Multimodal Large Language Models which can answer complex questions on an image struggle to tell the time on analog clocks. This is probably due to the lack of images with clocks at different times in their training set. In this work we explore this issue with one of the latest MLLMs: GPT-4.1 to understand why MLLMs fail to tell the time and whether fine-tuning can solve the problem. The results show how models are making progress in reading the time on analog clocks. But have they really learned to do it, or have they only learned patterns in their training datasets? In this work we put the models to the test with different clocks to illustrate the limitations of MLLMs to abstract and generalize.

Analog clock hands indicate the time. Robots have a hard time reading the time on an analog clock, but machine learning can now do it accurately by using a method that was hard to use before. Computer vision has been able to read digital clocks' times for a long time by just looking at the numbers on the screen. But analog clocks are much harder to read because of things like how their designs vary and how shadows and reflections can make the hands hard to see. The new method allows us to read analog clocks in photos or videos taken in the real world.

It seems like just about everything works with Alexa in some form or another, and the Echo Wall Clock is no exception. This timekeeper is Amazon's latest attempt to make everyday products easier to use--and it kind of succeeds at doing that. When using the clock, you can set multiple timers and reminders, and, instead of using your Amazon Echo speaker to keep track of how much time is left, you can see how much time remains when you look at the analog clock. That's because the Echo Wall Clock, which retails on Amazon for $29.99, has 60 LED tick marks that light up to show you how much time is left. It takes several minutes to get the Echo Wall Clock up and running, and you'll need an Echo speaker nearby to do so.