New Paradigm
Social World Model
Physical world models simulate gravity and friction. Social world models simulate people — putting humans at the center of AI interaction.
As most global video content is consumed on social platforms for interactive purposes, models built for social worlds are critical — yet largely overlooked. Catnip defines social world models as generative systems that actively observe users, internally simulate social dynamics, and react to people in real time.
MaineCoon is the first breakthrough at the rendering layer — the real-time audio-visual generation engine that makes social interaction possible.
Three layers of a social world model
Layer 1
Perception
Read user emotion and state from text, voice, and video input
Layer 2
Simulation
Predict social behavior dynamics from human-centric context
Layer 3
Rendering
Real-time audio-visual generation — MaineCoon's breakthrough
Why rendering first?
Catnip chose the hardest layer as the entry point — because without real-time generation capability, perception and simulation have no outlet.
The gap in the industry
Existing world models simulate physical environments or game exploration. They omit auditory information and fail to capture the high-engagement pacing, emotional resonance, and rapid conversational flow that define social media.
The commercial opportunity
AI companions, virtual streamers, customer service avatars, education tutors, and game NPCs all need real-time, emotionally responsive audio-visual interaction — not pre-rendered clips.
Agentic streaming inference
Three controllers power infinite-duration social streaming.
Cognitive core
Director
Plans structured prompts beat-by-beat, monitors quality drift, and triggers forward-fix correction without interrupting the stream.
Memory system
Cache Manager
Retains character appearance, scene-establishing frames, and key dialogue as long-term anchors while clearing stale context.
Pace regulator
Buffer Controller
Balances the generation-ahead buffer to keep playback smooth while ensuring user interactions take effect within reasonable latency.
Three-stage training
Self-Resampling
Bridges the train-inference gap by exposing the model to its own imperfect generated frames during training, preventing long-horizon drift.
Representation Alignment
Distills cross-modal structure from a frozen V-JEPA 2 encoder, accelerating convergence and stabilizing audio-visual joint training.
DPO + ROPD
Domain-aware preference optimization with reinforced online policy distillation — specialized experts unified into one deployable streaming policy.
Is MaineCoon a video model or social infrastructure?+
At the model layer, it's a 22B audio-visual autoregressive generator. At the systems layer, it's the rendering engine for next-generation social AI platforms. Think of it as infrastructure, not a SaaS product.
How is this different from 'world model' as used by others?+
Traditional world models (e.g., for robotics or game engines) simulate physical environments. Social world models simulate human-centric social dynamics with real-time multimodal output.
What's next after rendering?+
Full-duplex interaction — the AI generates continuously while simultaneously perceiving user feedback (text, voice, video), moving beyond half-duplex turn-taking.
Experience MaineCoon live
Input a prompt and watch real-time streaming audio-visual generation on the official platform.