To build AI that children can intuitively understand and benefit from, designers need a design grammar that serves their developmental needs. This paper bridges artificial intelligence design for children - an emerging field still defining its best practices - and animation, a well established field with decades of experience in engaging children through accessible storytelling. Pairing Piagetian developmental theory with design pattern extraction from 52 works of animation, the paper presents a six scaffold framework that integrates design insights transferable to child centred AI design: (1) signals for visual animacy and clarity, (2) sound for musical and auditory scaffolding, (3) synchrony in audiovisual cues, (4) sidekick style personas, (5) storyplay that supports symbolic play and imaginative exploration, and (6) structure in the form of predictable narratives. These strategies, long refined in animation, function as multimodal scaffolds for attention, understanding, and attunement, supporting learning and comfort. This structured design grammar is transferable to AI design. By reframing cinematic storytelling and child development theory as design logic for AI, the paper offers heuristics for AI that aligns with the cognitive stages and emotional needs of young users. The work contributes to design theory by showing how sensory, affective, and narrative techniques can inform developmentally attuned AI design. Future directions include empirical testing, cultural adaptation, and participatory co design.

Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents. This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents. Despite the emergence of some heuristic approaches, a systematic design principle remains absent. To fill this void, we draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic memory -- structured schemata, flexible assimilation, and dynamic accommodation. This blueprint forges a clear path toward a more robust and efficient memory system for LLM-based reading comprehension. To this end, we develop CAM, a prototype implementation of Constructivist Agentic Memory that simultaneously embodies the structurality, flexibility, and dynamicity. At its core, CAM is endowed with an incremental overlapping clustering algorithm for structured memory development, supporting both coherent hierarchical summarization and online batch integration. During inference, CAM adaptively explores the memory structure to activate query-relevant information for contextual response, akin to the human associative process. Compared to existing approaches, our design demonstrates dual advantages in both performance and efficiency across diverse long-text reading comprehension tasks, including question answering, query-based summarization, and claim verification.

It was previously thought that children younger than 7 couldn't find efficient solutions to complex problems, but new research suggests that much earlier, children can happen upon known sorting algorithms used by computer scientists Complex problem-solving may arise earlier in a child's development than previously thought Children as young as 4 years old are capable of finding efficient solutions to complex problems, such as independently inventing sorting algorithms developed by computer scientists. The scientists behind the finding say these skills emerge far earlier than previously thought, and should force a rethink of developmental psychology. Take control of your brain's master switch to optimise how you think Experiments carried out by Swiss psychologist Jean Piaget and widely popularised in the 1960s asked children to physically sort a collection of sticks into length order, a task Piaget called seriation. His tests revealed until around age 7, children applied no structured strategies; they approached the problem in messy ways through trial and error. But new research by Huiwen Alex Yang and his colleagues at University of California, Berkeley, shows a minority of even 4-year-old children can develop algorithmic solutions to the same task, and by 5 years old more than a quarter are capable of the same thing.

Large language models (LLMs) have shown remarkable performance on various tasks, but existing evaluation benchmarks are often static and insufficient to fully assess their robustness and generalization in realistic scenarios. Prior work using evolutionary or adversarial data augmentation has improved evaluation diversity but lacks systematic control over perturbation types and multi-step complexity, limiting comprehensive robustness analysis. To address these gaps, we propose AutoEvoEval, an evolution-based evaluation framework for close-ended tasks such as multi-choice question answering. AutoEvoEval introduces 22 interpretable atomic evolution operations and supports multi-round compositions, enabling controlled generation of diverse, challenging, and realistic test samples. We conduct extensive experiments addressing four research questions on a broad set of open- and closed-source LLMs. Our results show that atomic operations cause an average accuracy drop of 7.283\%, with structure-disrupting or misleading semantic edits causing the largest declines. Model sensitivities vary significantly for the same perturbation, and combining multiple evolution steps amplifies adversarial effects by up to 52.932\%. These findings suggest current benchmarks may overestimate true model generalization and emphasize the need for evolution-aware robustness evaluation. Code and resources are available at: https://github.com/SYSUSELab/AutoEvoEval.

