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What is TML-interaction-small?

TML-Interaction-Small is a real-time multimodal interaction model developed by Thinking Machines Lab to enable scalable human-AI collaboration through continuous interaction across audio, video, and text. The model is designed to overcome the limitations of traditional turn-based AI systems by allowing humans and AI to communicate more naturally through simultaneous perception, speech, visual understanding, interruptions, and collaborative reasoning. Instead of relying on external dialog management systems or separate real-time scaffolding, TML-Interaction-Small handles interaction natively through a time-aware architecture built around continuous 200ms micro-turn exchanges. This architecture allows the model to process streaming input and generate output concurrently while maintaining awareness of silence, interruptions, overlap, timing, and visual context. The model is capable of responding proactively to spoken and visual cues, enabling interaction patterns such as live translation, contextual interruptions, visual monitoring, simultaneous speech, live commentary, and continuous conversational collaboration. TML-Interaction-Small also coordinates with an asynchronous background reasoning model that performs deeper reasoning, tool usage, web browsing, and longer-horizon tasks while the interaction layer remains present and responsive throughout the conversation. Thinking Machines Lab designed the system to reduce the collaboration bottleneck in modern AI workflows by enabling humans to stay continuously involved in AI-assisted processes rather than being pushed out by fully autonomous systems. The model uses a multimodal streaming architecture with lightweight audio and visual processing pipelines, encoder-free early fusion techniques, optimized streaming inference infrastructure, and batch-invariant kernels for low-latency performance and training stability.

What is Griffin?

Griffin is recognized as Tavus’s first Human Interaction Model, an advanced full-duplex video-to-video system designed to interpret and generate real-time, face-to-face human engagements. Unlike traditional conversational systems that address speech recognition, language modeling, and speech synthesis in a step-by-step process, Griffin functions by constantly processing audio and video inputs, making prompt decisions for responses, and simultaneously providing expressive verbal and visual feedback. It pays close attention not only to the verbal content but also to the nuances of tone, eye contact, facial expressions, gestures, pauses, interruptions, and the overall visual context. This model demonstrates functionality such as backchanneling, yielding or taking conversational turns, effectively managing interruptions, staying engaged during silences, and modifying its emotional tone and nonverbal signals as the conversation progresses. Additionally, Griffin can create a rich visual scene based on a reference image, encompassing aspects like facial features, body movements, hand gestures, chair positioning, shadows, and background details, which enables it to offer a more immersive experience beyond merely presenting a talking head. The integration of these capabilities significantly enriches the quality and realism of human-like interactions, making conversations feel more genuine and engaging.

Media

Media

Integrations Supported

Integrations Supported

Tavus

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

$22 per month
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Thinking Machines Lab

Company Location

United States

Company Website

thinkingmachines.ai/

Company Facts

Organization Name

Tavus

Date Founded

2020

Company Location

United States

Company Website

www.tavus.io/griffin

Categories and Features

AI Models

Not specified

Multimodal Models

Not specified

Categories and Features

AI Models

Not specified

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