CUTEPKQ / NEWTON
NEWTON: Agentic Planning for Physically Grounded Video Generation
Why this job: topics: text-to-video, video-generation, video-diffusion, video generation
What it does
Video generation models produce visually compelling results but systematically violate physical commonsense — on VideoPhy-2, the best model achieves only 32.6% joint accuracy. We identify a specification bottleneck: text prompts are a lossy compression of the physical world, omitting the parameters that fully determine dynamics, and no amount of model scaling can recover what was never specified. From this diagnosis we derive three properties that physics conditioning must satisfy — sufficiency, dynamism, and verifiability — and show that no existing approach satisfies all three.
Get started
python loop/run_loop.py "a moving billiard ball rolls across a table and strikes a stationary ball head-on, transferring its momentum so the struck ball rolls forward while the first ball stops" --max-turns 10 - Maintenance
- Active
- Last commit 3 months ago
- Stars
- 139
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 100% of the code
Is it looked after?
- Contributors
- 0
- Last 90 days
- Issues
- 0% closed
- 1 still open
- Pull requests
- 0 merged
- 0 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Python 100%
Topics
- agentic-ai
- agentic-video-generation
- ai-agents
- aigc
- computer-vision
- diffusion-models
- grpo
- llm-agent
- multimodal
- physics-simulation
- reinforcement-learning
- research-paper
Project record
- JobVideo
- StatusListed
- Commits7
- Forks3
- Watching0
- Stars in 28 days+1
- CreatedJun 13, 2026
- First seen by usNov 7, 2026
- CheckedNov 7, 2026

