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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
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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
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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

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