The evaluation of large language models (LLMs) has traditionally relied on static benchmarks, a paradigm that poses two major limitations: (1) predefined test sets lack adaptability to diverse application domains, and (2) standardized evaluation protocols often fail to capture fine-grained assessments of domain-specific knowledge and contextual reasoning abilities. To overcome these challenges, we propose GuessArena, an adaptive evaluation framework grounded in adversarial game-based interactions. Inspired by the interactive structure of the Guess Who I Am? game, our framework seamlessly integrates dynamic domain knowledge modeling with progressive reasoning assessment to improve evaluation fidelity. Empirical studies across five vertical domains-finance, healthcare, manufacturing, information technology, and education-demonstrate that GuessArena effectively distinguishes LLMs in terms of domain knowledge coverage and reasoning chain completeness. Compared to conventional benchmarks, our method provides substantial advantages in interpretability, scalability, and scenario adaptability.

With rapid advancements in large language models (LLMs) across artificial intelligence, machine learning, and data sci-ence, there is a growing need for evaluation frameworks that go beyond traditional performance metrics. Conventional methods focus mainly on accuracy and computational metrics, often neglecting user experience and community interaction—key elements in open-source environments. This paper intro-duces a multi-dimensional, user-centered evaluation frame-work, integrating metrics like User Engagement Index (UEI), Community Response Rate (CRR), and a Time Weight Factor (TWF) to assess LLMs’ real-world impact. Additionally, we propose an adaptive weighting mechanism using Bayesian op-timization to dynamically adjust metric weights for more ac-curate model evaluation. Experimental results confirm that our framework effectively identifies models with strong user engagement and community support, offering a balanced, data-driven approach to open-source LLM evaluation. This frame-work serves as a valuable tool for developers and researchers in selecting and improving open-source models.