World Models help Artificial Intelligence (AI) predict outcomes, reason about its environment, and guide decision-making. While widely used in reinforcement learning, they lack the structured, adaptive representations that even young children intuitively develop. Advancing beyond pattern recognition requires dynamic, interpretable frameworks inspired by Piaget's cognitive development theory. We highlight six key research areas -- physics-informed learning, neurosymbolic learning, continual learning, causal inference, human-in-the-loop AI, and responsible AI -- as essential for enabling true reasoning in AI. By integrating statistical learning with advances in these areas, AI can evolve from pattern recognition to genuine understanding, adaptation and reasoning capabilities.

Despite excelling in high-level reasoning, current language models lack robustness in real-world scenarios and perform poorly on fundamental problem-solving tasks that are intuitive to humans. This paper argues that both challenges stem from a core discrepancy between human and machine cognitive development. While both systems rely on increasing representational power, the absence of core knowledge-foundational cognitive structures in humans-prevents language models from developing robust, generalizable abilities, where complex skills are grounded in simpler ones within their respective domains. It explores empirical evidence of core knowledge in humans, analyzes why language models fail to acquire it, and argues that this limitation is not an inherent architectural constraint. Finally, it outlines a workable proposal for systematically integrating core knowledge into future multi-modal language models through the large-scale generation of synthetic training data using a cognitive prototyping strategy.

Conservation is a critical milestone of cognitive development considered to be supported by both the understanding of quantitative concepts and the reversibility of mental operations. To assess whether this critical component of human intelligence has emerged in Vision Language Models, we have curated the ConserveBench, a battery of 365 cognitive experiments across four dimensions of physical quantities: volume, solid quantity, length, and number. The former two involve only transformational tasks, whereas the latter two involve non-transformational tasks assessing the understanding of quantitative concepts alone. Surprisingly, we find that while Vision Language Models are generally capable of conserving, they tend to fail at non-transformational tasks whose successes are typically considered to be evidence of the ability to conserve. This implies that the law of conservation, at least in concrete domains, may exist without corresponding conceptual understanding of quantity.

Are Multi-modal Large Language Models (MLLMs) stochastic parrots? Do they genuinely understand? This paper aims to explore the core cognitive abilities that human intelligence builds upon to perceive, comprehend, and reason in MLLMs. To this end, we propose CogDevelop2K, a comprehensive benchmark that spans 12 sub-concepts from primitive knowledge like object permanence and boundary to more complex abilities like intentionality understanding, structured via the developmental trajectory of a human mind. We evaluate 46 MLLMs on our benchmarks. Surprisingly, we observe a reversed cognitive developmental trajectory compared to humans. Comprehensively, we further evaluate the influence of evaluation strategies and prompting techniques. Website with this $\href{https://growing-ai-like-a-child.github.io/}{link}$.

Piaget's Theory of Cognitive Development (PTC) posits that the development of cognitive levels forms the foundation for human learning across various abilities. As Large Language Models (LLMs) have recently shown remarkable abilities across a wide variety of tasks, we are curious about the cognitive levels of current LLMs: to what extent they have developed and how this development has been achieved. To this end, we construct a benchmark CogLM (Cognitive Ability Evaluation for Language Model) based on PTC to assess the cognitive levels of LLMs. CogLM comprises 1,220 questions spanning 10 cognitive abilities crafted by more than 20 human experts, providing a comprehensive testbed for the cognitive levels of LLMs. Through extensive experiments across multiple mainstream LLMs with CogLM, we find that: (1) Human-like cognitive abilities have emerged in advanced LLMs (GPT-4), comparable to those of a 20-year-old human. (2) The parameter size and optimization objective are two key factors affecting the cognitive levels of LLMs. (3) The performance on downstream tasks is positively correlated with the level of cognitive abilities. These findings fill the gap in research on the cognitive abilities of LLMs, tracing the development of LLMs from a cognitive perspective and guiding the future direction of their evolution.

Starting from the Boolean notion of logical proportion in Piaget's sense, which turns out to be equivalent to analogical proportion, this note proposes a definition of analogical proportion between numerical values based on triangular norms (and dual co-norms). Frank's family of triangular norms is particularly interesting from this perspective. The article concludes with a comparative discussion with another very recent proposal for defining analogical proportions between numerical values based on the family of generalized means.