |
|---|---|
| Primary category | cs.LG |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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