Evaluating Cognitive Age Alignment in Interactive AI Agents
Summary
This paper introduces ChildAgentEval, a psychometrically grounded benchmark for assessing cognitive age alignment in MLLM-based agents, comparing their reasoning against human developmental stages.
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Paper page - Evaluating Cognitive Age Alignment in Interactive AI Agents
Source: https://huggingface.co/papers/2605.17894
Abstract
ChildAgentEval presents a psychometrically grounded benchmark for assessing cognitive age alignment in MLLM-based agents by comparing their reasoning performance against human developmental stages.
Whileagentic AIand its coremultimodal 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 groundedinteractive benchmarkfor evaluatingcognitive age alignmentin MLLM-based agents. ChildAgentEval systematically compares thereasoning performanceof various MLLM-based interactive agents against age-specific humandevelopmental stages, exposing where currentagentic AIsystems can and cannot simulate age-specific cognitive behavior.
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