Learns dynamics-aligned latent representations for parametric PDE forecasting using masked-latent predictive pretraining, a geometry projector that aligns latent trajectory geometry, and a physics-structured latent predictor that separates shared evolution from parameter-dependent responses.
Analyzes why self-evolving reasoning models collapse under repeated self-training and proposes R-Quest: a feedback-driven pipeline that trains solvers to reject invalid questions and uses a frozen base model to detect task-level repetition, filtering training data to sustain multi-round gains.