Large language models (LLMs) are increasingly used in role-playing applications, yet evaluating whether they maintain character-consistent behavior over extended interactions remains challenging. Existing benchmarks mostly focus on short exchanges, static long-context inputs, or isolated aspects of role-playing, making long-horizon failures difficult to diagnose. We present REMP (Relationship-Emotion-Memory-Persona), a structured event-probing benchmark for long-horizon role-playing dialogue. REMP constructs multi-chapter dialogue trajectories from character profiles and embeds diagnostic probe events targeting persona consistency, emotional consistency, memory integrity, and relational continuity. We further introduce REMP-EVAL, an anchor-based multi-turn evaluation protocol that assesses model behavior after event triggers using dimension-specific references. The current release contains 160 characters organized into 80 dialogue trajectories, each averaging about 60 sessions and 800 turns. Experiments on 11 LLMs show that memory integrity and emotional consistency are the main bottlenecks, indicating that long-horizon role-playing requires not only long-context retrieval but also sustained affective and relational consistency.

Large Vision-Language Models (LVLMs) commonly suffer from hallucinations, which appear as fabricating non-existent objects or outputting incorrect attributes and spatial relationships in images. Previous works empirically indicate that CLIP-style vision encoders tend to focus on primary objects while ignoring image details, which is one of the causes of hallucinations. To address this issue, we propose plug-and-play fine-grained visual experts (PLANE) comprising two components: LoRA visual experts operating on CLIP for multi-aspect image encoding, and an instruction-aware gate network for adaptive image feature fusion. During training, we construct a fine-grained image-caption pairs dataset, where images are described from three aspects: objects, attributes, and relations between objects. Image-caption pairs from different aspects are used to train the three fine-grained LoRA experts respectively. Aiming at comprehensively evaluate our method’s capabilities, we manually annotate a fine-grained hallucination evaluation benchmark for LVLMs which is GPT4-independent during evaluation phase (GIFT). We conduct experiments both on popular hallucination evaluation benchmarks and GIFT. The results show that our method effectively reduces hallucinations across models of different types and sizes while introducing minimal inference latency. Compared to advanced baselines, our model also demonstrates superior performance.