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University of Electronic Science and Technology of China
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Standard residual-based refinement routinely falters on steep gradients and phase transitions, but combining diffeomorphic coordinate mapping with Fourier spectral preconditioning cuts PINN errors down to $10^{-5}$ across stiff non-linear PDEs.
This survey provides a systematic taxonomy of recent research on rollout efficiency for reasoning-oriented reinforcement learning, classifying existing approaches from both mechanism and bottleneck perspectives.
EBL-Core, an execution-boundary conformance profile for deciding whether one canonical, fully materialized AI-generated candidate may receive action-scoped execution authority under explicit conditions, is specified.
