Document-level relation extraction (DocRE) requires integrating scattered evidence to uncover challenging implicit relations. While recent LLM-based approaches leverage rich prior knowledge for relation prediction, they suffer from two limitations. Pipeline-based approaches cause irrecoverable candidate pruning, and the absence of explicit logical constraints leaves spurious over-prediction unsuppressed. To address this, we propose Gated Logical-rule INTernalization (GLINT), a novel DocRE framework that integrates an LLM with end-to-end inductive rule reasoning within a fully differentiable architecture. GLINT preserves per-pair signals across all entity pairs through a Fused Fact Tensor with Gumbel-softmax selection, and refines LLM predictions via an inductive rule reasoner with per-atom learnable negation gates. Extensive experiments on three benchmark datasets show that GLINT outperforms existing LLM-based baselines.

Multimodal Knowledge Graphs (MMKGs) provide structured multimodal grounding for Multimodal Large Language Models (MLLMs), while Multimodal Entity Alignment (MMEA) is essential for integrating heterogeneous MMKGs. However, existing MMEA methods remain vulnerable to noisy modalities and unreliable pseudo-supervision. We propose CRMEA, a large–small collaborative framework for unsupervised MMEA that unifies PTM-based retrieval and MLLM-based reasoning through a closed-loop optimization process. CRMEA introduces a text-centric adaptive cross-modal coordination mechanism to regulate noisy visual evidence, together with a retrieve-and-vote strategy for reliable pseudo-supervision. Extensive experiments show that CRMEA consistently outperforms existing unsupervised MMEA methods and achieves performance competitive with state-of-the-art supervised approaches, while maintaining strong robustness, adaptability across heterogeneous backbones, and substantially improved cost-effectiveness over exhaustive MLLM-based alignment. Code and data are available at https://github.com/nsai-szu/CRMEA.

Temporal knowledge graphs (TKGs) serve as powerful tools for storing and modeling dynamic facts, holding immense potential in anticipating future facts. Since future facts are inherently unknowable, effectively modeling the intricate temporal structure of historical facts becomes paramount for accurate prediction. However, current models often rely heavily on fact recurrence or periodicity, leading to information loss due to prolonged evolutionary processes. Notably, the occurrence of one fact always influences the likelihood of another. To this end, we propose HTCCN, a novel Hawkes process-based temporal causal convolutional network designed for temporal reasoning under extrapolation settings. HTCCN employs a temporal causal convolutional network to model the historical interdependence of facts and leverages Hawkes to model link formation processes inductively in TKGs. Importantly, HTCCN introduces dual-level dynamics to comprehensively capture the temporal evolution of facts. Rigorous experimentation on four real-world datasets underscores the superior performance of HTCCN.