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CAMMAR introduces a representation learning framework that organizes Arabic metaphorical meaning into nested lexical, cultural, and metaphorical subspaces using a staged semantic curriculum, achieving strong metaphor detection (AUC up to 0.84) on a new span-annotated dataset.
This paper proposes a performance-driven state abstraction method for reinforcement learning that directly optimizes decision quality, using a multi-timescale framework to jointly adapt the policy and a tree-structured abstraction. The algorithm refines or aggregates state space based on Q-value discrepancies, achieving better sample efficiency and faster replanning than baselines.