Glossary
Definitions for the technical concepts behind MaineCoon — from streaming generation to agentic inference.
An inference system with autonomous controllers that manage narrative, memory, and pacing.
MaineCoon's inference framework uses three independent agents: the Director (plans prompts and corrects drift), Cache Manager (manages KV-cache retention and clearing), and Buffer Controller (balances generation-ahead buffer with interaction responsiveness). Together they enable thousand-second-scale generation without quality collapse.
A model that generates each output chunk conditioned on all previously generated chunks.
Autoregressive models generate sequentially — each new chunk depends on the history of prior chunks. For MaineCoon, this means each sub-second audio-visual segment is conditioned on all previous segments, maintaining temporal coherence. The challenge is that errors compound over time, which MaineCoon addresses through self-resampling training and agentic drift correction.
Regulates the generation-ahead buffer to balance smooth playback with interaction speed.
Because MaineCoon generates faster than playback speed, a buffer of pre-generated content accumulates. The Buffer Controller keeps this ahead-buffer within an optimal window — enough to prevent stutter, but small enough that user interactions (new prompts) take effect within reasonable latency.
Manages KV-cache retention — keeping identity anchors while clearing stale context.
The Cache Manager controls which generated frames remain in the model's KV-cache (attention memory). It retains character appearance frames, scene-establishing shots, and key dialogue moments as long-term anchors, while periodically clearing stale context and applying statistical anchors to correct global appearance drift.
AI-generated human-like characters for video communication and interaction.
Digital humans are AI-rendered characters that look, speak, and behave like real people. Platforms like HeyGen and Synthesia provide turnkey digital human video production. MaineCoon operates at a deeper layer — the real-time generative engine that can power next-generation digital human experiences with live streaming and interaction.
The cognitive controller that plans narrative beats and corrects quality drift.
The Director is MaineCoon's narrative and quality-control agent. It uses a planner to generate structured prompts (visual description + dialogue + ambient audio) beat-by-beat, and an observer to monitor output for quality drift. When drift is detected, it triggers forward-fix correction on the next frame without interrupting the stream.
Domain-aware preference optimization with reinforced online policy distillation.
MaineCoon's post-training stage uses Domain-Aware Preference Optimization (DPO) to train specialized expert models for different social scenarios (dance, dialogue, wide shots), then Reinforced Online Policy Distillation (ROPD) to unify them into a single deployable streaming policy. This balances scenario-specific quality with deployment efficiency.
Generation throughput — how many video frames the model produces per second.
FPS measures how many complete video frames the model generates per second during inference. Real-time playback typically requires 24–30 FPS. MaineCoon achieves 47.5 FPS on a single H100 and 30+ FPS on RTX Pro 6000 — exceeding real-time requirements and building a generation-ahead buffer for smooth streaming.
Changing generation behavior via new prompts while output is actively streaming.
Interactive control allows users to inject new instructions — tone changes, dialogue, questions — while the model is actively generating. The model adapts the ongoing stream without resetting, enabling conversational dynamics impossible with batch generation models.
Key-value attention cache storing computed attention states from prior generation steps.
In autoregressive generation, each new chunk requires attending to all prior chunks. KV-cache stores the computed key-value pairs from previous steps to avoid recomputation. MaineCoon's Cache Manager strategically retains or clears cache entries to balance generation speed, memory usage, and long-horizon consistency.
Alignment between generated speech audio and visible mouth movements.
Lip sync quality is a primary indicator of AI video realism in social contexts. Because MaineCoon generates audio and video jointly in each autoregressive chunk, lip movements naturally align with speech — unlike pipelines that generate video first and add audio separately.
Cross-modal distillation from a frozen V-JEPA 2 encoder to accelerate AV training.
Joint audio-visual training is slow to converge. MaineCoon introduces a frozen pre-trained V-JEPA 2 visual encoder as a distillation target, helping the model learn cross-modal semantic structure faster. This acts as both a training accelerator and stabilizer for audio-visual alignment.
Training technique that exposes the model to its own imperfect outputs as context.
During training, models typically use clean ground-truth frames as history context. But at inference, the model only has its own generated frames — creating a train-inference gap. Self-resampling feeds the model degraded versions of its own outputs during training, teaching it to stay stable even when history contains noise and drift.
AI systems that center human social dynamics — observing emotion, simulating behavior, and responding in real time.
Catnip's term for generative AI systems that put humans at the coordinate center. Unlike physical world models that simulate gravity and object physics, social world models simulate social behavior — reading user emotion, predicting interaction dynamics, and rendering responses through real-time audio-visual output. MaineCoon is the rendering-layer breakthrough.
Catnip's benchmark for social-interaction video across 7 scenarios and 9 metrics.
The first benchmark designed specifically for social-interaction video generation. It covers 7 scenarios (dense speech, two-person interaction, musical performance, emotional acting, dance, creative challenges, social memes) and 9 metrics (visual quality, motion, audio, alignment, consistency, etc.). MaineCoon scores 0.934 overall — surpassing all 7 compared baselines.
Incremental output produced and consumed simultaneously, rather than waiting for complete generation.
Streaming generation means the model produces output incrementally — chunk by chunk — while the user consumes it in real time. In text, this is how ChatGPT outputs tokens one at a time. In video, each chunk contains synchronized audio and visual frames. MaineCoon compresses the generation unit to sub-second chunks, enabling first-frame latency under 3 seconds.
First output frame appears within one second of receiving a prompt.
Sub-second latency refers to the time between submitting a prompt and seeing the first generated frame. MaineCoon achieves first frame in under 3 seconds, with subsequent chunks arriving at sub-second intervals. This is critical for social interaction where delays break the illusion of live conversation.
AI systems that simulate environments — physical, game, or social.
World models are generative systems that simulate environments internally. Physical world models (e.g., for robotics) simulate object physics. Game world models simulate exploration. Social world models — a new category — simulate human-centric social dynamics with real-time multimodal output. MaineCoon represents the rendering breakthrough for social world models.
Input a prompt and watch real-time streaming audio-visual generation on the official platform.