Synthetic History: Evaluating Visual Representations of the Past in Diffusion Models
Authors: Maria-Teresa De Rosa Palmini
Eva Cetinic
Venue: ICLR 2026 Poster (2026)
Keywords: Generative AI Evaluation
Diffusion models
Synthetic Imagery
Cultural Bias in AI
Historical Representation
Primary area: datasets and benchmarks
TL;DR: We introduce a benchmark and dataset for evaluating how text-to-image models represent history.

Abstract: As Text-to-Image (TTI) diffusion models become increasingly influential in content creation, growing attention is being directed toward their societal and cultural implications. While prior research has primarily examined demographic and cultural biases, the ability of these models to accurately represent historical contexts remains largely underexplored. To address this gap, we introduce a benchmark for evaluating how TTI models depict historical contexts. The benchmark combines HistVis, a dataset of 30,000 synthetic images generated by three state-of-the-art diffusion models from carefully designed prompts covering universal human activities across multiple historical periods, with a reproducible evaluation protocol. We evaluate generated imagery across three key aspects: (1) Implicit Stylistic Associations: examining default visual styles associated with specific eras; (2) Historical Consistency: identifying anachronisms such as modern artifacts in pre-modern contexts; and (3) Demographic Representation: comparing generated racial and gender distributions against historically plausible baselines. Our findings reveal systematic inaccuracies in historically themed generated imagery, as TTI models frequently stereotype past eras by incorporating unstated stylistic cues, introduce anachronisms, and fail to reflect plausible demographic patterns. By providing a reproducible benchmark for historical representation in generated imagery, this work provides an initial step toward building more historically accurate TTI models.

Published as a conference paper at ICLR 2026
SYNTHETIC HISTORY: EVALUATING VISUAL REPRE- SENTATIONS OF THE PAST IN DIFFUSION MODELS
Maria-Teresa De Rosa Palmini
Eva Cetinié University Zurich Zurich, Switzerland eva cea..etinnn www.ceiiiincee craacctiiii@uuh.c ch
University of Zurich Zurich, Switzerland ma maria. teria.terre ereee. wew.reeeeeeeaa..aaaaaaaaaaaiaaaa cii
ABSTRACT
As Text-to-Image (TTI) diffusion models become increasingly influential in con- tent creation, growing attention is being directed toward their societal and cultural implications. While prior research has primarily examined demographic and cul- tural biases, the ability these models to accurately represent historical contexts remains largely underexplored. To address this gap, introduce benchmark for evaluating how TTI models depict historical contexts. The benchmark combines HistVis, dataset of 30,000 synthetic images generated by three state-of of-the-arre art diffusion models from carefully designed prompts covering universal human activi- ties across multiple historical periods, with a reproducible evaluation protocol. We evaluate generated imagery across three key aspects: (1) Implicit Stylistic Associa- tions: examining default visual styles associated with specific eras; (2) Historical Consistency: identifying anachronisms such as modern artifacts in pre-modern contexts; and (3) Demographic Representation: comparing generated racial and gender distributions against LLM-estimated historically plausible demographics. Our findings reveal systematic inaccuracies in historically themed generated im- agery, TTI models frequently stereotype past eras by incorporating unstated stylistic cues, introduce anachronisms, and fail to reflect plausible demographic patterns. By providing reproducible benchmark for historical representation in generated imagery, this work provides an initial step toward building more historically accurate TTI models.
INTRODUCTION
Text-to-image (TTI) models have become powerful tools for generating high-quality images from text, with applications different domains such as art, education, and media (Vartiainen & Tedre 2023 Maharana et al. 2022). However, recent critical analyses of TTI models highlight various issues, most notably the reinforcement of demographic biases, particularly gender and racial stereotypes (Chauhan et al., 2024 Luccioni al. 2024 Zhang et al. 2024b) Bianchi al. (023). In addition, studies increasingly focus on cultural biases, such as Western-cenric defaults and limited global diversity the depiction of objects and traditions (Ventura et 2023 Senthilkumar et al. (024).
While these studies primarily focus on present-day representations, how TTI models depict the past remains largely underexplored area. Historical representation not merely matter of factual accuracy; it also reflects cultural memory, collective identity, and understandings of societal change. Synthetic imagery presents dell-documetted risks, such as the amplification of biases and stereotypes, the overlooking marginalized narratives (Sarhan & Hegelich, 2023), or the spread of misinformation (Dufour et al., 2024). When these issues appear historically themed generated content, they can distort public understanding of the past and undermine the integrity of cultural memory. Despite these risks, current evaluations often overlook how such distortions emerge in visual depictions of history.
Existing approaches to evaluating historical accuracy in foundation models mainly focus identifi- able landmarks (Weyand et al.) 2020), prominent historical figures (Lee et al. 2023). or objective description of archival imagery (Akbulut al. 2025). However, such approaches overlook more fundamental question: how do TTI models construct visual interpretations of the past? The widely publicized case of Google's Gemini (Team al. 2024). which generated racially diverse Nazi

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Evaluation Aspects Historical Representation
Stylittic
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Figure 1: Overview of the benchmark: (1) Prompt Design, (2) Hist Dataset of synthetic images, and (3) Evaluation of stylistic bias, historical consistency, and demographic representation.
soldiers, referred as the "Black Nazi Problem" (Jacobi & Sag 2024), highlighted the challenges of maintaining historical accuracy in Al-generated imagery and sparked debates on bias mitigation. Although AI bias is often discussed in terms of human representation, it also extends the visual characteristics of generated images, such as the tendency of TTI models to associate certain historical periods with specific styles. Offert (2023). for instance, describes the difficulty of generating color images of 1930s fascist parades without outputs defaulting to visual features of early Kodachrome or colorized black-and-white photography. Besides demographic and stylistic biases, TTI models often fail to preserve chronological coherence, resulting in anachronistic depictions of objects, settings, or behaviors, such as smartphones in 18th-century scenes. Evaluating the historical reasoning of TTI models therefore requires a multifaceted approach that considers not only factual accuracy, but also implicit demographic assumptions, stylistic defaults, and consistent chronological representations.
To address these challenges, we introduce HistVis, dataset of 30,000 synthetic images generated from 100 curated prompts covering 20 universal human activities (c.g., art, music) across five centuries (17th-21st) and five decades of the 20th century. The dataset is produced using three open-source diffusion models: Stable Diffusion XL. (SDXL) (Podell et al. 2023). Stable Diffusion (SD3) (Esser al. (0024), and FLUX.1 Schnell (Black Forest Labs 2025 Building on this dataset, we construct a benchmark that pairs HistVis with a reproducible evaluation protocol to assess how TTI models depict historical contexts along three dimensions: (1) Implicit Stylistic Associations, examining how models default to specific visual styles across time periods; (2) Historical Consistency, identifying anachronisms such modern artifacts in pre-modern contexts; and (3) Demographie Representation, assessing whether portrayals of race and gender align with historically plausible patterns. Although our experiments focus on Hist Vis, the evaluation framework is mgnel-agnostic and directly applicable to any other TTI model.
To the best of our knowledge, this is the first systematic evaluation diffusion models in historical representation, offering foundation for assessing representational fidelity and uncovering the underlying biases that shape how the past is portrayed in synthetic imagery.
2 RELATED WORK
TTI models have rapidly evolved from technical innovations into widely adopted tools with broad societal impact. As | argues, these models function as "cultural snapshots", encoding not only the culturally specific associations present in their training data but also the culturally contingent decisions made during model development. Consequently, they should be studied not just as tools but as artifacts that reveal tensions between neutrality, memorization and the politics of representation.
One significant dimension through which these tensions become evident is in demographic bias, par- ticularly along gender and racial lines, which remains among the most extensively studied aspects of cultural encoding in TTI models. Studies consistently show that these biases manifest as stereotypica associations, such as overrepresenting women in caregiving roles and men in technical professions, thereby reinforcing existing social hierarchies (Seshadri et al. [2023] Hall et al.) (2023)). Methodolog- ically, these patterns have been identified through comparisons with real- world benchmarks such
as labor statistics (Luccioni et al. 2024 Bianchi al. 2023 Luo et al. 2024), embedding- -based
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bias metrics (Mandal et al. 2023), and classifier-basee audits using models like BLIP-2 to evaluate responses to neutral occupational prompts (Cho et al., (023). Recent work reveals that these biases not only affect the portrayal of gender but also associated objects and image layouts (Wu et al. 2024).
Beyond demographic biases, TTI models struggle with cultural representations, often reinforcing dominant narratives and missing regional nuance. Benchmarks such as CUBE Senthilkumar et al. (2024), UCOGC ZZhag et al. (2024a). CulturalFrames (Nayak et al.] 2025). and cross-cultural annotation studies (Hall et al. 2024) show that even culturally specific prompts (e.g.. India's gopuram) frequently yield inaccurate depictions. Minor prompt changes, such as replacing a Latin character with Greek or Arabic homoglyph, can shift outputs toward stereotypes (Struppek et al. (003)), while neutral prompts often produce Western-centric imagery with limited regional authenticity (Ventura) et al. 2023 (Hll et al., 2023a). Also, omitting country names can skew outputs toward U.S. -.entrii imagery, highlighting the need for globally aware, context-sensitive TTI models (Basu et al., 2023)
While demographic and cultural biases in TTI models have received considerable attention, historical representation remains a relatively underexplored dimension. In one of the few works to address the concept of history TTI models, [Offert (023) discusses their stylistic biases and their tendency to produce visually reductive portrayals of the past. Related studies on Al-generated "promptography" show that models may produce convincing yet erroncous outputs that mimic historical artifacts, thereby revealing the constructed nature of these depictions (Martin Prada 2025). Beyond image generation, hallucinated narratives in conversational or museum-based Al systems further shape public perceptions, amplifying the risk of historical distortion EWills-Eve 2023). Taken together, these findings indicate that while generative Al is capable of reconstructing historical imagery, it frequently does in a selective, imprecise, and oversimplified manner.
