# Evaluating Cognitive Age Alignment in Interactive AI Agents

arXiv: https://arxiv.org/abs/2605.17894 · HTML: https://mlsanity.com/paper/2605.17894/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 24 of its 58 references that are in the map.
- First posted on arXiv 18 May 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)
- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 24 of 24)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://mlsanity.com/paper/2310.18018/) (2023)
- [Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions](https://mlsanity.com/paper/2507.05257/) (2025)
- [Emergent autonomous scientific research capabilities of large language models](https://mlsanity.com/paper/2304.05332/) (2023)
- [MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research](https://mlsanity.com/paper/2505.19955/) (2025)
- [What is the Visual Cognition Gap between Humans and Multimodal LLMs?](https://mlsanity.com/paper/2406.10424/) (2024)
- [Evidence of interrelated cognitive-like capabilities in large language models: Indications of artificial general intelligence or achievement?](https://mlsanity.com/paper/2310.11616/) (2023)
- [Working Memory Capacity of ChatGPT: An Empirical Study](https://mlsanity.com/paper/2305.03731/) (2023)
- [Comparing Machines and Children: Using Developmental Psychology Experiments to Assess the Strengths and Weaknesses of LaMDA Responses](https://mlsanity.com/paper/2305.11243/) (2023)
- [Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Morality](https://mlsanity.com/paper/2510.11254/) (2025)
- [Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions](https://mlsanity.com/paper/2506.13510/) (2025)
- [Toward Cognitive Supersensing in Multimodal Large Language Model](https://mlsanity.com/paper/2602.01541/) (2026)
- [The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks](https://mlsanity.com/paper/2410.07391/) (2024)
- [Benchmarking LLMs for Mimicking Child-Caregiver Language in Interaction](https://mlsanity.com/paper/2412.09318/) (2024)
- [Large Language Models as Psychological Simulators: A Methodological Guide](https://mlsanity.com/paper/2506.16702/) (2025)
- [Automatic Annotation of Grammaticality in Child-Caregiver Conversations](https://mlsanity.com/paper/2403.14208/) (2024)
- [Visual Large Language Models Exhibit Human-Level Cognitive Flexibility in the Wisconsin Card Sorting Test](https://mlsanity.com/paper/2505.22112/) (2025)
- [SproutBench: A Benchmark for Safe and Ethical Large Language Models for Youth](https://mlsanity.com/paper/2508.11009/) (2025)
- [Evaluating LLM Safety Across Child Development Stages: A Simulated Agent Approach](https://mlsanity.com/paper/2510.05484/) (2025)
- [Children's Intelligence Tests Pose Challenges for MLLMs? KidGym: A 2D Grid-Based Reasoning Benchmark for MLLMs](https://mlsanity.com/paper/2603.20209/) (2026)
- [Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap](https://mlsanity.com/paper/2508.18646/) (2025)
- [Doing Things with Words: Rethinking Theory of Mind Simulation in Large Language Models](https://mlsanity.com/paper/2510.13395/) (2025)

## Built on by (most cited first, 2 of 2)

- [Decoding Children's Gait Behavior](https://mlsanity.com/paper/2608.00371/) (2026)
- [ChronoVision: Temporal Reasoning via Latent State Reconstruction](https://mlsanity.com/paper/2608.05631/) (2026)

## Abstract (arXiv)

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.