Multimodal Sarcasm Understanding (MSU) comprises multiple subtasks, demanding both incongruity perception and intent reasoning. However, this progress is impeded by two bottlenecks. First, the lack of a unified benchmark for holistic satirical cognition hinders comprehensive evaluation of MSU. Second, jointly modeling these heterogeneous subtasks often leads to feature entanglement. Specifically, while subtasks share a dependence on incongruity, they diverge in granular focus, causing specific execution patterns to erode the fundamental perception capability. To address these challenges, we make two contributions. First, we introduce DocMSU-PLUS, a comprehensive benchmark covering five cognitive dimensions of MSU. All tasks are reformulated into multiple-choice questions (MCQs), enabling a unified accuracy-based evaluation. Second, we propose the Dual Orthogonal Stream Experts (DOSE) framework. DOSE structurally decouples experts into orthogonal shared perception and private execution streams to physically block gradient interference between tasks. Experiments demonstrate that DOSE achieves superior performance on DocMSU-PLUS, effectively balancing general perception with task-specific adaptation.

Event prediction plays a critical role in high-stakes applications such as military operations, public safety, and healthcare. Current methods learn temporal knowledge graphs to predict events at future timestamps, and the predictions directly influence decision-making and resource allocation. However, these methods lack rigorous uncertainty quantification, which limits their reliability for decision-making, especially in high-stakes scenarios where the cost of errors is high. In this paper, we propose CFEP, a conformal prediction framework tailored for event prediction to address this challenge. This is achieved through end-to-end optimization that ensures coverage while improving efficiency. Specifically, we first introduce non-conformity score diffusion, which captures both topological and temporal uncertainty in temporal knowledge graphs. Additionally, we propose an efficiency-aware optimization algorithm to reduce the coverage gap and improve computational efficiency. Experimental results on three public datasets demonstrate that our approach consistently guarantees statistical coverage while improving efficiency. The code and datasets are available at https://github.com/hucheng-IIE/CFEP.