Although some recent work started to address history-related tasks, these efforts tend to focus on recognition and factual alignment. For example, CENTURY (Akbulut al. 2025) investigates how accurately and objectively multimodal models describe sensitive historical photographs, while HEIM (Lee et al., 2023) and the Google Landmarks Dataset v2 (Weyand et al., 2020) evaluate recognition of historical figures and landmarks. However, these existing benchmarks focus on factual correctness and identification rather than probing how models contextualize representations of the past.
Despite growing recognition of the socio-cultural implications of TTI systems, their historical grounding remains largely underexplored. Our work introduces a benchmark for evaluating historical representation in generated imagery, positioning historical depiction as a key dimension of cultural reasoning in generative models.
3 THE HISTVIS DATASET
As part of our benchmark, we introduce HistVis, a dataset of 30,000 synthetic images generated from prompts describing historically situated but universal human activities. Each prompt follows the neutral template "A person [activity] in the [time period]", combining 100 activities A = {ai...., , ]100) with 10 historical periods T (ff......to), Activities are grouped into 20 categories K. (k....., } spanning domains such art, religion, agriculture, work, and daily chores (Appendix D We deliherately select activities that are timeless (e.g., farming, traveling) rather than tied to particular eras, so that cross cross-period variation reflects models' internal historical representations rather than prompt wording. By avoiding explicit references to figures, events, or objects, the design remains broadly applicable across cultures and minimizes the risk of encoding external biases.
Each activity is paired with 10 historical time periods T = (41.... :a:....,4o)) (10). spanning five centuries (17th-21st) and five decades of the 20th century (1910, 1930, 1950. 1970, 1990). For example, the prompt "A person listening to music" can be adapted to "in the 17th century" or "in the 1910s," enabling comparisons of how the same activity is rendered across eras. This selection balances broad historical coverage with finer temporal resolution: centuries capture long-term cultural trends, while 20th-century decades reveal more nuanced societal shifts. Although these choices serve our goals, alternative periodizations could highlight different aspects of historical representation. We use three soute-of-the-e open-source diffusion models, SDXL, SD3, and FLUX.1 Schnell, chosen because they allow comparisons across both distinct architectures and successive versions of the same system. For each model m e (SDXL, FOUX.1 J,. we generate N 10 outputs for every activity-perite pair (a, 1). yielding total of 30,000 synthetic images.
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4 IMPLICIT STYLISTIC ASSOCIATIONS
The first aspect of our benchmark examines whether TTI models default to stylistic cues for historical periods, even when such features not specified in the prompt. Although these implicit stylistic associations do not introduce factual errors, they can reinforce stereotypes and fixed assumptions about how different historical periods are "supposed" look. Leveraging, therefore, the structure of the HistVis dataset where the activity remains fixed and only the time period varies, we introduce a methodology to quantify the strength and consisteney of these associations across models and periods. enabling systematic analysis of phenomenon previously described only qualitatively (Offert 2023).
A person expressing Joy after achiesing goal In the 17th century SDAL
expresiing after actieing goalin the 1830s FLUXA
Engrasnge
Nansthronn Postegrahy
person expressing joy after achiesing goal century
expressing joy a gosl century
Pakntinge
Figure 2: Examples of generated images reflecting different stylistic biases when a specific time period added to the prompt "A person expressing joy after achieving a goal".
4.1 EXPERIMENTAL SETUP
To quantify implicit stylistic associations, we develop style predictor using curated WikiArt corpus (WikiArl 2025) of public-domain and permissively licensed images, organized into five broad, visually distinet style classes: drawing, engraving. illustration,, painting, and photography. Although not exhaustive, these categories were selected because they capture distinct stylistic modes that emerged consistently in our initial qualitative analysis of historically themed generations, providing a structured foundation for identifying patterns of stylistic bias in generated imagery. The dataset consists of 1,280 training and 320 validation images per class (6,400/ / 1,600 total). Since WikiArt does not distinguish between b&w and color photography, we apply a post-processing step using the colorfulness metric of Hasler Suesstrunk (2003), re-labeling photographs with a score < 10 as monochrome.
We benchmark multiple encoders by training a linear probe on each frozen backbone, including CNNs (VGG-16, ResNet-500,, vision transformers (Swin-Base BEiT-Base, MAE ViT-Base, DINOv2 ViT-B/14), and a zero-shot CLIP baseline (ViT-B/32). DINOv2 (Oquah et al., 2023) achieves the strongest performance on the validation set (Acc. = 0.89, F1 0.87), outperforming all others, and therefore adopted for all downstream analyses. Using this predictor, we assign a stylistic label to every MistVis generation, enabling systematic measurement of model-specific tendencies in historical representation. Detailed metrics and confusion matrices for all backbones are reported in Appendix D
4.2 VISUAL STYLE DOMINANCE (VSD) SCORE
We define the Visual Style Dominance (VSD) score as the maximum proportion of images generated by model m for period f that are classified as single style: VSD(m,t) t) max, (s t), where P. (s t) the proportion of images predicted style s. Higher scores indicate strong convergence toward a dominant style; lower scores reflect greater stylistic diversity. To account for classification uncertainty. we estimate 95% confidence intervals (CI) via bootstrap resampling as detailed in Appendix
Table shows VSD scores and dominant styles across models and time periods. the 17th and 18th centuries, SDXL exhibits strong preference for engravings, while SD3 and FLUX.1 favor paintings. In modern periods, FLUX.1 I and SD3 converge on photography, whereas SDXL shifts toward illustrations. All three models display strong monochrome dominance in early 20th-century decades
1950s). Most VSD scores are statistically robust; however, for a few periods (e.g., FLUX.1 in the 1950s), overlapping CI preclude reliable identification of a single dominant style. While this may reflect classification uncertainty, it could also capture genuine cultural transitions, such the shift from monochrome to color media during that period.
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To assess mitigation potential, applied prompt engineering, common strategy for bias reduction in text and image generation (Clemmer et al. 2024 Dwivedi et al. 2023), and re-generated SDXL outputs using prompts that explicitly requested photorealism and discouraged monochrome aesthetics. These interventions occasionally reduced stylistic convergence (e.g.. 17th-18th c.) or shifted the dominant style (e.g., from monochrome painting), but often failed to override stylistic defaults, suggesting that such biases are persistent and not easily mitigated through prompting alone.
Table I: VSD scores by model and historical period. Asterisks (*) denote cases where dominance not statistically significant due overlapping CI. The final columns indicate changes in SDXL after prompt engineering.
FLUX.1
SD3
SDXL
SDXL
Mitigated
Period
Score
Visual Style
Score
Visual Style
Score
Visual Style
Score
Visual Style
17th c.
0.88
painting
0.86
painting
0.93
engraving
0.83
engraving
18th c.
0.84
painting
0.79
painting
0.89
engraving
0.80
engraving
19th c.
0.58
painting
0.64
painting
0.61
engraving
0.51
engraving
20th c.
0.83
photog.
0.66
photog.
0.35*
illustr.
0.82
illustr. +
21st c.
0.84
photog.
0.94
photog.
0.83
illustr.
0.90
illustr. ++
1910s
0.75
monochrome
0.82
monochrome
0.73
monochrome
0.78
painting 1
1930s
0.77
monochrome
0.79
monochrome
0.73
monochrome
0.51
painting 1
1950s
0.50*
monochrome
0.54
monochrome
0.66
monochrome
0.82
illustr. 1
1970s
0.91
photog. 0.85
photog.
0.33*
photog.
0.70
illustr. 1
1990s
0.95
photog. 0.96
photog.
0.50
illustr.
0.56
illustr. +++
Legend: t/J: Score increase/decrease; <.; Unchanged; 5: Style changed.
HISTORICAL CONSISTENCY
The second aspect our benchmark evaluates historical consistency. This involves assessing whether TTI models introduce anachronisms, e.g. modern artifacts in the depiction of pre-modern contexts. Inspired by the open-set bias detection strategies proposed by D I'Incàà al. (2024) and Chinchure al. (2024), which identify biases without predefined categories and focus cultural issues (e.g.. gender stereotypes, brand associations), we apply a similar approach to target anachronisms and propose an automated detection methodology which we validate through human judgment.
person letening to music the 18t centary 50XL
penso using learning tool the 1980s
Whoden tearnwee
Modern
Yoal
clea 19th century
person muniscinnn using
Medem MEPOEEIII
Figure 3: Examples of generated images with anachronisms identified by our two-stage method: headphones in the 18th, vacuum cleaner in the 19th-century, laptop in 1930s and smartphone in 1950s.
