Role-playing with large language models is fundamentally a *session-level* task, requiring agents to sustain character identity and interaction quality across extended multi-turn conversations. Yet existing evaluation and optimization methods remain largely turn-level, failing to capture long-horizon quality. We propose **DynSess**, a unified session-level framework for role-playing agents. **DynSess-Eval** scores complete dialogue sessions via rubrics targeting long-horizon behaviors. Leveraging its session-level rewards, we construct high-quality training trajectories through multi-turn lookahead search and train **DynSess-Character** with two complementary variants: DSPO (off-policy) and GSRPO (on-policy). Experiments show that DynSess-Eval aligns with human judgments substantially better than prior evaluators, and blind human evaluation further shows that DynSess-Character matches the strongest character model despite using substantially fewer parameters, while maintaining strong role consistency and interactive ability. The dataset and code are publicly available in https://github.com/Collab-Gen/DynSess.

While role-playing agents excel in short-term interactions, long-term conversations overwhelm context windows, motivating external memory frameworks. Current systems typically rely on persona-agnostic summarization, which records facts without persona-specific interpretation, yielding generic responses that compromise persona fidelity. To bridge this gap, we introduce RoleMemo, a dataset featuring four reasoning tasks where the factual fragments must be interpreted through the persona to reach the correct answer. Evaluation on RoleMemo exposes critical limitations of persona-agnostic frameworks. We thus propose DualMem, which decouples memory into two streams: factual cognition and persona-conditioned insight. Trained through Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), our framework with a 4B-parameter model outperforms zero-shot persona-agnostic frameworks powered by DeepSeek-V3.2 for sustained persona fidelity. Our resources are available at https://github.com/Collab-Gen/RoleMemo.

This paper describes our system participated in Task 6 of SemEval-2021: the task focuses on multimodal propaganda technique classification and it aims to classify given image and text into 22 classes. In this paper, we propose to use transformer based architecture to fuse the clues from both image and text. We explore two branches of techniques including fine-tuning the text pretrained transformer with extended visual features, and fine-tuning the multimodal pretrained transformers. For the visual features, we have tested both grid features based on ResNet and salient region features from pretrained object detector. Among the pretrained multimodal transformers, we choose ERNIE-ViL, a two-steam cross-attended transformers pretrained on large scale image-caption aligned data. Fine-tuing ERNIE-ViL for our task produce a better performance due to general joint multimodal representation for text and image learned by ERNIE-ViL. Besides, as the distribution of the classification labels is very unbalanced, we also make a further attempt on the loss function and the experiment result shows that focal loss would perform better than cross entropy loss. Last we have won first for subtask C in the final competition.