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The results suggest that the architectural prior provides a useful organization of contextual information, while exposure to missing-modality cases is still needed for robust fallback when auxiliary inputs are absent.
TATK, a Triple-Aware framework that couples Top-K Learning (TKL) with Knowledge-Grounded Verification (KGV) for LLM-based sequential recommendation, shows that structural evidence is most useful for recoverable top-M candidates with reliable KG support, and should be gated when metadata relations are sparse or noisy.
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