5.1 ANACHRONISM DETECTION
Our anachronism detection method applied to the same structured prompts defined in HistVis, where each prompt is combination of human activity and historical time period, denoted as (a, t). The detection pipeline consists two stages: (i) LLM-guided Anachronism Proposal and (ii) VQA-based Anachronism Detection.
Given a prompt (a, t). an LLM is used to generate a set of potential anachronistic elements Zz, { } in the historical context defined by t. For each proposed anachronism 2, it also produces a corresponding binary yes/no question q; designed to identify whether the anachronism appears in the generated image. For example, given the prompt person listening to music in the 18th century."
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the LLM might return the anachronism "audio devices" along with the identification question "Is the person using audio devices, such as headphones or smartphones? Answer with 'yes" or 'no'. For this stage, we evaluated multiple LLMs, including GPT-40, LLaMA-3.2-11B, and Qwen2.5-7B. While GPT-4o generated the broadest and least redundant set of candidate anachronisms, L.LaMA- 3.2 achieved closely comparable coverage, underscoring that our framework is reproducible with open-source alternatives, Full comparisons across LLMs are provided in Appendix
Given the generated image M and the associated set of questions 2o,t ((zi, 9:)}, we query three VLMs, GPT-4o, LLaMA-3.2-1I1 IB, and Qwen2.5 VL-7B, in a VQA setup. Each model answers every question 9, with binary yes/no response, indicating whether the corresponding anachronistic element 2, is visually present in the image. The final decision for each qi is obtained by majority vote across the three models, which reduces model-specific errors and improves agreement with human judgment compared to relying on a single VLM as detailed in Section 5.2
Anachronism Assessment Metrics To systematically evaluate anachronisms in generated images, we first normalize lexical variations in the phrasing of proposed anachronisms introduced by the LLM. For instance, a model may refer to the same object as 'audio device' in one case and 'digital audio device' in another. To avoid double-counting these identical references, we apply fuzzy matching (SeatGeek 1) to treat them the same entry. Importantly, this normalization only addresses surface-level naming inconsistencies and does not involve semantic grouping of distinct concepts.
For each time period E T and each TTI model m E (SDXL, FLUX.1). 1). we compute two metrics for each anachronistic element 2; proposed by the LLM. Frequency is defined as the proportion of generated images (for the given time period and model) in which 2, is detected, and Severity as the consistency with which the model introduces 2j once it is proposed by the LLM. Formally, we define:
detectted
detected
Frequenccyz,,t,1
Severity (z+)
(1)
Nm
proposed
where NT" is the total number of images generated by model m for time period 1, detected is the number of images in which z; is detected, and prnpppoee is the number of prompts in which the LLM proposed 2 as a plausible anachronism. For example, if "modern phone equipment" is detected in 10 out of 1,000 images for the 18th century, its frequency is 1,, and if it is proposed in exactly 10 instances and appears in all 10, then its severity is 1.0.
5.2 ANACHRONISM FREQUENCY & SEVERITY SCORES
Sererity
503
FLUX11
SOXL
Syghing
botwerr
Des drigg
Figure 4: Top 15 anachronistic elements per model, ranked by frequency (x-axis) and severity (y-axis). Circles indicate elements in the top 15 by frequency, triangles by severity, and diamonds by both.
TigureAplot. the top 15 anachronistic elements per model, ranked independently by frequency and severity, aggregated across all centuries. Each data point corresponds to an element proposed by the LLM and confirmed via VQA, with fuzzy matching applied to unify lexical variations. For example, clothing appears in about 2-5% of all anachronistic images, making it the most frequent item overall, yet it shows relatively low severity, indicating a scattered but recurring misrepresentation By contrast, items like audio devices and ironing equipment occur less often but with higher severity: when these

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elements are proposed by the LLM as plausible anachronisms for a given prompt, they are almost always identified in the corresponding generated images. This behavior suggests that models often rely more on the prompt's conceptual cues (e.g.. "listening to music") than on the intended historical context, revealing a structural lapse in historical conditioning. Furthermore, S3 appears most prone anachronisms, with 20% of its 19th-century and 25% of its 1930s images flagged, followed by FLUX.1 I and SDXL, revealing varying model tendencies. Examples of high-severity anachronisms are shown Figure with detailed breakdowns in Appendix J
User Study. To evaluate the alignment between our anachronism detection method and human judgment, conducted anonymous study public crowdsourcing platform with no geographical restrictions. Since SD3 exhibited the highest anachronism rate, we sampled 200 images per decade and century from this model (1,800 in total). Each image was annotated by three participants, producing 5,400 responses, of which 2,040 high-quality annotations from 234 reliable participants were retained after filtering based on predefined control questions. Inter-annotator agreement, measured by Fleiss' k 0.63, indicated substantial agreement. Using the majority vote as the human baseline, our ensemble method reached 75% agreement with human judgments, substantially above chance, and outperformed individual VLMs (72% for GPT-4o, 68% for LLaMA- 3.2, 63% for Qwen2.5). For context, prior work (D Incà et al. 2024) reports 67% agreement on simpler contemporary VQA tasks (e.g., identifying the color of a car), suggesting that our approach performs competitively in a substantially richer historical and semantic setting. Appendix provides additional details on the interface, instructions, and data collection.
6 DEMOGRAPHIC REPRESENTATION
The third dimension of our benchmark evaluates how TTI models portray gender and race in relation to LLM-predicted historically plausible demographics. The goal of this analysis to reveal whether TTI models exhibit extreme patterns of over- and under-represennttion of certain demographic groups with respect to specific activities and time periods. Because our benchmark and dataset target broad period categories and general activitics, a factual historical baseline grounded in real-world data across the full range of cultures, regions, periods and activities cannot be reliably established within the scope of this work. However, still address historical demographic bias at least at coarse level, we extract LLM-based estimates that synthesize contextual knowledge for each activity and period as starting points for analyzing over- and under-representation. These estimates are not substitutes for historical records or expert annotation, but serve as reference points for identifying major divergences, cases where model outputs reflect stereotypical assumptions about the prompt activity itself rather than the specified temporal condition. While this approach provides a practical and scalable baseline, it remains a coarse approximation of historical accuracy and inherits the limitations and biases the underlying language model.
person cooking century -
A person enjoying foed from a street vandor in the 20th century -50
Southhooes Autte ronnctatton
person using leaming tool the 18" FLUX.1
smiling warmly friend the 15a century SDXL
Figure 5: Examples of generated images illustrating demographic overrepresentation across models, time periods, and activities.
6.1 METHODOLOGY
To examine demographic representation in historically conditioned TTI images, we implement three-step pipeline that compares generated demographics to LLM-predicted historically plausible expectations: (1) extracting attributes from generated images with automated face classifier, (2)
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estimating expected distributions with an LLM that synthesizes contextual knowledge for each activ- ity ity-time pair, and (3) quantifying under- and overrepresentation through metric- based comparison.
Demographic Extraction from Generated Images. We use the FairFace classifier (Karkkainen & Dool (021). a ResNet34-baseer model trained on 108,501 facial images with balanced demographic at- tributes, to detect gender (male, female) and race (White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, Latino) in generated images, including those with multiple faces. For each image I(a,)) corresponding to an activity- period pair (a, t), the classifier outputs {(g) r5, gender c. 3-1-
where 9j and ry denote the gender and race of the jth face and gender dgee [0. 1] are confidence scores, Predictions below 0.7 in either attribute are discarded to reduce misclassification. Demo- graphic proportions are computed by normalizing valid predictions across all detected faces in each image and then averaging over the N images for each pair (a, t). yielding the distributions Pmm
gender (a. () and Pince (a,
To assess reliability, we cross-validate predictions using the DeepFace classifier (Serengil & Ozpinar 2020), an ensemble of VGG-Face (Parkhi al. 2013), FaceNet (Schroff' et al. 2015), DeepFace (Taig man et al., 2014), and AreFace (Deng et al. 2019). On 5,000 randomly sampled images (500 per -ediddmmoel pair), we observe strong inter- mnter-moeel agreement: 95.9% for gender (x = 0.93) and 90.8% for race (x = 0.83), where denotes Cohen's 's kappa coefficient for inter-rater reliability.
Estimating Historical Demographic Baselines. To generate estimates of historically plausible expectations, we use LLMM-based predictions conditioned on each activity-perion pair (a, t). For each case, the model is prompted with: "Given the [activity] and the [historical period], estimate the plausible demographic breakdown (in percentages) for gender and race." This yields baseline distributions Palm (a,t) t) and pilm (a, t). Although real-world data is not available for the full scope of our categories and periods, we partially assess the L..M-based estimates by comparing them with the closest available data for three categories. We validated four candidate LLMs, GPT--4o, Claude 3.5 Sonnet, LLaMA-3.. 1.---1111111 IB, Qwen2.5 VL-7B, against Our World in Data (OWID) statistics on gender and continent- level trends, focusing on categories with coverage since the 19th century: Education (Ritchie et al. 2023), Agriculture (Roser) 2023), and Work & Collaboration (Ortiz-Ospine & Roser 2023). Because OWID does not provide mased statisties and to avoid oversimplified region-rage race mappings, this evaluation of LLMs replaces race estimation with continent-level estimation. Our main analyses compare LLM race estimates with predictions derived from the FairFace classifier.
