Papers › CogDevelop2K: Reversed Cognitive Development in Multimodal Large Language Models
CogDevelop2K: Reversed Cognitive Development in Multimodal Large Language Models
Yijiang Li, Qingying Gao, Haoran Sun, Haiyun Lyu, Dezhi Luo, Hokin Deng
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 $\href{https://growing-ai-like-a-child.github.io/}{link}$.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections