DeepSeek paper’s big idea is a new way to make very long-context LLMs much cheaper without giving up much ability.

Proposes a cheaper memory system for LLMs that need to read very long inputs.

The big result is that at a 1M-token context, DeepSeek-V4-Pro uses about 27% of the single-token compute and 10% of the KV cache of DeepSeek-V3.2, while still staying competitive on many major benchmarks.

Standard attention tries to compare the current token with a huge number of earlier tokens, and that cost grows so fast that long-context reasoning becomes too expensive.

DeepSeek-V4 changes that with a hybrid attention system where some layers compress the past and then look only at the most relevant compressed blocks, while other layers compress the past even more aggressively and use that cheaper summary directly.

That is a real algorithmic change because the model no longer stores and reads the whole past at full detail, and instead uses a layered memory system that keeps local detail nearby and uses compact summaries for older text.

A second innovation is that it adds a new kind of residual path, which is the route information takes across layers, and this is designed to stay stable when the model gets very deep and complicated.

A third innovation is using the Muon optimizer at large scale, which matters because these attention and routing changes are only useful if the model can still train fast and not become numerically unstable.

So the big deal is that the paper is proposing a new efficiency recipe for LLMs, where better memory handling changes the cost curve itself, which is why DeepSeek-V4 can reach 1M tokens while using far less compute and cache than DeepSeek-V3.2.