GPT-4o achieved the lowest MAE (4.64), narrowly ahead of LLaMA-332 (4.83), while Claude 3.5 Sonnet (6.93) and Qwen2.5 VL-7B (7.66) performed less consistently. We therefore adopt GPT-4o as primary baseline, while recognizing LLaMA-3.2 3,2 as strong open-source alternative. Appendix
further reports full category-level deviations with both backbones (Tables 16 17b. confirming that results robust across estimators.
Under & Overrepresentation Metric. We propose two metrics, underrepresentattinn and over- representation, to quantify how closely TTI-generated images align with the estimated his- torically plausible demographic expectations. Inspired by fairness metrics in ML that quan- tify deviations from reference distribution (Wang et al.) 2022, Hall et al., 2023b; Bianchi jet al. 2023), our approach adapts these to the historical domain using M.---derveed estimates rather than population statisties. For each activity-period pair (a, and demographic category (male, female, White, Black, Middle Eastern, Indian, Southeast Asian, East Asian), we compute both metrics. Formally, let pmodel denote the proportion of demographic group observed in the generated images, and pilm the corresponding estimate provided by the L.M., We define:
Under, = L( L(P < <illm [ - -pmidde] |
(2)
Over = I. pllm } [model pllm
(3)
These two metrics measure the absolute deviation in cases where the TTI model underrepresents or overrepresents demographic group relative to the LLM--deived historically plausible bascline. We compute these values for each prompt and then aggregate them at the domain category level (e.g.. education, arts) identify systematic demographic disparities across broader cultural contexts.
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6.2 DEMOGRAPHIC ALIGNMENT EVALUATION
Gender Icelor intendlty century)
Gonier Ovenerreanaation by Catagery Across Centuties
Aericuraao
Colebration & Fesmieses
Comminr
Communication Intrrcciion
Cooking Cining
Coiftrmanhh & Labor
Daily Chores Actiitess
Education Learming
Fashion & Personal Care
Health Weil-tiing
Music
Physical & Decreutions Activtiis
Rel leggin & Spintuellt
Survicl Comfort
Unben Life
Work & Collecortioon
Family & Ratationshig
Emakion Nancoral
Transpotattin
Category
Gender overrepresentation across the 17th-1sst centuries.
Weit
Mack
Latina Hoporic
bast Asian
Southesst Aslon
trika
Madle tssion
11
11
Over
century
Agroutture
ARC
Commerce
Communicatio & Social Interaction
Cooking G Dining
Education Leariing
& Personal Tratts
Reaatonships
Prysical Recreational Activiies
Religion Spiitaatty
Surviva Comfort
Personal
Tonsportation
Craftmmarhii
Daily Chores
Category
(b) Racial under- and overrepresentation in 19th and 21 century.
Figure 6: Gender and racial overr/undderrppresenttiion in FLUUX.1 outputs across different centuries and activities, measured as absolute deviations from GPT-4o demographic baselines.
When comparing GPT-4o-derived demographic baselines with face classifier predictions across TTI models, we find frequent divergences from the estimated historically plausible patterns, with variation depending the model. As shown in Figure 6al FLUX.1 tends overrepresent male figures, even in categories like "cooking & dining," where GPT-4o estimates predominantly female presence until the 20th century. This male bias persists across centuries, including "work collaboration." which GPT-4o anticipates becoming gender-balancee by the 21st century. Conversely, "education learning" skews female in the 17th-18th centuries, although GPT-4o indicates male majorities for that period. White individuals are generally overrepresented across models, though this trend diminishes by the 21 century, aligning closer to LLM-estimated historical expectations. However, specific categories (e.g., "religion & spirituality." " "agriculture") show pronounced spikes in non-W non-White representations, as illustrated in Figure 6b for 1. Similar trends are visible across other models and time periods, suggesting that these distributions may reflect correlations in the training data rather than historically grounded depictions. Additional figures with representation scores across all models, time periods, and activity categories provided in Section

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7 DISCUSSION
7.1 LIMITATIONS
While our study offers the first systematic evaluation of historical representation in TTI models, is important to emphasize several key limitations. First, our analysis of stylistic defaults is based on broad categories and therefore does not capture finer stylistic distinctions, which remains a potential direction for future work. Second, although we selected range of different centuries, as well as decades, to capture broad trends and subtler shifts, these choices were somewhat arbitrary; alternative periods could have been chosen and might have yielded different insights. Third, in our benchmark we decided to focus on a broad spectrum of universal activities for this initial investigation, and are aware that this may omit details relevant to specific historical contexts. Fourth, while use of LLMs for demographic estimation and anachronism detection demonstrates promising initial results, we remain mindful of the challenges inherent in historical analysis, the potential biases highlighted in prior research (Gallegos et al. 2024; Navigli et al., 2023), and the fact that LLMs cannot replace the depth or reliability of expert historical knowledge. We are also aware that our baselines are significantly simplified, as they are obtained by averaging demographic expectations across period and activity and therefore omit other potentially relevant factors. Similarly, the use of face classifiers further oversimplifies social complexities by reducing gender and race to discrete categories. Finally, while our anachronism pipeline was validated with user study, judgments may vary due to differences in historical expertíse and subjective interpretation. Moreover, the LLM-gennrated anachronism list is not exhaustive; it targets prominent and recurring inconsistencies rather than every possible historical deviation. Despite these limitations, our approach establishes strong inital basis for systematically evaluating historical aspects of TTI models, and its flexibility allows application to future models for continuous improvement.
7.2 HISTORICAL PLAUSIBILITY AND DIVERSITY
Evaluating demographic patterns in distorically-situatt synthetic imagery raises an inherent ethical dilemma: how to generate content that accurately depicts the past while avoiding that such content further propagates biases and reinforces exclusionary narratives. Recent work on sociopolitical aspects of generated images (Shaw et al. (025) suggests that users are more receptive to diversity when it is transparently and authentically contextualized, indicating that neither strict historical fidelity nor unrestricted diversification is sufficient on its own. Striking a balance between these competing aims remains an open challenge. Within this context, we find it necessary to emphasize that we position our LL.M-derived historical plausibility as one possible evaluation axis, complementary to others such cultural sensitivity, aesthetic quality, and faithfulness the prompt, and not as an expected standard. Our intention is not to prescribe a "correct" way of depicting the past, but to provide a starting point for addressing this challenge at broader, systematic scale.
CONCLUSION
We presented a benchmark for assessing how TTI diffusion models represent historical contexts. Our analysis is focused on three aspects: stylistic bias, chronological inconsistencies and demographic representations. Our results suggest that current models rely on narrow and stereotyped stylistic cues rather than nuanced understandings of historical periods, often defaulting to era-specific stylistic stereotypes and producing photorealistic depictions only from the 20th century onward. In addition, frequent anachronisms suggest that historical periods are not cleanly separated in the latent spaces of these models, since modern artifacts often emerge in pre-modern settings, undermining the reliability of TTI systems in education and cultural heritage contexts. Finally, our demographic analysis indicates that TTI outputs often diverge from LLM-estimated historically plausible expectations, sometimes aligning with contemporary expectations and sometimes reflecting activity-related biases rather than the specified historical condition. Ultimately, balancing historical fidelity with broader representational goals remains an open challenge, and future work should integrate richer historical knowledge and interdisciplinary expertise to develop models that depict the past with greater accuracy and sensitivity.
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9
ACKNOWLEDGMENTS
This research supported by the Swiss National Science Foundation (SNSF) under the Ambizione Grant Scheme, Grant No. 216104.
10 ETHICS STATEMENT
Human Subjects Our study includes human subjects for the purpose of evaluating anachronism detection. All annotations were carried out by adult participants recruited under fair working conditions and compensated at above the local minimum wage. The task was limited to visual evaluation of synthetic images, and did not involve collection personally identifiable information (PII) sensitive data. Participants provided informed consent and were free to withdraw at any time.
Data Sources The image data used training and evaluation are sourced from WikiArt, which contains both public domain artworks and copyright- prottecte- artworks that it hosts under the U.S. "fair use" principle. In line with legal and ethical standards, we filtered the dataset to include only artworks that are explicitly in the public domain released under license permitting research use. No copyrighted or private data were used without permission.
Demographic Classification For demographic analysis, employed face classifier (FairFace) that treats race and gender as categorical attributes. We recognize that such classifications simplify complex, socially constructed identities and may reproduce existing biases. We therefore report demographic results in aggregate, and interpret them cautiously, emphasizing limitations and potential sources of misclassification. Our intention not essentialize reify these categories, but critically assess how generative models encode and reproduce demographic representation.
11 REPRODUCIBILITY STATEMENT
Both the datase including all associated metadata, and our reproducible evaluation scripts are publicly available under ec by-ne 4.0 license.
In Appendix B, we provide the full prompt design used in the HistVis dataset, with all prompts organized by category. This structure is intended to support future applications and benchmarking across different TTI models.
Image generation was performed using an NVIDIA A100 GPU (40GB), with 24 CPU cores, 64GB RAM, and Python 3.12.1.
Some of the models used for evaluation (e.g., GPT-4o) are proprictary and accessed via API, so we cannot guarantee that future queries will yield identical outputs. To support reproducibility, also run our analyses using open-source alternatives and report the strongest substitutes in Appendix H (LLM Backbones for Anachronism Proposal), Appendix O (Validation of Demographic Estimation with Historical Data), and Appendix P (Comparative Results for Demographie Deviations with an Open-Source Alternative).
