Rho-1 outputs native continuous action tokens and future video frames from the same latent state, emitting 7 action channels in a LIBERO simulation episode

Overview Debates Related claims Topics Sources

Mach9 developed and trained proprietary in-house AI models to turn multimodal image and laser scan datasets into survey-grade geometry at centimeter accuracy.

Black Forest Labs incorporated shared training across images, video, and audio to help FLUX 3 Image Klein learn visual structure and physical behavior

Nvidia released Nemotron 3.5 Super VL, an open-weight multimodal 120-billion-parameter model designed for continuous enterprise AI agents on October 15, 2026

Meta's Muse Spark Open features 816 billion total parameters, 42 billion active parameters, up to 1 million tokens of context window, and native multimodal support for text, image, video, and audio

Nemotron 3.5 Super VL parses PDFs, tables, charts, and videos without requiring a separate vision model, achieving an 80.8 percent score on the CharXiv chart benchmark

Google broadly released its flagship Gemini 4 Argon multimodal AI model globally on October 13, 2026, making it available to developers, businesses, and consumers

Google reported that Gemini 4 Argon achieved an 87% score on Google's Embodied Reasoning Question Answering benchmark and instructed Apptronik's Apollo humanoid robot to land seven consecutive cornhole throws

Decart developed the Lucy artificial intelligence model, which is designed to edit live video in real time

Nvidia provides backing for Decart's Lucy real-time video editing model

OpenAI's launch of ChatGPT's Intelligent UI alongside GPT-6 signals a broader evolution of chatbots into multimodal experiences where text, images, and voice blend into a single interactive user experience

Reka AI's Rho-1 processes all modalities as tokens inside a single shared context window, eliminating the need for tool calls, external models, or multi-agent pipelines

On October 5, 2026, Reka AI released a research preview of Rho-1, a 19-billion-parameter omni-reasoning model that processes and generates text, images, video, and robot control actions in a single neural network

Single omni-models are more effective than multi-agent pipelines for multimodal AI

To address scarce robot training data, Reka AI paired Rho-1 with an Inverse Dynamics Model that infers control signals from raw internet videos

Reka AI's Rho-1 training combines next-token prediction for discrete sequences, such as text and symbolic reasoning, with flow matching for continuous outputs, including image latents, video frames, and robot actions

A distilled variant of Reka AI's Rho-1 reduces denoising steps from 99 to 8, returning a 5.3-second video clip in approximately one second with minimal reported quality loss

Unlike ByteDance BAGEL, BAAI Emu3.5, and Google Genie 3 (which takes navigation inputs and supports steering), which do not output robot actions, Reka AI's Rho-1 emits native continuous action tokens and supports real-time steering

Reka AI's base Rho-1 model generates video at 0.79x real-time median speed, delivering a watchable stream in approximately 6 seconds and a first clip in 7.0 seconds

Reka AI's Rho-1 architecture uses two expert weight streams within each transformer block—an understanding stream for language and visual parsing, and a generation stream for denoising latents—which share attention and a single KV cache

The video generation capability of Reka AI's Rho-1 model is capped at a resolution of 672×384 pixels