On May 11, AI startup Thinking Machines released a research preview of Interaction Models — a noteworthy foundational architecture-level attempt in the AI field. The company was co-founded by former OpenAI CTO Mira Murati and co-founder John Schulman, focused on integrating multimodal real-time interaction capability directly into the model architecture itself, rather than relying on external matryoshka-style engineering solutions.
The interaction logic of current mainstream models is essentially single-threaded: it must wait for the user to finish speaking before the model begins processing; during the model's generation, the entire perception freezes. This turn-based paradigm is sufficient for quick Q&A, but once it involves real work flows requiring continuous collaboration — such as debugging code, reviewing documents, jointly analyzing problems — interaction bandwidth becomes a bottleneck. Users are forced to adapt to the AI's pace, rather than the AI adapting to the human's.
Thinking Machines' solution is to train from scratch a model with native interaction capability. The core technology is a time-aligned micro-turn mechanism: the model slices continuous audio, video, and text input into extremely fine-grained micro-turns, rather than the traditional model's whole-sentence/whole-segment alternation. This design lets the model perceive the user speaking (rather than having finished), perform tool calls and content generation simultaneously, and also actively interrupt or supplement during generation.
From the benchmark results, the interaction model maintains intelligence capability while significantly reducing response latency. More importantly, it unlocks capabilities that previously required additional engineering to achieve: real-time simultaneous interpretation, spoken interrupt correction, search-while-speaking with results woven into the conversation. The model is no longer just answering questions, but truly collaborating.
The underlying philosophy is worth attention: using scaling to simultaneously improve intelligence level and interaction quality, rather than optimizing them separately. This differs fundamentally from the current mainstream approach — wrapping a real-time interaction layer around an existing language model. If this path works, it means future more powerful AI will itself be a better collaboration partner; conversely, if interaction must rely on external add-ons, true collaboration capability will forever be limited by interface engineering.