We provide all human annotation instructions as screenshots of the task interface, exactly as seen by participants, in Appendix K.
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A LARGE LANGUAGE MODEL USAGE
We used large language model (LLM) as a writing assistant for polishing grammar, improving clarity, and rephrasing some of text included in our paper.
B HISTVIS PROMPT DESIGN
The HistVis dataset universal activity prompis (e.g.. "a person working in the 18th century") rather than historically specific events. This choice was guided by the following considerations:
Minimizing prompt bias Prompts are deliberately simple and neutral to avoid encoding historical facts, stylistic cues, or demographic priors. This allows observe how models default to particular representations.
Example: The prompt "a person cooking in the 18th century" leads some models generate male-presenting figures, revealing latent gender bias not specified in the input.
Cross- tross-tempporl comparability
All 100 activity prompts are reused across 10 historical periods, enabling consistent com- parisons, Activities are conceptually universal (e.g.. working, celebrating, communicating). even if their historical realizations differ. This isolates the effect of temporal conditioning from prompt content.
Prompt construction. All prompts follow predefined template ("a person [activiry] in the [historical period]"), and the set of time periods was specified by authors in advance. LLMs were used during the early ideation stage to surface candidate activities, which was then manually curated by the authors.
Limitations. While our prompts support consistency and neutrality, they do not capture historically grounded facts that could reveal additional gaps in models' historical knowledge. Future work could extend this approach with historically specific events assess how models respond more context-rich and accurate historical inputs.
HistVis Prompts.
Below are all the prompts used in the HistVis dataset, organized by category.
Music
A person listening music
2. A person performing music
3. A person playing instrument
4. A person listening to live performance
5. A person singing
Art
6. A person creating art
7. A person using colors to create something
8. A person admiring an artwork
9. person sharing their artwork
10. A person sketching something
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Communication and Social Interaction
11. A person communicating using device
12. person negotiating with someone
13. person sending message
14. A person listening someone speak
15. A person spreading rumor
Family and Relationships
16. A person spending time with their family
17. A person laughing with friend
18. A person holding hands with a loved one
person embracing their child
20. A person welcoming a loved home
Transportation and Travel
21. A person traveling from place to another
22. A person waiting for ride
23. A person carrying belongings during journey
24. A person navigating using a device
25. A person preparing for journey
Urban Life
26. A person carrying shopping bags
27. A person walking through a market
28. A person navigating through a street
29. A person waiting at public gathering spot
30. A person enjoying food from street vendor
Physical and Recreational Activities
31. A person dancing with someone
32. A person running
33. A person playing a game with someone
34. A person discovering a new toy
35. A person exercising
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Cooking and Dining
36. A person sharing meal with another
37. A person cooking dinner
38. A person setting table
39. A person drinking from bottle
40. A person holding bowl of food
Daily Chores and Activities
41. person cleaning with a tool
42. A person washing laundry
43. A person organizing items in bag
44. A person feeding an animal
45. A person ironing clothes
Craftsmanship and Labor
46. person observing building under construction
47. A person repairing an object
48. A person making clothes
49. A person building shelter
50. A person painting surface
Religion and Spirituality
51. A person praying
52. A person celebrating a religious event
53. A person reading sacred text
54. A person lighting candle in place of worship
55. A person meditating quietly
Celebration and Festivities
56. A person unwrapping gift
57. A person dancing during a festival
58. A person toasting with glass at a celebration
59. A person cheering at a celebration
60. A person decorating space for a celebration
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Survival and Comfort
61. A person using a tool to illuminate a dark space
62. A person lighting a
63. A person gathering water from a natural source
64. A person shielding themselves from rain
65. A person shielding their eyes from the sun
Health and Well-being
66. A person taking medicine
67. A person examining patient
68. A person assisting someone who is injured
69. A person treating wound with care
70. A person applying a medical treatment
Education and Learning
71. A person studying
72. A person teaching music another
73. A person guiding someone in understanding
74. A person using learning tool
75. A person reading and taking notes
Agrieulture
person planting seeds the ground
77. A person tending vegetable garden
78. A person caring for livestock
79. A person watering crops
80. A person plowing a field
Fashion and Personal Care
81. A person putting on clothing
82. A person showing off their style
83. A person accessorizing their outfit
84. A person shaving their face
85. A person combing their hair
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Emotion and Personal Traits
86. A person showing bravery
87. A person expressing joy after achieving goal
88. A person smiling warmly a friend
89. A person expressing gratitude another
90. A person standing up for cause
Work and Collaboration
91. A person working
92. A person organizing their work equipment
93. A person reviewing group's s progress on a work-related task
94. A person assisting a coworker with challenge
95. A person preparing for day of work
Commerce
96. A person selling items at a market stall
97. A person bargaining with a vendor
98. A person wrapping goods for a customer
99. A person opening shop
100. A person closing register after sale
C WHY STYLISTIC BIAS CAN BE PROBLEM
While stylistic bias does not introduce factual errors, raises critical issues about cultural stereotypes and visual assumptions embedded in generative models. Prior work (Senthilkumar et al.) 2024) shows that models often default cartoonish depictions for non-Western artifacts, lacking cultural nuance. We observed a similar pattern: when prompted for "photorealistic" images from earlier centuries, models often reverted engraving- or monochrome styles. Even when directly instructed, models frequently failed to override these defaults, indicating that historical periods are often visually "hard- coded" based on past visual documentation conventions. This undermines the flexibility of style as controllable attribute and can reinforce learned stereotypes about what the past is "supposed" to look like.
D STYLE CLASSIFIER: TRAINING & FULL REPORTS
Training Configuration We benchmarked seven encoders by training linear classifier on frozen features extracted from curated WikiArt-derived dataset comprising 6,400 training and 1,600 validation images across five stylistic categories. All images were resized to 224 224 pixels and normalized the [0, range by scaling pixel values by 1/255. To enhance generalization, applied standard data augmentation, including random rotations (up to 20%), translations (10%), shear and zoom transformations (10%), and horizontal flipping. Training was performed using linear probe setup (frozen encoder with trainable linear head), optimized with cross-entropy loss and the Adam optimizer (learning rate 1 10-3), using batch size of 32 for up to 50 epochs. All models trained single NVIDIA A100 GPU.
In parallel, we evaluated CLIP ViT-B/32 in a zero-shot setting. Images were classified based on cosine similarity handcrafted textual prompts representing each style category (e.g., "an image of a painting"). We experimented with several prompt structures and report results for the best- performing variant. Despite its broad representational capacity, CL.PP underperforms fine-grained
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style distinctions (Macro F1 0.66), particularly for less semantically grounded classes such as drawing and illustration. Based on its superior overall and per-class performance (Macro F1 0.88), selected DINOv2 ViT-B/14 the default encoder for all downstream analyses.
Classification Report Table presents the classification performance of each encoder the WikiArt-derived validation set. We report overall accuracy, macro-averaged F1 score, and per-class Fl scores for the six style categories. Among the supervised models, DINOv2 ViT-B/14 achieves the highest macro Fl score (0.876) and ties with Swin-B for best overall accuracy (0.896), demonstrating strong and balanced performance across all classes. In contrast, the zero-shot CLIP baseline shows competitive results for painting and photography but struggles with less semantically grounded styles such drawing and illustration, leading to lower macro F1 score of 0.658. These results validate our choice of DINOv2 as the default encoder for downstream stylistic analyses.
Table 2: Classification performance of different encoders on the WikiArt-derived dataset. Metrics are reported as F1-scores per class, with overall accuracy and macro average F1.
Model
Accuracy
Macro F1
Drawing
Engraving
Illustration
Painting
Photography
CLIP ViT-B/32
0.734
0.658
0.488
0.539
0.436
0.905
0.925
ResNet-50
0.879
0)852
0.783
0.788
0.826
0.965
0.900
VGG-I6-BN
0.858
0.827
0.746
0.743
0.804
0.951
0.892
Swin-B
0.896
0.868
0.825
0.797
0.828
0.974
0.916
BEiT- Basc
0.888
0.857
0.813
0.788
0.796
0,976
0,912
MAE ViT-Base
0.873
0.848
0.780
0.788
0.826
0,951
0.894
DINOv2 ViT-B/14
0.896
0.876
0.802
0.850
0.836
0.957
0.935
E PREDICTED VISUAL STYLES BEFORE AND AFTER MITIGATION ATTEMPT
Predicted Viuue Style bes Over Time for S3XL
Fred cted Vouia Styae Doe Time rLIX]
Predicted Viual Styles Over Time for
Ner1ber of
690
600
200
19t1 centory
1200 cantery
20th cestory
22 cembrry
cestary
Lath cartory
zaee cantory
yoi recliry
cmmtontoryy
saen ceetery
228
Time
phocppppy
Figure 7: Predicted visual styles of generated images across different historical periods for each TTT model using DINOv2. Each historical period is evaluated using 1,000 generated images per model.
