Efficient 4B-scale image generation and editing model family that pairs a lightweight VAE tokenizer (Mage-VAE) with a native-resolution multimodal diffusion transformer, reducing tokenization cost by an order of magnitude and enabling few-step high-resolution generation and editing.
AI Model2026
Efficient 4B native-resolution diffusion foundation model for text-to-image generation and instruction-based image editing. Uses a lightweight Mage‑VAE tokenizer and a 4B NR‑MMDiT backbone to produce 512–2048 outputs with low memory and fast inference; ships in Base, RL-aligned and few-step Turbo variants.
AI Dataset2026
A compact evaluation dataset and harness for testing agentic AI on safety-critical robotics tasks. Includes multimodal episodes in parquet format, task-specific eval scripts (gauge reading, human safety monitoring, VLA estimators), and TFDS/Hugging Face integration for reproducible safety evaluations.
AI Image2026
Performs instruction-based image editing from reference images using a 4B native-resolution diffusion transformer; the Turbo variant uses 4-step distillation for interactive latency (≈1.02 s per 1024² edit on A100) while supporting semantic, appearance, structure-aware and restoration edits.
A PyTorch-native training framework for agentic reinforcement learning research that keeps researcher-facing code compact and editable. Uses an asynchronous loop to train multimodal and mixture-of-experts policies while never training on tokens the agent didn't generate; matches Megatron-style stacks under a comparable protocol and ships recipes and containers on GitHub.
AI Video Papers2026
Extrapolates long video sequences from very short contexts by restoring memory-writing supervision in autoregressive video diffusion models using a two-pass Self Gradient Forcing (SGF). SGF records a no-gradient rollout at a sampled denoising exit and then recomputes KV context in a second parallel pass so future losses teach earlier latent writes, enabling minutes-long extrapolation from ~5s windows.
AI Video2026
Generates synchronized audiovisual output from text, image, or audio prompts — a diffusion-based multimodal model with componentized weights (video/audio VAEs, multilingual text encoder, distilled transformer) and ready integration with HuggingFace pipelines and ComfyUI.
Evaluates spatial cognition of image-generation models by eliciting protocol-constrained visual answers and parsing pixel outputs into structured predictions compatible with existing metrics. Introduces the ProVisE framework and SpatialGen-Bench (470 samples) to compare image-generation models and text-output VLMs on unified spatial tasks.
Converts image-content removal into a contrastive on-policy self-distillation signal: the EMA teacher produces next-token distributions with and without image content, uses their log-probability differences to sharpen visual-grounded candidates, and distills that full-distribution target into the student—no external teacher or extra inference cost.
Empirically studies how transformer-based native multimodal pre-training scales under fixed compute, deriving compute- and data-allocation power laws and an efficiency frontier for model size, token count, and data mixture; evaluates cross-modal transfer and multimodal in-context learning.
AI Model2026
Delivers image and video understanding plus a built-in event‑gated streaming gate — a unified 4B multimodal foundation model that uses codec-aligned tokenization to cut visual tokens by >75% and yield up to 3.5× wall‑clock inference speedup for streaming and long‑horizon video tasks.
AI Dataset2026
Provides a public test split of multimodal financial GUI interaction examples for evaluating agents that convert instructions and screenshots into grounded UI actions. Includes step-level screenshots, dialogue history, an OpenAI-style computer_use tool schema, and JSON next-action references; training data available on request.