How ReFusion can make LLMs generate faster?

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The problem

Most AI models write text one word at a time.

That keeps answers good, but makes them slow.

Some models try to write everything at once to be faster.

But then:

– they lose memory efficiency

– the text feels less connected

– compute cost goes up

So currently it’s fast vs good, not both.

The solution: ReFusion

ReFusion doesn’t work word by word.

It works in small chunks of text, called slots.

How it works:

– First, the model plans which chunks can be written together

– Then it writes those chunks in parallel

For example: Sentence to write:

“The cat sat on the mat.”

Step 1: Plan slots

The model decides:

Slot 1: “The cat”

Slot 2: “sat on”

Slot 3: “the mat”

Step 2: Write slots in parallel

All three are generated at the same time:

Slot 1 → “The cat”

Slot 2 → “sat on”

Slot 3 → “the mat”

Step 3: Combine

Final sentence:

“The cat sat on the mat.”

The result

– Faster than the strong models we use today

– Quality stays almost the same

Do you think decoding speed and not model size might be the next big frontier?

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