Figure presents the predicted stylistic properties of generated images across time periods T for each TTI model using DINOv2. Even though explicit stylistic instructions were provided in the prompts, models exhibit consistent yet model-specific stylistic tendencies. For instance, SDXL predominantly generates engraving- like images for the 17th and 18th centuries (c.g., 947 of 1,000 images for the 17th century, 909 for the 18th), while SD3 and FLUX. favor painting-lik renderings for these same periods. the 20th and 21 centuries, SD3 and FLUX.1 predominantly generate photography- llie images, whereas SDXL exhibits greater stylistic diversity, generating illustrations, drawings, engravings and some photography. More specifically, in the 1910s, 1930s, and 1950s, SD3 and FLUX.1 mostly generate b&w photographs, reinforcing the historical association of these decades with monochrome photography. In contrast, SDXL produces a wider variety of styles, including illustrations and engravings, results indicate that TTI models encode implicit associations between historical periods and specific stylistic renderings, which emerge when style is specified in the prompt, raising important
when generating images for the same time periods. These
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questions about how generative models learn and internalize visual conventions from their training data.
Visual Bias Mitigation Prompt engineering has been explored bias mitigation strategy in both text and image generation, with studies demonstrating its effectiveness in reducing demographic biases in LLMs and guiding TTI models toward more balanced outputs Clemmer al. (2024); Dwivedi et al. 2023). Taking that into consideration, we examined whether the implicit stylistic tendencies of SDXL. which exhibited the most diverse associations across historical periods (e.g., engravings for the 17th and 18th centuries, drawings for the 19th century, and illustrations for the 2 century), could be adjusted through targeted prompt enginecring. Specifically, we replaced the standard neutral prompt (e.g., "A person [activity] in the [time period]"") with image of [activity] in the [historical period]"). Additionally, we introduced negative prompt ("black and white image") to discourage monochrome outputs by applying negative weights, common practice in diffusion-basee TTI models to steer the generation process away from unwanted elements AAranndourr al. (2023). The effectiveness of these modifications was then evaluated using DINOv2 to predict the visual styles of the newly generated images.
explicit instruction ("A photorealistic
Predicted Visal Styles Over Time for SDXL After Blas Mitigtionn
of maapess
600
220
century
1it antorry
20th centory
12309
100
Zist
Fetidd
Figure 8: Predicted visual styles of SDXL SDXL-generated images across different historical periods after applying bias mitigation via prompt engincering. Each historical period is evaluated using 1,000 generated images.
Figure shows Sioows the visual predictions of SDXL images after applying bias mitigation through targeted prompt engineering. Even when explicitly prompting for "photorealistic images," the distribution of stylistic outputs at the century level remains largely unchanged. SDXL continues predominantly generate engraving-like images in the 17th, 18th and 19th century, suggesting that its decp-rooted historical stylistic associations are not easily overridden by prompt modifications alone. At the decade level, however, some shifts are observed. Notably, the proportion of monochrome images decreases following bias mitigation. Yet, rather than transitioning toward color photography as one might expect, SDXL instead increases the generation of paintings and illustrations. These results indicate that while prompt-level interventions can partially influence stylistic biases at more granular level, such reducing the proportion of monochrome outputs. they remain insufficient to fully counteract the model's 's learned stylistic tendencies. Figures 9 and [0] illustrate how certain visual styles either persist or shift following the application of the bias mitigation approach.
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Promat person laughing with friendlnn in Piittoiiaa period] Promes
Premat: A photoreakitic image of penson laughing with frend in Jhstonical period]
Negadve Prompt:
monochrome image
1910s
1910s
1930s
1930s
tok photogmphh
1950s
1950s
1970s
photogmephy
phologadh
p sohry
1990s
Figure 9: Predicted visual styles of generated images derived from the prompt "A person laughing with friend in the [historical period]" (left) and "A photorealistic image of a person laughing with a friend in the [historical period]" (right) with "monochrome picture" used as negative prompt.
Prometi person dancing with another (historical period]
photorealieiic
with Disttoical
bossttie Frome::
17th century
17 century
18th century
18th century
angnaings
engrodings
19th century
19th century
engrashig
20 century
20th century
21 century
21 century
Hustral one.
Figure 10: Predicted visual styles of generated images derived from the prompt "A person dancing with another in the [historical period]" (left) and "A photorealistic image of person dancing with another in the [historical period]" (right) with "monochrome picture" used as a negative prompt.
F VSD: CONFIDENCE INTERVALS & ROBUSTNESS
To assess the robustness of our Visual Style Dominance (VSD) scores, we implemented bootstrap- ping procedure that quantifies uncertainty arising from classifier error. For each model-period pair, generated 5,000 bootstrap replicates by resampling with replacement from the corresponding image set. To account for misclassification noise, each predicted label was retained or reassigned according to the class-specific precision of DINOv2 (e.g., a "drawing" prediction with 0.80 precision was retained 80% of the time). For each bootstrap replicate, we recomputed the style proportions and corresponding VSD score.
This procedure yields empirical 95% confidence intervals (CIs) for the dominant style's share as well as for the second-most frequent style. The Cls therefore indicate how stable style dominance is under
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Table VSD scores with 95% confidence intervals and dominant/second styles for FLUX.1, SD3, and SDXL. Asterisk (*) indicates dominance is not statistically significant (overlapping Cls).
Model
Period
VSD (95% CT)
Dominant Style
Second Style (Prop, 95% CT)
FLUX.1
1950s
0.47 (0.46, 0.51)*
monochrome
photography 0.46 (0.42, 0,49)
FLUX.1
19th c.
0.57 (0.54, 0.60)
painting
photography 0.38 (0.35, 0.41)
SD3
1950s
0.51 (0.47, 0.54)
monochrome painting
photography 0.42 (0.39, 0,45)
SD3
19th c.
0.63 (0.60, 0.66)
color_photograpp 0.31 (0.28, 0.34)
SD3
20th
0.63 (0.60, 0.66)
photography
monochrome 0.24 (0.22, 0.27)
SDXL
1910s
0.69 (0.66, 0.72)
monochrome
illustrations 0.14 (0. 0.16)
SDXL
1930s
0.70 0.73)
monochrome
illustrations 0.16 (0.14, 0.18)
SDXL
1950s
0.63 (0.60, 0.66)
monochrome
illustrations 0.19 (0.17, 0.21)
SDXL
1970s
0.33 (0.31, 0.36)
photography
illustrations 0.30 (0.27, 0.33)
SDXL
1990s
0.44 (0.41, 0.47)
illustrations
photography 0.37 (0.34, 0.40)
SDXL
19th c. 0.54 (0.51, 0.57)
engraving
drawings 0. (0.17, 0.22)
SDXL
20th c. 0.33 (0.32, 0.36)
monochrome
illustrations 0.32 (0.29, 0.35)
Table 4: VSD scores with 95% confidence intervals and dominant/secoond styles for SDXL (Mitigated).
Period
VSD (95% CT)
Dominant Style
Second Style (Prop, 95 % CT)
1930s
0.51 (0.48, 0.54)
painting
illustrations 0.26 (0.23, 0.29)
1950s
0.70 (0.67, 0.72)
illustrations
monochrome 0.09 (0.07, 0.10)
1970s
0.61 (0.57, 0,64)
illustrations
photography 0.23 (0.21, 0.26) photography 0.42 (0.39, 0.45)
1990s
0.49 (0.46, 0.52)
illustrations engraving
19th c.
0.46 (0.43, 0.49)
drawings 0.21 (0.18, 0.23)
classifier uncertainty. In most cases, the intervals are well separated, supporting that the reported dominant style is statistically robust. few cases, however, the intervals overlap, which implies that multiple styles are plausible candidates for dominance.
For example, FLUX.1 (1950s) the intervals for monochrome (0.46-0.51) and color photography (0.42-0.49) substantially overlap, reflecting the historical transition from monochrome color pho- tography in this period. Similarly, SDXL in the 1970s (color photography 0.3 0-31- vs. illustrations 0.27-0.33) and SDXL in the 20th century (monochrome 0.32-0.36 vs. illustrations 0.29-0.35) exhibit overlapping intervals. We mark such cases with an asterisk in the summary tables.
G ADDITIONAL QUALITATIVE EXAMPLES OF STYLISTIC BIAS
The examples illustratd Figure illustrate how TTI models implicitly associate specific stylistic patterns with historical periods. Rather than generating images solely based the historical context in the prompt, they impose learned stylistic conventions, even when no explicit artistic style is specified.
A person showing off their style in the:
17th Century- Paintings (SD3)
18th Century Engravings (SDXL)
1990s Photography (FLUX..1
A person teaching music to another in the:
19th Century Canntry-Paintings (SD3)
21st Century Illustrations (SDXL)
1910s Monochrome Photography (FLUX.1)
A person embracing their child in the:
19th Century Drawings (SDXL)
19th Century- Paintings (SD3)
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20th Century Monochrome Photography (FLUX.1)
person showing off thei 18th SD0.
A Aprson showing off their style the 1010s - SD3
century
A person showing off their thyle style in the 1990s- - FLUX1
A zerson teaching music to another 19th century 5D3
Aperson teaching music to another in the 1510s FLUK11
A person embracing their child the 15" 5DNL
contury
pesson ombradin
parson embracing child century- TLULL1
Figure 11: Examples of visual outputs across styles, activities, and time periods.
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H ANACHRONISM PROPOSAL
A key step in our evaluation pipeline is to generate structured set of potential anachronisms for each activity-pene prompt. Rather than relying on predefined object lists, we frame this as an open-ended proposal task: an LLM is asked to anticipate which historically implausible artifacts might plausibly appear in a generated image. Each proposed artifact is then converted into an identification question that can be applied to the images produced by TTI models.
