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The paper presents Prometheus, a framework that uses large language models to extract local causal claims from text and organizes them into navigable causal atlases, enabling deep causal research across diverse domains.
The paper introduces TTCD, a novel framework for temporal causal discovery from non-stationary time series data using transformer-based feature learning and reconstruction-guided signal distillation.