Empathetic dialogue requires not only recognizing a user’s emotional state but also making strategy-aware, context-sensitive decisions throughout response generation.However, the lack of a comprehensive empathy strategy framework, explicit task-aligned multi-stage reasoning, and high-quality strategy-aware data fundamentally limits existing approaches, preventing them from effectively modeling empathetic dialogue as a complex, multi-stage cognitive and decision-making process.To address these challenges, we propose STRIDE-ED, a STRategy-grounded, Interpretable, and DEep reasoning framework that models Empathetic Dialogue through structured, strategy-conditioned reasoning.To support effective learning, we develop a strategy-aware data refinement pipeline integrating LLM-based annotation, multi-model consistency-weighted evaluation, and dynamic sampling to construct high-quality training data aligned with empathetic strategies.Furthermore, we adopt a two-stage training paradigm that combines supervised fine-tuning with multi-objective reinforcement learning to better align model behaviors with target emotions, empathetic strategies, and response formats.Extensive experiments demonstrate that STRIDE-ED generalizes across diverse open-source LLMs and consistently outperforms existing methods on both automatic metrics and human evaluations.

Information diffusion prediction is crucial for understanding how information spreads within social networks, addressing both macroscopic and microscopic prediction tasks. Macroscopic prediction assesses the overall impact of diffusion, while microscopic prediction focuses on identifying the next user likely to be influenced. However, few studies have focused on both scales of diffusion. This paper presents HyperIDP, a novel Hypergraph-based model designed to manage both macroscopic and microscopic Information Diffusion Prediction tasks. The model captures interactions and dynamics of cascades at the macro level with hypergraph neural networks (HGNNs) while integrating social homophily at the micro level. Considering the diverse data distributions across social media platforms, which necessitate extensive tuning of HGNN architectures, a search space is constructed to accommodate diffusion hypergraphs, with optimal architectures derived through differentiable search strategies. Additionally, cooperative-adversarial loss, inspired by multi-task learning, is introduced to ensure that the model can leverage the advantages of the shared representation when handling both tasks, while also avoiding potential conflicts. Experimental results show that the proposed model significantly outperforms baselines.