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It is demonstrated that model selection, retrieval behaviour, and hardware limits must be evaluated separately when deploying open-weight LLMs for Turkish domain documents.
FineX is introduced, which factorizes fine-grained cues into RGB appearance, pose heatmap geometry, and skeletal-graph topology and raises mean class accuracy on Gym99, Gym288, and Diving48 without textual supervision or large-scale vision-language pre-training.
DC-FEN, a MobileNetV3-based design that models spatial-token relations and channel interactions in parallel and injects them through gated residual fusion is introduced and shows that adding intermediate transfer constraints does not guarantee a stronger student.
This work proposes Language-Structured Relational Q-Learning, instantiated through an Ego-Centric Relational Q-Network (ERQ-Net), which jointly learns inter-vehicle relevance and action values from dynamic traffic graphs, and formalises this discrepancy as a recognition-control gap.
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