Temporal knowledge graph reasoning (TKGR) is crucial for capturing dynamic relational dependencies and inferring evolving facts. However, existing embedding-based and diffusion-based methods primarily rely on Euclidean point embeddings and face two limitations: (1) sparse entities may suffer from embedding crowding and norm-sensitive scoring, especially under limited supervision in long-tail temporal knowledge graphs; (2) uniform noise schedules in diffusion models ignore temporal heterogeneity and instance-level prediction difficulty. To address these challenges, we propose SimDiff-DNS (Simplex Diffusion with Dynamic Noise Scheduling), a simplex-anchored logit diffusion framework for TKGR. First, we represent entity states as probability distributions on the simplex and obtain continuous embeddings as convex combinations of learnable entity basis vectors. This simplex-coordinate parameterization provides a probabilistic view of uncertainty and alleviates norm-sensitive ranking behavior for sparse entities. Second, we introduce a difficulty-aware dynamic noise scheduling strategy for the training corruption process, which adjusts the noise applied to target-entity logits according to a bounded difficulty score. Extensive experiments on three TKGR datasets show that SimDiff-DNS consistently improves over the diffusion-based baseline (improving MRR by 3.25 points on ICEWS14) and achieves competitive or state-of-the-art results on several metrics. Additional diagnostic analyses further demonstrate its benefits for long-tail entities, calibration, and ranking stability.

Multi-modal Knowledge Graph Completion (MKGC) aims to infer missing links in multimodal knowledge graphs by leveraging structured triples together with auxiliary modalities such as text and images. Existing MKGC methods typically train with all modalities available, implicitly assuming consistent complementarity; however, this practice often induces modality dependence and modality competition under heterogeneous noise, which can hinder robust multi-modal fusion and limit overall performance.To address these issues, we propose MDBGF, a Modality Dropout and Bidirectional Gated Fusion framework for MKGC. MDBGF introduces a dynamic, probability-based modality dropout schedule. When the dropout is activated, MDBGF drops either the textual or visual modality during training while always preserving the structural information, encouraging the model to reduce over-reliance on any single auxiliary modality and to learn complementary cues under missing-modality conditions. When the dropout is not activated (i.e., all modalities are present), we further design a bidirectional gated fusion mechanism that enables mutual modulation between textual and visual modalities, enhancing cross-modal interaction and flexible fusion. In addition, we propose an adaptive proportional hybrid negative sampling strategy to strengthen MDBGF’s discriminative ability on hard negatives. Experiments on three benchmarks show that MDBGF consistently outperforms existing baselines and achieves new state-of-the-art results. Our code is available at https://anonymous.4open.science/r/MDBGF-AHNS.