Large language models have made role-playing agents viable as interactive characters, virtual companions, game agents, and social actors. The harder problem, however, is not producing one convincing response but sustaining a coherent role through long, changing, and sometimes adversarial interactions. This survey examines recent work on role consistency: the maintenance of identity, character knowledge, expressive style, memory, values, behavioral logic, and safety boundaries over time. We organize the literature around role specification, role-state maintenance, role-aware reasoning, role-consistent alignment, and evaluation.

Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thought (CoT) have further elevated their performance in handling complex logical problems. Despite these advances, high-quality reasoning remains heavily reliant on manual static prompts and is sensitive to decoding configurations and task distributions, leading to performance fluctuations and limited transferability. Existing automatic prompt optimization methods typically adopt single-agent local search, failing to simultaneously optimize prompts and decoding hyperparameters within a unified framework to achieve stable global improvements. To address this limitation, we propose Agent-GWO, a dynamic prompt optimization framework for complex reasoning. Specifically, we unify prompt templates and decoding hyperparameters as inheritable agent configurations. By leveraging the leader-follower mechanism of the Grey Wolf Optimizer (GWO), we automatically select three leader agents (𝛼, 𝛽, and 𝛿) to guide the collaborative updates of the remaining agents, enabling iterative convergence toward robust optimal reasoning configurations that can be seamlessly integrated for inference. Extensive experiments on multiple mathematical and hybrid reasoning benchmarks across diverse LLM backbones show that Agent-GWO consistently improves accuracy and stability over existing prompt optimization methods.