It's Always 10:10: Reference Images Break a Bias That Prompts Only Dent
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Abstract (arXiv)
Text-to-image models appear to reproduce the habits of the photographs they learned from. Analog clocks are an extreme case: in advertising, watches almost always show 10:10, and generated clocks return to 10:10 even when another time is requested. We measure this bias and test three ways of overcoming it on 52 models available on the Magnific platform, with a replication on Higgsfield. Every image shows three identical clocks that must show 2:35, 6:50 and 11:20. The description of the object is fixed and only the request about the time changes: no time (A), the time in digits (B), the hand positions described by construction relative to the dial numerals (C), or the same description plus a drawn reference dial (D). Two AI readers read all 1,799 images blind from coded copies, with a third reader and the author settling disagreements (dial-level agreement 96.0% and 97.3%). With no time requested, 67% of the images have all three clocks at 10:10. On the 20 current models, all three clocks are correct in 34% of the images with digits, 30% with the hands described in words and 75% with the reference dial (D-B: +37 points, 95% CI +28 to +45); we found no evidence that describing the hands in words beats the digits (C-B: -4 points, CI -10 to +1). The replication on the 12 models shared by both platforms gives the same picture (B 54%, C 50%, D 81%). Writing the time reduces the bias but leaves two thirds of the images of the 20 current models with at least one wrong clock; adding a drawn reference raises full accuracy to three quarters and almost eliminates images entirely at 10:10. We release all images, prompts, raw readings and a script that recomputes every result.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 8 Oct 2026.
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References · 21
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
- 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,439
- 2023GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignmentcited by 946
- 2023Extracting Training Data from Diffusion Modelscited by 723
- 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
- 2024Evaluating Text-to-Visual Generation with Image-to-Text Generationcited by 465
- 2024ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignmentcited by 455
- 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 418
- 2023TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answeringcited by 416
- 2024MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmarkcited by 362
- 2023T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generationcited by 253
- 2023Stable Bias: Analyzing Societal Representations in Diffusion Modelscited by 225
9 more
- 2024GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generationcited by 130
- 2025GenEval 2: Addressing Benchmark Drift in Text-to-Image Evaluationcited by 42
- 2024Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2)cited by 17
- 2021It's About Time: Analog Clock Reading in the Wildcited by 14
- 2025Lost in Time: Clock and Calendar Understanding Challenges in Multimodal LLMscited by 10
- 2025MultiRef: Controllable Image Generation with Multiple Visual Referencescited by 9
- 2026CollabEval: Enhancing LLM-as-a-Judge via Multi-Agent Collaborationcited by 4
- 2026SCHEMA for Gemini 3 Pro Image: A Structured Methodology for Controlled AI Image Generation on Google's Native Multimodal Modelcited by 1
- 2026It's Time to Get It Right: Improving Analog Clock Reading and Clock-Hand Spatial Reasoning in Vision-Language Modelscited by 1
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