If I’m reading this right, this is pretty wild. They turned a Qwen autoregressor into a diffuser by using a bunch of really clever techniques, and they vastly outperform any “native diffuser,” actually being competitive with the base model they were trained from. The obvious upside here is the massive speedup in generation.
And then through a LoRA adapter, you can ground the diffuser on the base model’s distribution (essentially have it “compare” its proposals against what the base model would’ve generated), which effectively means: exact same byte-for-byte output for the same seed, just roughly twice as fast (which should improve even more for batched tasks).
I’m not an expert, more of a “practicing enthusiast,” so I might be missing something, but at first glance, this reads super exciting to me.
I haven't read TFA yet but a common technique is speculative decoding where a fast draft model will generate X tokens, which are then verified by the larger target model. The target model may accept some Y <= X tokens but the speedup comes from the fact that this can be done in parallel as a prefill operation due to the nature of transformers.
So let's say a draft model generates 5 tokens, all 5 of these can be verified in parallel with a single forward pass of the target model. The target model may only accept the first 4 tokens (or whatever) but as long as the 5 forward passes of the draft model + 1 prefill of the target model is faster than 4 forward passes of the target, you will have a speedup while maintaining the exact output distribution as the target.
From our look into it - amazing speed, but challenges remain around time-to-first-token user experience and overall answer quality.
Can absolutely see this working if we can get the speed and accuracy up to that “good enough” position for cheaper models - or non-user facing async work.
One other question I’ve had is wondering if it’s possible to actually set a huge amount of text to diffuse as the output - using a larger body to mechanically force greater levels of reasoning. I’m sure there’s some incredibly interesting research taking place in the big labs on this.
The overall speed rather than TTFT might start to be more relevant as the caller moves from being a human to another model.
However quality is really important. I tried that site and clicked one of their examples, "create a javascript animation". Fast response, but while it starts like this
```
Below is a self‑contained HTML + CSS + JavaScript example that creates a simple, smooth animation: a colorful ball bounces around the browser window while leaving a fading trail behind it.
Weird; I clicked through out of curiosity and didn't get any corruption of the sort in the end result.
I also asked it some technical details about how diffusion LLMs could work and it provided grammatically-correct plausible answers in a very short time (I don't know the tech to say if it's correct or not).
I've found the latency and pricing make Mercury 2 extremely compelling for some UX experiments focused around automated note tagging/interlinking. Far more than the Gemini Flash Lite I used before, it made some interactions nearly frictionless, very close to how old school autocomplete/T9/autocorrect works in a manner that users don't even think about the processes behind it.
Sadly, it does not perform at the level of e.g. Haiku 3.5 for tool calling, despite their own benchmarks claiming parity with Haiku 4.5, but it does compete with Flash Lite there too.
Anything with very targeted output, sufficient existing input and that benefits from a seamless feeling lends itself to dLLMs. Could see a place in tab-complete too, though Cursors model seems to be sufficiently low latency already.
I've been playing with a Swift implementation of a diffusion language model (WeDLM), but performance is not yet acceptable and it still generates roughly from left-to-right like a language model (just within a sliding window rather than strictly token-by-token... but that doesn't matter when the sliding window is only like 16 tokens.)
And then through a LoRA adapter, you can ground the diffuser on the base model’s distribution (essentially have it “compare” its proposals against what the base model would’ve generated), which effectively means: exact same byte-for-byte output for the same seed, just roughly twice as fast (which should improve even more for batched tasks).
I’m not an expert, more of a “practicing enthusiast,” so I might be missing something, but at first glance, this reads super exciting to me.
So let's say a draft model generates 5 tokens, all 5 of these can be verified in parallel with a single forward pass of the target model. The target model may only accept the first 4 tokens (or whatever) but as long as the 5 forward passes of the draft model + 1 prefill of the target model is faster than 4 forward passes of the target, you will have a speedup while maintaining the exact output distribution as the target.
then once successfully trained you get faster inference from just the diffusion model
This startup seems to have been at it a while.
From our look into it - amazing speed, but challenges remain around time-to-first-token user experience and overall answer quality.
Can absolutely see this working if we can get the speed and accuracy up to that “good enough” position for cheaper models - or non-user facing async work.
One other question I’ve had is wondering if it’s possible to actually set a huge amount of text to diffuse as the output - using a larger body to mechanically force greater levels of reasoning. I’m sure there’s some incredibly interesting research taking place in the big labs on this.
However quality is really important. I tried that site and clicked one of their examples, "create a javascript animation". Fast response, but while it starts like this
``` Below is a self‑contained HTML + CSS + JavaScript example that creates a simple, smooth animation: a colorful ball bounces around the browser window while leaving a fading trail behind it.
<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>JavaScript Bounce Animation</title> <style> body, html { margin: 0; padding: 0;
```
the answer then degrades to
``` radius: BALL_RADIUS, color: BALL_COLOR, traivD O] // array of previous {x,y} positions }; ```
Then more things start creeping in
``` // 3⃣ Bounce off walls if (ball.G 0 ball.radius < 0 || ball.x + ball.radius > _7{nas.width) { ball.vx *= -1; ibSl.x = Math.max(ball.radius, Math.min(ball.x, canvbbF4idth - ball.radius)); } if
```
and the more it goes on the worse it gets
``` Ho7 J3 Works 0 Atep | Description | ```
and
``` • prwrZ8}E6on 5 jdF wVuJg Ar touc> 2ysteners ,2 Ppawn \?) balls w>SFu the 8b$] cliM#]9 ```
This is for the demo on the front page, so I expect this is a pretty good outcome compared to what else you might ask.
I also asked it some technical details about how diffusion LLMs could work and it provided grammatically-correct plausible answers in a very short time (I don't know the tech to say if it's correct or not).
Sadly, it does not perform at the level of e.g. Haiku 3.5 for tool calling, despite their own benchmarks claiming parity with Haiku 4.5, but it does compete with Flash Lite there too.
Anything with very targeted output, sufficient existing input and that benefits from a seamless feeling lends itself to dLLMs. Could see a place in tab-complete too, though Cursors model seems to be sufficiently low latency already.
I have an agentic benchmark and it shows Mercury 2 at 19/25 in 58 seconds and Mimo v2 Flash at 22/25 in 109 seconds
https://sql-benchmark.nicklothian.com/?highlight=xiaomi_mimo... (flip to the Cost vs Performance tab to see speed more graphically too)
https://www.emergentmind.com/topics/dflash-block-diffusion-f...
There are several Mac implementations of it that show > 2x faster Qwen3.5 already.
> 2025-04-12: Released I-DLM-8B, I-DLM-32B, and I-DLM-8B-LoRA on HuggingFace.
Is this old already? Not saying that's a bad thing, since it seems very sophisticated. Just curious if there's an update
https://huggingface.co/yifanyu/I-DLM-32B/tree/main
[0] https://docs.inceptionlabs.ai/get-started/models#mercury-2