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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsSome multimodal AI models still struggle to read an ordinary analog clock from a picture. In a University of Edinburgh study presented at the ICLR 2025 Workshop, the best clock score was only 22.58% exact match for Gemini 2.0. That is a poor result on this small benchmark—not proof that every AI product always misreads clocks.
The task sounds trivial: look at a clock face and answer, “What time is shown on the clock in the given image?” But an image-based answer requires several operations at once. A model must locate both hands, distinguish their shapes, map their angles to numbers, account for the hour hand’s position between numerals, and then produce an exact time string.
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What the study actually tested
The paper, “Lost in Time: Clock and Calendar Understanding Challenges in Multimodal LLMs,” by Rohit Saxena, Aryo Pradipta Gema and Pasquale Minervini, evaluated seven multimodal large language models in a zero-shot setting. The authors describe the work as preliminary and use relatively small, purpose-built datasets.
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ClockQA: six kinds of clock faces
ClockQA contained 62 analog-clock images covering six variants:
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- standard clock faces;
- black dials;
- clocks without second hands;
- easy, on-the-hour examples;
- Roman-numeral faces; and
- arrow-style hands.
The visual style mattered. Roman numerals and stylized hands produced more errors, showing that the challenge is not simply knowing what a clock is. It is precise visual parsing followed by numerical reasoning.
CalendarQA: dates, weekdays and counting
The researchers also tested yearly calendar images covering 10 years, with six questions for each year. Examples included “Which day of the week is Christmas?” and “What is the 153rd day of the year?” These questions combine reading a grid with date lookup or day-counting.
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The headline numbers—and how to read them
| Task | Reported result | What it measures |
|---|---|---|
| ClockQA | Gemini 2.0: 22.58% exact match | Whether the complete clock answer exactly matched the expected answer |
| CalendarQA | GPT-o1: 80.0% accuracy | Correct answers across calendar questions |
| ClockQA size | 62 images | Six clock-face variants |
| CalendarQA size | 10 years, six questions per year | Image-based date, weekday and counting questions |
These metrics should not be merged into a general ranking. A clock exact-match score and a calendar accuracy score are different measurements, on different questions and datasets. GPT-o1’s stronger calendar result also did not mean it performed equally well on every calendar question type.
The precise results appear in the authors’ paper, “Lost in Time: Clock and Calendar Understanding Challenges in Multimodal LLMs”. A popular account in Futurism’s March 19, 2025 article described Gemini as the best of the tested models for clocks; the paper’s exact figure is the 22.58% Gemini 2.0 score shown above.
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Why an analog clock is harder than it looks
Small visual errors change the answer
On a digital display, the model can usually read four digits. On an analog face, the answer is encoded in angles and relative hand lengths. A slight error in locating a hand can change the minute, and the hour hand moves continuously rather than jumping from one numeral to the next.
Style variation breaks familiar shortcuts
Roman numerals, dark backgrounds, missing second hands and unusual arrow shapes alter the visual cues a model may rely on. A system that has learned a strong pattern for a conventional white clock can still fail when the same information is presented in a less familiar design.
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The answer requires structured inference
Recognizing two lines is only the beginning. The model must decide which is the hour hand, convert positions into minutes, reconcile the hour hand’s intermediate position, and format one exact answer. The paper links the errors to a combination of visual precision, numerical computation and structured inference.
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What the result does—and does not—show
- It does show: the seven tested multimodal models had a substantial weakness on this bounded analog-clock benchmark, with style and question design affecting performance.
- It does show: image-based calendar reasoning can fail in a different way, especially when a question requires counting or date arithmetic rather than a familiar lookup.
- It does not show: that all AI systems always fail to tell time.
- It does not show: how often a particular consumer app fails in ordinary scheduling, accessibility or assistant workflows.
- It does not show: that a model with the best clock score is generally “best at time.”
Lead author Rohit Saxena summarizes the motivation this way: “Understanding time from visual representations is a fundamental cognitive skill, yet it remains a challenge for multimodal large language models (MLLMs).”
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How to use AI safely when the image matters
For a low-stakes curiosity, asking an image model to read a clock can be entertaining. For alarms, medication schedules, transport, work deadlines or other consequential decisions, verify the answer against the image or a reliable digital time source. If you are evaluating a model, test several face styles and include both straightforward readings and questions that require date counting. Report the model version, image style, question type and metric rather than a single impression of whether it “understands time.”
Bottom line
Reading an analog clock is a deceptively compact test of multimodal reasoning. In this ICLR 2025 workshop study, Gemini 2.0 reached only 22.58% exact match on 62 clock images, while GPT-o1 reached 80.0% on a separate calendar benchmark. The numbers reveal a specific, benchmark-limited weakness in visual parsing and structured reasoning—not a universal inability to tell time.
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