Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey

• URL: http://arxiv.org/abs/2506.11102v1
• Date: Fri, 06 Jun 2025 17:52:18 GMT
• Title: Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey
• Authors: Jiachen Zhu, Menghui Zhu, Renting Rui, Rong Shan, Congmin Zheng, Bo Chen, Yunjia Xi, Jianghao Lin, Weiwen Liu, Ruiming Tang, Yong Yu, Weinan Zhang,
• Abstract summary: The transition from traditional large language models (LLMs) to more advanced AI agents represents a pivotal evolutionary step.<n>Existing evaluation frameworks often blur the distinctions between LLM chatbots and AI agents, leading to confusion among researchers selecting appropriate benchmarks.<n>This paper introduces a systematic analysis of current evaluation approaches, grounded in an evolutionary perspective.
• Score: 45.485318955120924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of large language models (LLMs), such as GPT, Gemini, and DeepSeek, has significantly advanced natural language processing, giving rise to sophisticated chatbots capable of diverse language-related tasks. The transition from these traditional LLM chatbots to more advanced AI agents represents a pivotal evolutionary step. However, existing evaluation frameworks often blur the distinctions between LLM chatbots and AI agents, leading to confusion among researchers selecting appropriate benchmarks. To bridge this gap, this paper introduces a systematic analysis of current evaluation approaches, grounded in an evolutionary perspective. We provide a detailed analytical framework that clearly differentiates AI agents from LLM chatbots along five key aspects: complex environment, multi-source instructor, dynamic feedback, multi-modal perception, and advanced capability. Further, we categorize existing evaluation benchmarks based on external environments driving forces, and resulting advanced internal capabilities. For each category, we delineate relevant evaluation attributes, presented comprehensively in practical reference tables. Finally, we synthesize current trends and outline future evaluation methodologies through four critical lenses: environment, agent, evaluator, and metrics. Our findings offer actionable guidance for researchers, facilitating the informed selection and application of benchmarks in AI agent evaluation, thus fostering continued advancement in this rapidly evolving research domain.

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