Generates multi‑speaker speech and environmental audio from textual instructions or a reference clip, supporting zero‑shot voice cloning and detailed scene/specification control. Combines a cleaned, captioned dataset with a VAE-based multimodal generator, reward-conditioned quality control, and staged training to improve expressiveness and multi-audio modeling.
AI Video Papers2026
Performs real-time, instruction-guided video-to-video editing on streaming input using a 16B autoregressive diffusion model that preserves subject identity and long-term temporal coherence; achieves end-to-end 720p at ≈30 FPS on a single Nvidia B200 GPU. Key features include chunk-wise autoregressive adaptation, Source-Anchored Distribution Matching Distillation (SA-DMD) that reduces diffusion to a two-step generator, and Long-Horizon Autoregressive Distillation to mitigate temporal drift.
AI Model2026
A quickly trained 2D "tine" VAE for MiniMax‑H3 that speeds up preview renders of video outputs and typically outperforms latent2rgb for preview use. Currently only compatible with the ModelPreviewOverride node in ComfyUI‑KJNodes and intended for previewing rather than production-grade decoding.
AI Video2026
Experimental MiniMax H3 variant that injects learned stylistic and motion 'character' from LTX 2.3, Wan 2.2 and Krea 2 into H3 by surgically grafting attention and MLP components; preserves H3 modality routing while shifting t2v/i2v aesthetics, with limited audio impact and community-license constraints.
AI Agent Papers2026
Analyzes how to build effective training environment distributions for multimodal agents and proposes Ability-aware Environment Selection (AES) and Hierarchical Difficulty Curriculum (HDC) to improve diversity and difficulty scheduling, yielding large relative gains in experiments.
Detects and filters spurious token-level teacher supervision in on-policy distillation by estimating input-groundedness and removing high-impact misleading updates, improving OPD on both LLM and VLM benchmarks.
AI Video2026
Generates short videos with stereo audio from text prompts using a MiniMax‑H3 checkpoint; community‑uploaded on Hugging Face and distributed under Apache‑2.0. Tuned toward stylized creature and floral visuals and updated frequently per the model card.
AI Agent Papers2026
Generates large-scale, explorable 3D open-world scenes from open-ended text prompts, producing editable instance-level assets and a consistent global terrain. Uses agentic planning to convert text into region/terrain/asset specifications and a coarse-to-fine pipeline for terrain construction, mesh reconstruction, and render-based refinement.
AI Agent Papers2026
Orchestrates reasoning, external tool use, and native image generation under one unified multimodal agent policy via post-training. Introduces RAD-GRPO for agentic reinforcement fine-tuning and releases training data plus the full post-training infrastructure.
AI Model2026
A 27B-parameter causal language model with a native vision encoder for image/video+text understanding, long-horizon agentic tasks, and tunable thinking-mode reasoning. Native 262,144-token context (extensible to 1,000,000) and production-focused inference recipes.
AI Video2026
A LoRA adapter for MiniMax-H3 that enables joint video + synchronized stereo audio generation in as few as 4 sampler steps, cutting sampling time roughly ~5×; early prototype under-trained, so 6–8 steps or newer checkpoints give better sharpness.
Systematically studies how language and vision interact during unified multimodal pretraining, identifies mechanisms that enable modality synergy versus competition, demonstrates the benefit of early joint training, and derives efficient pretraining recipes validated at scale.