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title: README
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# NU · World Model & Embodied AI

We build and benchmark world models, embodied agents, and world-action models — systems that simulate, predict, and act in the physical world.

Our public releases include video-generation benchmarks, physics-aware world models, robot policies, action models, datasets, and open evaluators grounded in human judgments of the physical world.

## Part of the Moxin Open AI Ecosystem

NU World Model & Embodied AI is part of the [Moxin Organization](https://github.com/moxin-org) open AI ecosystem, extending Moxin’s work from language models, agents, applications, and efficient inference into world models and physical intelligence.

[Moxin Website](https://moxin-ai.org) · [Moxin GitHub](https://github.com/moxin-org) · [Moxin Hugging Face](https://huggingface.co/moxin-org)

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## Current Releases

### PhyGround — the ruler

PhyGround is a benchmark for evaluating the physical plausibility of text-and-image-to-video generation.

It includes 250 curated prompts, a taxonomy spanning 13 physical laws across solid-body, fluid, and optical domains, and a quality-controlled human study with 459 annotators and more than 37,000 fine-grained labels.

We also release PhyJudge-9B, an open vision-language evaluator trained on the human ratings.

[Project Page](https://phyground.github.io/) · [GitHub](https://github.com/NU-World-Model-Embodied-AI/PhyGround) · [Dataset](https://huggingface.co/datasets/NU-World-Model-Embodied-AI/phyground) · [PhyJudge-9B](https://huggingface.co/NU-World-Model-Embodied-AI/phyjudge-9B)

### PhyWorld — a model trained against the ruler

PhyWorld is a physics-aware video-generation world model post-trained from Wan2.2-I2V-A14B.

Its two-stage training combines flow-matching fine-tuning for temporal coherence with preference optimization using physics judgments derived from the PhyGround human-annotation pool.

[Project Page](https://nu-world-model-embodied-ai.github.io/PhyWorld/) · [GitHub](https://github.com/NU-World-Model-Embodied-AI/PhyWorld) · [Model](https://huggingface.co/NU-World-Model-Embodied-AI/phyworld)

### Flash-WAM — efficient world-action models

Flash-WAM is a modality-aware distillation framework that accelerates joint video-action world models while preserving embodied-task performance.

[GitHub](https://github.com/NU-World-Model-Embodied-AI/Flash-WAM) · [Browse Releases](https://huggingface.co/NU-World-Model-Embodied-AI)

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## A Shared Research Loop

PhyGround and PhyWorld form a shared research loop: the human annotations used to evaluate generated videos also provide the preference signals used to train a more physically coherent world model.

More releases in robot policies, world-action models, efficient embodied intelligence, and physical-reasoning benchmarks are on the way.