Text-to-image models can render recognizable cultural symbols, but cultural activity generation requires coordinated control over participants, actions, interactions, scenes, and culture-specific constraints. We propose CARE, an evidence-grounded visual checklist framework for controllable cultural activity image generation. Given an activity prompt, CARE builds source-traceable cultural evidence, converts it into an optimized prompt and fine-grained checklist, verifies generated images with concrete VLM-based visual checks, and uses failed checks for targeted prompt repair. We introduce CARE-1360, a dataset of 1,360 cultural activity prompts across eight countries, and evaluate generated images with a unified human protocol. Experiments show that CARE consistently improves over direct generation and retrieval-based prompt refinement baselines across cultural and visual quality dimensions. Results with both ZImage and Stable Diffusion XL suggest that evidence-grounded visual checklists provide an interpretable control mechanism that transfers across the tested generation backends.

Temporal knowledge graph forecasting predicts future facts from historical snapshots, and its accuracy hinges on how well a model exploits the historical evidence available to each query. Existing methods route every query through a single reasoning pipeline, yet queries with the same predicate form can rest on very different histories and call for different inductive biases. We observe that query histories fall into three regimes: sparse histories with few prior events, recurrent histories dominated by stable repetition, and ambiguous histories with many competing candidates. Building on this observation, we propose QTI-MoE, a Query-Type-aware Inductive-bias Mixture-of-Experts framework. QTI-MoE characterises each query with a soft type label derived from its historical prefix and forwards it to one of three experts whose memory pathways carry complementary biases: recency-weighted aggregation for sparse queries, count-based repetition for recurrent queries, and diversity-normalised scoring for ambiguous queries. QTI-MoE achieves state-of-the-art performance on four standard benchmarks, and a type-stratified analysis confirms that each expert peaks on its target regime.

Event forecasting requires modeling historical event data to predict future events, and achieving accurate predictions depends on effectively capturing the relevant historical information that aids forecasting. Most existing methods focus on entities and structural dependencies to capture historical clues but often overlook implicitly relevant information. This limitation arises from overlooking event semantics and deeper factual associations that are not explicitly connected in the graph structure but are nonetheless critical for accurate forecasting. To address this, we propose a dual-criteria constraint strategy that leverages event semantics for relevance modeling and incorporates a self-supervised semantic filter based on factual event associations to capture implicitly relevant historical information. Building on this strategy, our method, termed ITHI (Integrating Three types of Historical Information), combines sequential event information, periodically repeated event information, and relevant historical information to achieve context-aware event forecasting. We evaluated the proposed ITHI method on three public benchmark datasets, achieving state-of-the-art performance and significantly outperforming existing approaches. Additionally, we validated its effectiveness on two structured temporal knowledge graph forecasting dataset.

Narratives have been shown to be an effective way to communicate health risks and promote health behavior change, and given the growing amount of health information being shared on social media, it is crucial to study health-related narratives in social media. However, expert identification of a large number of narrative texts is a time consuming process, and larger scale studies on the use of narratives may be enabled through automatic text classification approaches. Prior work has demonstrated that automatic narrative detection is possible, but modern deep learning approaches have not been used for this task in the domain of online health communities. Therefore, in this paper, we explore the use of deep learning methods to automatically classify the presence of narratives in social media posts, finding that they outperform previously proposed approaches. We also find that in many cases, these models generalize well across posts from different health organizations. Finally, in order to better understand the increase in performance achieved by deep learning models, we use feature analysis techniques to explore the features that most contribute to narrative detection for posts in online health communities.