Lost in Time: Clock and Calendar Understanding Challenges in Multimodal
  LLMs
Authors: name: Rohit Saxena, name: Aryo Pradipta Gema, name: Pasquale Minervini

Understanding time from visual representations is a fundamental cognitive
skill, yet it remains a challenge for multimodal large language models (MLLMs).
In this work, we investigate the capabilities of MLLMs in interpreting time and
date through analogue clocks and yearly calendars. To facilitate this, we
curated a structured dataset comprising two subsets: 1) ClockQA,
which comprises various types of clock styles-standard, black-dial,
no-second-hand, Roman numeral, and arrow-hand clocks-paired with time related
questions; and 2) CalendarQA, which consists of yearly calendar
images with questions ranging from commonly known dates (e.g., Christmas, New
Year's Day) to computationally derived ones (e.g., the 100th or 153rd day of
the year). We aim to analyse how MLLMs can perform visual recognition,
numerical reasoning, and temporal inference when presented with time-related
visual data. Our evaluations show that despite recent advancements, reliably
understanding time remains a significant challenge for MLLMs.