Tag
The paper describes a metric-aware hybrid forecasting system for the CTF4Science Lorenz challenge, combining neural denoisers, ODE fitting, and histogram-tail substitution to optimize different metrics across nine task pairs, achieving a public leaderboard score of 83.85529.
Introduces horizon-constrained Rashomon sets to characterize how model multiplicity evolves in chaotic systems. The framework proves exponential contraction of predictive equivalence and develops decision-aligned algorithms that improve decision quality by 18-34%.