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This paper describes two pre-specified empirical tests to evaluate whether phase-derived features improve prediction accuracy for commitment or contradiction endpoints, but results show no significant improvement over baseline models.
DSWorld introduces a Data Science World Model that predicts environment state transitions to reduce costly trial-and-error in autonomous agents, achieving 14x acceleration in RL training and 3-6x in inference while maintaining competitive performance.