Despite mastering complex tasks, cutting-edge AI systems are failing at simple time and date problems—raising serious questions about their readiness for real-world jobs like scheduling and automation.
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A new study suggests that some of the world's most advanced AI systems struggle to tell the time and calculate calendar dates.
While AI models can perform complex tasks such as writing essays and generating art, researchers say they have yet to master some skills that humans easily perform.
A team from the University of Edinburgh has shown that state-of-the-art AI models are unable to reliably interpret clock-hand positions or correctly answer questions about dates on calendars.
Unlike simply recognizing shapes, the team says understanding analog clocks and calendars requires a combination of spatial awareness, context, and basic math—something that remains challenging for AI.
Researchers say overcoming this could enable AI systems to power time-sensitive applications like scheduling assistants, autonomous robots, and tools for people with visual impairments.
The team tested whether AI systems that process text and images—known as multimodal large language models (MLLMs)—can answer time-related questions by looking at a picture of a clock or a calendar.
Researchers tested various clock designs, including some with Roman numerals, with and without second hands, and different colored dials.
Their findings show that AI systems, at best, got clock-hand positions right less than a quarter of the time. Mistakes were more common when clocks had Roman numerals or stylized clock hands. AI systems also did not perform any better when the second hand was removed, suggesting deep-seated issues with hand detection and angle interpretation.
The researchers asked AI models to answer various calendar-based questions, such as identifying holidays and working out past and future dates. The team found that even the best-performing AI model got date calculations wrong one-fifth of the time.
The findings are reported in a peer-reviewed paper that will be presented at the Reasoning and Planning for Large Language Models workshop at The Thirteenth International Conference on Learning Representations (ICLR) in Singapore on 28 April 2025.
Rohit Saxena of the University of Edinburgh's School of Informatics, who led the study, said: "Most people can tell the time and use calendars from an early age. Our findings highlight a significant gap in the ability of AI to carry out what are quite basic skills for people. These shortfalls must be addressed if AI systems are to be successfully integrated into time-sensitive, real-world applications, such as scheduling, automation and assistive technologies."
Aryo Gema, also of the School of Informatics, said, "AI research today often emphasizes complex reasoning tasks, but ironically, many systems still struggle with simpler, everyday tasks. Our findings suggest it's high time we addressed these fundamental gaps. Otherwise, integrating AI into real-world, time-sensitive applications might remain stuck at the eleventh hour."