# Lost in Time: Clock and Calendar Understanding Challenges in Multimodal LLMs

arXiv: https://arxiv.org/abs/2502.05092 · HTML: https://mlsanity.com/paper/2502.05092/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Builds on (most cited first, 11 of 11)

- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?](https://mlsanity.com/paper/2408.13257/) (2024)
- [GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoning](https://mlsanity.com/paper/2312.12241/) (2023)
- [DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM](https://mlsanity.com/paper/2403.12488/) (2024)
- [ReMI: A Dataset for Reasoning with Multiple Images](https://mlsanity.com/paper/2406.09175/) (2024)
- [Are Language Models Puzzle Prodigies? Algorithmic Puzzles Unveil Serious Challenges in Multimodal Reasoning](https://mlsanity.com/paper/2403.03864/) (2024)
- [It's About Time: Analog Clock Reading in the Wild](https://mlsanity.com/paper/2111.09162/) (2021)
- [Evidence of Cognitive Deficits andDevelopmental Advances in Generative AI: A Clock Drawing Test Analysis](https://mlsanity.com/paper/2410.11756/) (2024)

## Built on by (most cited first, 10 of 10)

- [Large Language Model Reasoning Failures](https://mlsanity.com/paper/2602.06176/) (2026)
- [Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench](https://mlsanity.com/paper/2510.26865/) (2025)
- [Have Multimodal Large Language Models (MLLMs) Really Learned to Tell the Time on Analog Clocks?](https://mlsanity.com/paper/2505.10862/) (2025)
- [Measuring Iterative Temporal Reasoning with Time Puzzles](https://mlsanity.com/paper/2601.07148/) (2026)
- [It's Time to Get It Right: Improving Analog Clock Reading and Clock-Hand Spatial Reasoning in Vision-Language Models](https://mlsanity.com/paper/2603.08011/) (2026)
- [What Really Controls Temporal Reasoning in Large Language Models: Tokenisation or Representation of Time?](https://mlsanity.com/paper/2603.19017/) (2026)
- [SPAN: Benchmarking and Improving Cross-Calendar Temporal Reasoning of Large Language Models](https://mlsanity.com/paper/2511.09993/) (2025)
- [The Cartesian Shortcut: Re-evaluate Vision Reasoning in Polar Coordinate Space](https://mlsanity.com/paper/2605.09883/) (2026)
- [Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models](https://mlsanity.com/paper/2607.08317/) (2026)
- [It's Always 10:10: Reference Images Break a Bias That Prompts Only Dent](https://mlsanity.com/paper/2610.11320/) (2026)

## Abstract (arXiv)

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) textitClockQA, 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) textitCalendarQA, 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.