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As diffusion large language models (dLLMs) become more capable, they are moving from research settings to real-world \textit{serving}, where request management (such as scheduling and resource allocation) relies on accurate estimation of per-request inference cost. However, common cost proxies fall short for dLLMs: out...
DeepSTE is proposed, a deep spectral temporal embedding framework for dynamic graphs that learns RFF representations via Monte Carlo importance sampling with a tractable proposal distribution and adopts a data-dependent scale parameter to construct the frequency proposal distribution reflecting time–frequency uncertain...
PoP-RAG is proposed, which features a passage-on-edge (PoE) graph that links the passages with graph edges to resolve the one-to-many entity-passage mapping problem and enables targeted retrieval for each sub-query and ensures transparent response derivation logic, thus enhancing explainability.
Experiments show that LAUA substantially mitigates performance degradation under modality missingness across retrieval and regression tasks, attaining up to 20% relative improvement in MRR for retrieval and up to 24.3% relative improvement in MSE for regression.
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