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This paper investigates semantic context drift in reasoning-class LLMs within hybrid decision support systems, proposing a mathematical model and a stability metric. A two-month experiment reveals latent goal-targeting drift and formulates engineering recommendations for control stability.
Introduces Drift-Aware Temporal Graph Rewiring (DATGR) to dynamically update co-occurrence edges in biomedical text graphs, capturing semantic drift without full retraining. Evaluated on BIOMRC, it achieves a mean AUROC improvement of 0.066 over static baselines while maintaining precision.
The paper proposes Geo-Anchored Cloud Removal (GACR), a framework that uses Observation-Anchored Residual Flow and Geo-Contextual Prior Alignment to remove clouds from optical remote sensing images while preserving semantic structures for downstream tasks.