Viewing profile — volodia

Thank you for the detailed feedback! I shared this already with the team.

This looks like an inference glitch that we are working on fixing, thank you for flagging.

There are many ways to do it, but the simplest approach is block diffusion: https://m-arriola.com/bd3lms/ There are also more advanced approaches, for example FlexMDM, which essent…

Would love to hear about your experience. Send us an email.

Not imminently, but hard to predict where the field will go

There are few: fast agents, deep research, real-time voice, coding. The other thing is that when you have a fast reasoning model, you spend more effort on thinking in the same late…

We agree! In fact, there is an emerging class of models aimed at fast agentic iteration (think of Composer, the Flash versions of proprietary and open models). We position Mercury …

That is also our view! We see Mercury 2 as enabling very fast iteration for agentic tasks. A single shot at a problem might be less accurate, but because the model has a shorter ex…

You can think of Mercury 2 as roughly in the same intelligence tier as other speed-optimized models (e.g., Haiku 4.5, Grok Fast, GPT-Mini–class systems). The main differentiator is…

Thanks for trying it and for the thoughtful feedback, really appreciate it. And we’re actively working on improving quality further as we scale the models.

Thank you for your patience. We are working to handle the surge in demand.

Just to clarify one point: Mercury (the original v1, non-reasoning model) is already used in production in mainstream IDEs like Zed: https://zed.dev/blog/edit-prediction-providers …

I’d push back a bit on the Pareto point. On speed/quality, diffusion has actually moved the frontier. At comparable quality levels, Mercury is >5× faster than similar AR models (in…

Co-founder / Chief Scientist at Inception here. If helpful, I’m happy to answer technical questions about Mercury 2 or diffusion LMs more broadly.

There is also this one that was released in October: https://github.com/kuleshov/char-mdlm

the LLaDA paper is a scaled-up version of this paper; they cite it as an anonymous ICLR submission

Great question! The model can more efficiently leverage existing GPU hardware---it performs more computation per unit of memory transferred; this means that on older hardware one s…

Yes, we plan to be releasing a tech report soon. We are not open sourcing the models at launch time, but we have a roadmap of future releases in which we hope to make some of our m…

That's a good point. In this context, we've been using "commodity GPUs" to refer to standard Nvidia hardware, in contrast to specialized chips like Groq and Cerebras. While these c…

Good question! We are not open sourcing the models at launch time, but we have a roadmap of future releases in which we hope to make some of our models accessible to the research c…

The short answer is that we do more than one parallel pass over multiple tokens: we iteratively refine them over a few passes to fix incoherences. This can be seen as a generalizat…

Not today, but we will be following up with a technical report over the next week or so. In the meantime, you can take a look at some of the research papers that inspired our work:…