Thinking Machines Lab unveils a preview of interaction models, which can think, respond, and act in real time, letting users continuously collaborate with AI
Today, we're announcing a research preview of interaction models: models that handle interaction natively rather than through external scaffolding.
Thinking Machines Lab
Context & Ripple Effects
Thinking Machines Lab’s interaction-model preview is part of a stated effort to make AI systems that people and organizations can shape, rather than simply consume as fixed outputs. The related coverage frames continuous collaboration as a central product and research direction.
The lab has also introduced Inkling as a broad, open-weight Mixture-of-Experts model. Together, the releases suggest a split but complementary strategy: make capable models available broadly while developing interaction behavior as a distinct layer of differentiation.
First-order effects
The preview gives Thinking Machines Lab a concrete research direction centered on native real-time interaction, shifting emphasis from one-shot prompting toward ongoing user–AI collaboration.
Users and prospective partners can evaluate whether the lab’s models can retain enough responsiveness and controllability during an active exchange to be useful beyond conventional chat workflows.
Second-order effects
If interaction is handled natively rather than through external scaffolding, application builders may need to reconsider agent orchestration, interface design, and evaluation methods built around discrete prompt-and-response turns.
Rival model providers face added pressure to show not only reasoning or broad capability, but also how reliably their systems behave when users continuously intervene, redirect, and collaborate.
Third-order effects
The meaningful unit of competition may increasingly become the human–AI working loop—responsiveness, steerability, and recoverability during use—rather than benchmark performance alone.
If this approach proves practical, model releases could become more tightly coupled to interaction tooling and user-control mechanisms; whether native interaction displaces external orchestration remains unproven from this preview.
The trend: This is one data point in the shift from AI as a request-and-answer system toward AI designed for persistent, user-steered collaboration.
Today we're sharing our work on interaction models. A new class of model trained from scratch to handle real-time interaction natively, instead of gluing it onto a turn-based one. https://www.youtube.com/...
I think this is bigger than it sounds at first glance. Thinking Machines hasn't just unveiled “ChatGPT, but better.” Instead, they've introduced something that addresses a much deeper issue: the very way we interact with AI. So far, AI often feels like email with very clever [ima…
Very cool announcement from Thinky! The model looks nice (they go into some reasonable amount of detail), and reading some parts of the blog you can definitely see that the infea guys had a lot of fun there! [image]
People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way. We share our approach, early results, and a quick look at our model in action. https://thinkingmachines.ai/ ... [video]
Sharing our work on full-duplex multimodal models — real-time interaction that's natural and intuitive without compromising on intelligence. We started Thinky in part to differentially advance capabilities for human-AI collaboration, which are underemphasized relative to
Thinky's secret plan: 1: Increase Human<->AI bandwidth 2: Raise ceiling of human+AI intelligence 3: Help humans continue as main-characters in the new world We are at Step 1. Interaction Models are great real-time collaborative tools for humans. Here's a preview:
In modern ML accelerators, FLOPS have absolutely exploded. Often though, the bottleneck is not FLOPS but memory bandwidth. Similarly, model intelligence has exploded, causing the bottleneck to be human<->AI bandwidth. At Thinky, we think that it's important to solve this. 1/4 [im…
We started Thinking Machines to advance human-AI collaboration, and this is our first bet on what that looks like. Most labs treat autonomy as the goal and interactivity as scaffolding around a turn-based core. We think the way we work with AI matters as much as how smart it is.
the “small” model behind this demo is a 276B total 12B active MoE (larger pretrains are cooking), sparsity ratio looks pretty standard compared to open models of the same size [image]
The demo of this model is cool. Interacts with multiple people in multiple languages. On multiple tasks. Now we know why @miramurati got the big bucks.
In the past few months, we had a lot of fun (and stress 😅) to produce 12 versions (+ many subversions) and 137 pages in our training run log book. Turns out human-human collaboration is important to improving human-AI collaboration. 😊 [image]
lowkey the funniest videos of the batch. thinky has some comedians!! congrats to @thinkymachines on reviving the omnimodel dream that others could not [image]
Haven't tried this but it seems very neat... Yet all of the demos (except maybe one) are the model being fun and/or annoying by correcting or reminding in real time. There are obvious uses for this sort of model in meetings, education, training, etc. Why not demo valuable cases?
The current “AI experience” often feels like a conversation that only begins after we stop talking. We have to batch our thoughts. We can't point at things. We phrase questions like emails. The interface doesn't leave room for us so we adapt to the models.
AI changes us. Thinking Machines exists to build AI tools that increase human participation, preserve dignity across different minds, and move fast without severing society from our slower layers of memory, culture, and care. Interaction models are such a tool: an experiment in
While Lilian is telling a story, the interaction model can track when she is thinking, yielding, self-correcting, or inviting a response; there is no specific built dialogue management system. [video]
New thinking machines research!!! They present interaction models, “To ensure real-time responsiveness, we adopt a multi-stream, micro-turn design.” — I've been saying this!!!! — There is A LOT in this post, but tldr: they use two models, one with a very short ‘turn’, and a …
Ex OpenAI CTO Mira Murati is giving them a serious fight for the bucks. Her new “Interaction Model” makes “GPT-Realtime-2” look like caveman, current capabilities level wise