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arXiv cs.LG
arXiv cs.LG
6/19/2026
When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting

When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting

Short summary

Guard framework distills knowledge from misaligned foundation models into lightweight forecasters using contextual routing and uncertainty gating. Achieves strong scientific forecasting on meteorology, carbon flux, soil moisture, and energy grids with reduced computational cost suitable for edge deployment.

  • •Guard uses uncertainty-aware routing to select relevant teachers from multiple foundation models
  • •Tested on four climate-critical domains with 28.5% improvement on hardest instances
  • •Enables high-precision forecasting on resource-constrained edge devices

Generated with AI, which can make mistakes.

#research-breakthrough#open-source#ai-tools
Read full article at arXiv cs.LG

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