How LLMs compress a long prompt 16x · Latent Context LMs #Shorts

Latent Context LMs are encoder–decoder language models that compress a long prompt into a much shorter sequence of latent embeddings the decoder reads directly as if they were tokens.

A long prompt is expensive because the decoder's prefill pass and KV cache both grow with the number of positions it processes. Latent Context LMs add a small 0.6B-parameter encoder that squeezes the prompt into latents a 4B-parameter decoder reads natively — trained end-to-end on 350B+ tokens — reaching 1:4, 1:8, and 1:16 compression, so a 16,000-token prompt becomes about 1,000 latent positions and prefill, the KV cache, and the attention sweep all shrink with it.

Full explainer (interactive): https://learnaivisually.com/g/latent-context-lms-encoder-decoder-compression
Source: https://arxiv.org/abs/2606.09659

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