Anachronism Identification: You will provided with a list prompts describing people engaged in specific activities during various historical time periods. These prompts will serve input for Text-to-Image Generative Model like Stable Diffusion. Based on each prompt, perform the following
tasks:
Identify potential anachronisms that might appear in the generated image. Ensure that the list is relevant to the activity, time period, and setting described in the prompt.
For each identified anachronism, generate question to determine whether the anachronism appears in the generated image. Each question should end with: 'Answer with 'yes' (if the anachronism is present) or 'no' (if it is absent)."
All answers must be in json format.
Example JSON Representation:
Listing 1: Example JSON structure for anachronism detection
"index":
"prompt": "A parson listening mustc in the 1tt century", aonasible_annchhoniiee *modern music devices", *modern clothing",
"modern auzcoundings"
1,
"questiona_toe waloonn__t_ieetnityy.lla iydiidddd_tanaaeeeei anachrrnnnhh
10 "modern muaia devices": smarlphones that didn't exist in the 19th century? Answes with "yest or "no" i" typical for
person using
muate devicas, auch headphores
"modern clothing":
the person wearing
clothing ssyles
century? Answer with 'yes' or 'na'.",
12 "modern suzcoundings": "Are there nodezn surroundings in the image, such as skyscrapeze, items that didn't exist in the 19th century? Answer with 'yes' or 'ne"."
13
]
Choice of LILM Backbone. To generate candidate anachronisms, we compared both proprietary and open-source LLMs of comparable scale: GPT-4o (proprietary), LLaMA-3.2-1 11B Instruct (open- source), Gemma-2-9B-it (open-source), and Claude 3.7 Sonnet (proprietary). As shown in Table performance varies across coverage, redundancy, and runtime. Here, coverage ((Z) denotes the average number of candidate anachronisms proposed per prompt, while redundancy measures the proportion of these proposals that collapse into duplicates under fuzzy string matching (e.g., "cell phone," * "mobile phone," and "smartphone" counted as one). GPT-4o achieves the best overall performance, with the highest coverage (8.9 anachronisms per prompt) and lowest redundancy (12.4%). LLaMA-3.2 performs nearly as well, while Gemma and Claude return fewer candidates with higher duplication, although Claude the fastest overall. Given these results, we adopt GPT-4o as the default proposal backbone in our main pipeline.
I ANACHRONISM DETECTION
Multi- VLM Detection. To avoid over-reliance single vision llnguue model, reran the anachronism detection stage with three VLMs: GPT-4o. LLaMA-3.2-1 IB, and Qwen2.5 VL-7B. For each yes/no verification question, collected predictions from all three models and selected
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Table 5: Comparison of LLMs for anachronism proposal. [ average number of proposed
anachronisms per prompt; % Dup. redundant lexical variants (fuzzy-matcher at 0.8 similarity); TIME average runtime per prompt (s). Best values in bold.
Model
Version
Z
Dup.
TIME
GPT
4o (2024)
8.9
12.4
7.6
LLaMA
3.2-11B Instruct
8.5
13.1
8.2
Gemma
2-9B-it
6.8
14.7
9.3
Claude
3.7 Sonnet
7.2
14.9
6.4
the majority answer. This ensemble strategy increases robustness by reducing idiosyncratic errors of individual models. On our validation set with human annotations, the majority-vote approach achieved 75% agreement with human judgments, compared to 72% for GPT-4o alone, 68% for LLaMA-3.2, and 63% for Qwen2.5. All three models reliably identify clear anachronisms, such smartphones, headphones, modern appliances. Differences mainly occur in ambiguous cases, notably involving clothing styles or background elements lacking clear temporal indicators. Figure [2 illustrates representative examples of disagreement, partial agreement, and full consensus among the three VLMs.
(a) Disagreement. Prompt: per-
(b) Partial agreement. Prompt: person cooking in the 18th cen- tury." All three models detect mod-
(c) Full agreement. Prompt: "A
son exercising in the 18th century." GP and LLaMA-3.2 detect modern clothing, while Qwen2.5 an-
person communicating using de- vice in the 19th century." GPT--4o, LLaMA-3.2, and Qwen2.5 all unan- imously detect the presence of smartphone, matching human judg- ment.
em cooking equipment (e.g.. fry-
swers
ing pan, glassware), hut only GPT- 4o and I.L.MA-322 flag the en- tire kitchen setting as anachronistic, while Qwen2.5 does not.
Figure 12: Representative examples of (a) disagreement, (b) partial agreement, and (c) full agreement among VLMs in the anachronism detection stage. Clear object-level anachronisms (c.g., smartphones) yield consistent consensus, while contextual or stylistic ambiguitics (c.g., clothing, kitchen setting) lead to divergent responses across models.
ANACHRONISM FREQUENCY & SEVERITY RESULTS
Anachronism Frequency and Severity by Decade Figure 13 plots the top 15 anachronistic elements model, ranked by frequency and severity, and aggregated across different decades rather than centuries. Each point corresponds to an element proposed by the LLM and verified via VQA, with fuzzy matching used to unify lexical variations. Items like clothing continue to appear more frequently than they do severely (7-10%), whereas elements such as audio devices are comparatively rare but exhibit higher severity, once generated, they strongly conflict with the historical context. This pattern again illustrates that the models rely heavily on conceptual cues (c.g.. "audio sources" for music-related prompts), often the expense of accurate chronological conditioning.
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S93
FLOXA
SONL
Figure 13: Top 15 anachronistic elements per model, aggregated across all decades, ranked by frequency (x-axis) and severity (y-axis), Circles indicate elements in the top 15 by frequency only, triangles by severity only, and diamonds appear in the top 15 of both.
Overall Anachronism Frequency To assess each model's overall anachronistic tendency across historical periods, we measure the overall anachronism rate, defined as the proportion of images containing at least one detected anachronism. The frequency of specific anachronisms across centuries and decades for each model illustrated in Figure 14 The 21st century excluded as anachronisms cannot be meaningfully defined. By century. SD3's anachronism frequency increases from 15% in the 17th and 18th centuries to over 20% in the 19th century, then decreases to 17% the 20th. FLUX.1 remains stable 12-13%. while SDXL stays below 5%, with slight uptick in the 20th century. By decade, SD3 peaks at 25% in the 1930s before falling to 10% in the 1990s. FLUX.1 follows a similar but milder trend, while SDXXL remains consistently low (3.3-6.4%) These results suggest that while all models generate anachronisms, SD3 does so most frequently, especially in earlier periods. FLUX.1 remains stable, improving over time, while SDXL has the highest historical accuracy.
Model
Model
RUX:1
SOXL
Anachron sm Frequency (%0
933
FLUKX]
SOXL
Anashron sm Freque saa e
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
19505
Century
Decade
(a) Anachronism frequency across centuries. Each bar shows the percentage of images with at least one anachronism per model per century.
(b) Anachronism frequency across decades. Each bar shows the percentage of images with at least one anachronism per model decade.
Figure 14: Anachronism frequency for each model, grouped by centuries (left) and decades (right). Each model was evaluated on 1,000 images per period, totaling 4,000 per century and 5,000 per decade.
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J.1 ANACHRONISM SEVERITY AND FREQUENCY SCORES PER MODEL AND HISTORICAL PERIOD
FLUX.I
SD3
SDXL
Freq. (%)
Sev.
Frey.1%) )
Frey.(%)
Anuchronism
FLUX.I1
SD3
SDXI.
0.350
0.015
0,625
U.026
0.075
U.003
Freq-1%)
Sev.
(req.(%)
Sev.
Freq.(%)
Sev.
0.325
0.65
0.425
0.85
0.100
0.20
Clothing Designs
0.455
0.015
0.725
0.032
0.075
0.003
0.000
0.00
0.175
0.225
0,325
0.65
0.475
1.0
0.075
0.15
0.200
0.267
0.175
0.253
0,000
0.00
Audio Devices
0.0
0.0
0.125
0.5
0.125
0.5
0.025
0.10
0.225
1,0
0.025
0.10
Communication Devices
0.225
0.3
0.1
0,133
0.0
0.0
0.200
0.050
0.050
Troning Equipment
0.175
0.7
0:25
1.0
0.05
0.2
0.150
0.60
0.200
0.80
0.000
0.00
Boule Design
0.25
1.0
0.175
0.7
0.025
0.1
0.100
0.40
0.125
0.50
0,050
0.20
Food Containers
0.15
0.6
0.2
0.8
0.0
0.0
0.100
0.000
0.00
Lighting, Devices
0.2
0.075
0.3
0.05
0.2
0.075
0.30
0.050
0.20
0.100
0.40
Vendor Setup
0.15
0.6
0,225
0.9
0,0
0.0
0.200
0.80
0.075
0.30
0,000
0.00
Art Supplies
0.2
0.4
0.125
0.5
0.025
0.1
0.075
0.10
0.125
0.000
0.00
Sound Equipmentt
0.175
0,7
0,075
0.3
0.0
0.0
0.100
0.10
0.075
0.30
0.025
0.10
Writing Instruments
0.233
0.1
0.0
0.050
0.20
0.175
0.70
0,025
0.10
Gallery Setting
0.175
0.35
0.075
0,333
0.075
0.3
0,100
0.40
0,100
0.000
0.00
Gift Wrapping Materials
0.15
0.6
0.0
0.0
0.025
0.1
0.100
0.40
0.125
0.50
0.000
0.00
Rain Gear
0.35
0.05
0.200
0.80
0.050
0.20
0,000
0.00
Ambient Lighting
0.1
0.4
0.075
0.3
0.0
0.0
0,050
0.10
0.250
0.000
0.00
Postal Devices
0.075
0.3
0,075
0.3
0.0
0.0
0,200
0,80
0,000
0,00
0,000
0.00
Athletic Clothing
0.0
0.000
0.00
0.125
0.50
0,000
0.00
Writing Technology
0.075
0.3
0.075
0.3
0.0
0.0
0.025
0.10
0.125
0.50
0.050
0.20
Pharmaccutical Packaging
0.075
0.15
0,075
0.3
0.0
0.0
0.000
0.100
0.000
Building Materials
0.2
0.025
0.000
0.00
0.025
0.025
0.000
0.00
Learning Devices
0.0
0.0
0.0
0.0
0.0
0.0
0.000
0.00
0.025
0.10
0.000
0.00
Maps or Interfaces
0.0
0.0
0,0
0.0
0.0
0.0
0.000
0.050
0.000
Navigating Devices
0.05
0.025
0.10
0.025
0.10
0.050
0.20
Running Eavironment
0.0
0.0
0.0
0.0
0.0
0.000
0.00
0.075
0.30
0.025
0.10
Street Fixtures
0.075
0.3
0.0
0.0
0.0
0.0
0.025
0.025
0.025
Design
0.05
0.2
0,05
0.2
0.0
0.0
Watcring Equipment
0.075
0.15
0.05
0.2
0.0
0.0
Table 7:
18th Century
Anachronism Scores
Table 6: 17th Century Anachronism Scores
FLUX.I
SD3
SDXL
Annchhonism
FLUXXI
SD3
SDXT,
Freq. (%)
Sev.
