Proposal: Applying Recurrent-Depth Architectures for Unbiased Meteorological LLM Forecasting

Hi Kye, I am exploring the limits of recurrent-depth transformers in physical world reasoning, specifically processing climate data tensors.

I built an open-source pipeline that extracts bias-free 1D vertical atmospheric profile matrices from JMA's JRA-3Q datasets to let models reason purely based on fluid dynamics without geographical prompt bias. I verified that advanced LLMs can successfully predict severe mesoscale systems (e.g., historical linear rainbands) 24 hours in advance under this setup.

I am planning to integrate OpenMythos recursive base blocks into my local_ai.py node to build an edge-computing climate forecasting LLM.

Highly appreciate your work. Here is the prototype and live demo:

• Repository: https://github.com/name-5096/jra3q-ai-forecaster
• Live App: https://jra3q-ai-forecaster-d3bjvmaprm4sish9c844yt.streamlit.app/