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This paper introduces "Crackilyaefimov Longitudinal Kontakt-RARL" (CK-LKR), a conceptual framework combining longitudinal analysis with a Kontakt-based relational autoregressive reinforcement learning (RARL) approach. We define CK-LKR, derive its core components, present a simulated experimental setup, and report results demonstrating improved temporal stability and contact-aware policy adaptation in benchmark simulations.

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