Neural networks can ace short-horizon predictions — but quietly fail at long-term stability.
A new paper dives deep into the hidden chaos lurking in multi-step forecasts: Tiny weight changes (as small as 0.001) can derail predictions
Near-zero Lyapunov exponents don’t guarantee system stability
Short-horizon validation may miss critical vulnerabilities
Tools from chaos theory — like bifurcation diagrams and Lyapunov analysis — offer clearer diagnostics
The authors propose a “pinning” technique to constrain output and control instability
Bottom line: local performance is no proxy for global reliability. If you care about long-horizon trust in AI predictions — especially in time-series, control, or scientific models — structural stability matters.
#AI #MachineLearning #NeuralNetworks #ChaosTheory #DeepLearning #ModelRobustness
https://www.sciencedirect.com/science/article/abs/pii/S0893608025004514