Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.17894 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916022435446784 |
|---|---|
| author | Shen, Yifan Zhang, Jiawen Xu, Jian Kim, Junho Lourentzou, Ismini Cao, Xu Huang, Meihuan |
| author_facet | Shen, Yifan Zhang, Jiawen Xu, Jian Kim, Junho Lourentzou, Ismini Cao, Xu Huang, Meihuan |
| contents | While agentic AI and its core multimodal large language models (MLLMs) have demonstrated remarkable promise in language and visual reasoning across domains ranging from daily life to advanced scientific research, a profound gap remains between artificial and human intelligence. Despite the integration of powerful tools and advanced MLLMs, state-of-the-art AI agents frequently fail at foundational, seemingly simple tasks that a child can resolve with ease. Inspired by the Wechsler Intelligence Scale for Children (WISC), we introduce ChildAgentEval, the first psychometrically grounded interactive benchmark for evaluating cognitive age alignment in MLLM-based agents. ChildAgentEval systematically compares the reasoning performance of various MLLM-based interactive agents against age-specific human developmental stages, exposing where current agentic AI systems can and cannot simulate age-specific cognitive behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17894 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluating Cognitive Age Alignment in Interactive AI Agents Shen, Yifan Zhang, Jiawen Xu, Jian Kim, Junho Lourentzou, Ismini Cao, Xu Huang, Meihuan Artificial Intelligence While agentic AI and its core multimodal large language models (MLLMs) have demonstrated remarkable promise in language and visual reasoning across domains ranging from daily life to advanced scientific research, a profound gap remains between artificial and human intelligence. Despite the integration of powerful tools and advanced MLLMs, state-of-the-art AI agents frequently fail at foundational, seemingly simple tasks that a child can resolve with ease. Inspired by the Wechsler Intelligence Scale for Children (WISC), we introduce ChildAgentEval, the first psychometrically grounded interactive benchmark for evaluating cognitive age alignment in MLLM-based agents. ChildAgentEval systematically compares the reasoning performance of various MLLM-based interactive agents against age-specific human developmental stages, exposing where current agentic AI systems can and cannot simulate age-specific cognitive behavior. |
| title | Evaluating Cognitive Age Alignment in Interactive AI Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.17894 |