Most adaptive basis models fall apart when non-stationary signals shift or out-of-distribution noise creeps in. Even worse, standard models tend to stay overconfident right when their predictions are completely wrong.
To solve this, we developed AstralModel, introducing Spectral Resonance Decomposition (SRD) to bridge the gap between adaptive basis modeling and calibrated uncertainty.
Why check it out?
Filters Out Transient Noise: SRD isolates fundamental latent oscillatory modes while suppressing high-frequency non-stationary noise.
Causal-Invariant Generalization: Learns structural representations that remain robust under severe distribution shifts.
Calibrated Uncertainty Bounds: Provides reliable confidence intervals for risk-sensitive and safety-critical tasks.
Fully Open-Source: Includes complete PyTorch implementation, synthetic benchmarks, and real-world experiments.
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💻 GitHub Repo: https://github.com/Aditya7129p/astral-model
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