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An information-weighted cross-entropy loss that rescales token-level contributions using TF-IDF statistics, emphasizing semantically informative tokens while down-weighting ubiquitous ones is presented, offering a lightweight and principled way to mitigate memorization without disrupting standard training dynamics.
Achieving a 91.7% success rate in obstacle avoidance, Orbit-Planner redefines how satellite agents navigate dynamic environments without relying on fixed maps.
