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.

Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the basic types, which is characterized by its particular-to-general thinking process and the non-uniqueness of its answers. The inductive mode is crucial for knowledge generalization and aligns better with human cognition, so it is a fundamental mode of learning, hence attracting increasing interest. Despite the importance of inductive reasoning, there is no systematic summary of it. Therefore, this paper presents the first comprehensive survey of inductive reasoning for LLMs. First, methods for improving inductive reasoning are categorized into three main areas: post-training enhancement, test-time exploration, and data augmentation. Then, current benchmarks of inductive reasoning are summarized, and a unified sandbox-based evaluation approach with the observation coverage metric is derived. Finally, we offer some analyses regarding the source of inductive ability and how simple model architectures and data help with inductive tasks, providing a solid foundation for future research.

Personalized large language models (LLMs) have attracted great attention in many applications, such as emotional support and role-playing. However, existing works primarily focus on modeling explicit character profiles, while ignoring the underlying personality traits that truly shape behaviors and decision-making, hampering the development of more anthropomorphic and psychologically-grounded AI systems. In this paper, we explore the modeling of Big Five personality traits, which is the most widely used trait theory in psychology, and propose P-React, a mixture of experts (MoE)-based personalized LLM. Particularly, we integrate a Personality Specialization Loss (PSL) to better capture individual trait expressions, providing a more nuanced and psychologically grounded personality simulacrum. To facilitate research in this field, we curate OCEAN-Chat, a high-quality, human-verified dataset designed to train LLMs in expressing personality traits across diverse topics. Extensive experiments demonstrate the effectiveness of P-React in maintaining consistent and real personality.