Freq.(%) )
Sev.
Freq.(%
Sev,
Freq.(%)
Sev,
Freq.(%)
Sev.
Freq.(%)
Sev.
0.400
0.017
0.725
0.031
0.125
0.005
Clothing
0.850
0.036
2.225
0.094
0.250
0.011
0.225
0.300
0.175
Bag Designs
0,275
0.55
0.400
0.80
0.125
0.00
0.225
0.90
0.225
0.90
0,150
0.60
Audio Devices
0.075
0.200
0.150
0.20
0.100
0.133
0,050
0.067
Communication Devices
0.225
0.30
0.175
0,233
0.000
0.00
0.075
0.30
0.175
0.70
0.050
0.20
Troning Equipment
0.025
0.10
0,200
1.0
0.050
0.20
0.075
0.30
0.100
0.40
0.000
0.00
Bottle Design
0.70
0.025
0.075
0.30
0.100
0.40
0.000
0.00
Food Containes
0.150
0.60
0.100
0.40
0.000
0.00
0.150
0.60
0.200
0
0,000
0.00
Lighting Devices
0.100
0.40
0.125
0.50
0.050
0.20
0.075
0.30
0.100
0.000
0.00
Vendor Setup
0,100
0.40
0.100
0.40
0.000
0.00
0.025
0.10
0,000
0,00
0.000
0.00
Art Supplies
0.075
0.30
0.050
0.20
0.100
0.40
0.025
0.10
0.050
0.222
0.000
0.00
Sound Equipment
0.200
0.80
0.075
0.30
0.000
0,00
0.025
0.033
0.125
0.167
0.000
0.00
Writing Instruments
0.075
0.10
0.125
0.50
0.000
0.00
0.025
0.10
0.050
0,20
0.075
0.30
Gallery Sctting
0.100
0.10
0.075
0.30
0.025
0.10
0.000
0.00
0.000
0,00
0.000
0.00
Gift Wiapping Materials
0.050
0.20
0.175
0.70
0.025
0,10
0.000
0.000
0.000
Rain Gear
0.100
0.40
0.100
0.40
0.000
0.00
0.000
0.00
0.000
0.50
0.000
0.00
Ambient Lighting
0.100
0.40
0.125
0.50
0.000
0.00
0.025
0.10
0.000
0.00
0.025
0.10
Postal Devices
0.200
0.80
0.050
0.20
0.000
0.00
0.025
0.05
0.225
0.000
Athletic Clothing
0.050
0.10
0,250
0.50
0.000
0.00
0.025
0.10
0.000
0.00
0.025
0.10
Writing Technology
0,200
0.80
0.000
0.00
0.000
0.00
0.075
0.30
0.050
0.20
0,000
0.00
Pharmaccutical Packaging
0.000
0.00
0.125
0.50
0.000
0.00
0.000
0.00
0.000
0.000
Building Materials
0.025
0.10
0.125
0.50
0.050
0.20
0.050
0.20
0.225
0.90
0.075
0.30
Learning Devices
0.00
0.40
0.050
0.20
0.250
1.00
0.125
0.50
Maps or Interfaces
0.000
0.00
0.025
0.025
0.000
0.00
0.075
0.30
0.250
0.125
0,50
Navigation Devices
0.000
0.00
0.025
0.10
0.000
0.00
0.075
0.30
0.100
0.40
0.025
0.10
Running Environment
0.00
0.025
0.10
0.025
0.10
0,000
0.00
Street Fixtures
0.025
0.10
0.025
0.10
0.050
0.20
0.075
0.30
0.025
0.000
0.00
Toy Design
0.000
0.00
0.075
0.30
0.025
0.10
0,000
0.00
0.000
0.00
0,000
0.00
Watering Equipment
0.025
0.025
Table 9:
20th Century
Table 8: 19th Century Anachronism Scores
Anachronism Scores
29

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Published as a conference paper at ICLR 2026
K EXAMPLE OUTPUTS OF SEVERE ANACHRONISMS
carrying shopping the 17th
A person cooking cinner in the 17th century-5D3.
A persion ironing the 18th cenbbury 5D3
(a) Modern shopping bags
(b) Modern cooking tool
(c) Modern ironing equipment
parson using communicaiion dovice the 20th century - FLLI.
singing the 10th centiryy- F
A penson Interinn to mude in the 18 oatt -S3
(c) Modern communication de- vice
(d) Modern audio device
(1) Modern audio device
person communictinggggg using device
pesson usig
perar communkatino caing ruvfacsion Device the
(g) Modern communication de- vice
(h) Modern learning tool
(i) Modern navigation device
Figure 15:
Examples of generated images depicting universal human activities across different historical periods, highlighting instances of high-severity anachronisms.
30

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Published as a conference paper at ICLR 2026
L
USER STUDY
presents the user interface for the human evaluation study, which includes the task de- scription given to participants and example questions. The task description introduces the concept of anachronisms to ensure participants understand it before beginning the task and provides illustrative examples of both historically accurate and anachronistic images for further clarification. Participants were then required to assess ITTl-generaeed images for chronological inconsistencies by answering series of yes-no no questions.
Figure T6
Identify Anachronisms Al Generated Images
Task Description: hhss lask, you will evaluata images that have been Al-genenteed to represent: di offerent historical reallies, such centuries (e.g. 17th century, 188 cantury) or Please
decades (e.g 1910s, anachrorisms given the conditioned time pertod.
images Al-generated and are genuine historical photographs.
idendity existonce
What is an anachroniam? An anachronism is somothing that out of place in terma of time-for example, modem object or style appoaring in Historical setting whore doas not balong.
Examples
Example
Example
person ironing
century (inodern iroting board present)
person spending time with their famiy the 18h century (historicaly accurate)
anachroniom in the picture?
Yee
No
Noxt
Deseripiion the
Descripion: Auenos accosoridng o thit in cenkay
Deserip Blet percoe brughing with conkey
Oveatic: anacheoiie the picuas? femoor
Heal
Figure 16: User interface for the human evaluation study, presenting the task description given to participants along with example questions. The interface explains anachronisms, provides illustrative examples of historically accurate and anachronistic images, and includes yes-no questions for assessing chronological inconsistencies in TTI-generated images.
31

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Published as a conference paper at ICLR 2026
M LLM INPUT FOR DEMOGRAPHIC PREDICTION
For a given activity and historical period described the prompt, estimate the plausible demographic breakdown based on global historical context:
Gender: %) Proportion of male and female participants
Race:(% (%) Estimated distribution across the following categories: White, Black, Asian, Indian, East Asian, Southeast Asian, and Middle Eastern.
The estimates should be informed by historical social structures, legal constraints, cultural practices, and prevailing social hierarchies. Avoid focusing on specific geographic region (e.g., Europe); instead, consider patterns and norms that shaped access and participation globally across different societies.
N FAIRFACE-DERIVE DEMOGRAPHIC OUTPUTS
We analyze the face classifier predictions from the three TTI models m é (SDXL, SD3, FLUX.1) to understand the distributions they produce before comparing them to GPT-4o's estimates. Out of 30,000 generated images, 1,221 (4.07%) were excluded due to low classifier confidence, often because the faces were blurred or unclear.
Comparison to DeepFace's Predictions To assess FairFace's consistency, we sampled 5,000 images (500 per time period/model pair) and found that FairFace and DeepFace agreed on gender 95.9% of the time (k 0.93) and on race 90.8% of the time (k 0.83). To align FairFace's seven race categories with DeepFace's single 'Asian' label, aggregated FairFace's 'East Asian' and "Southeast Asian' predictions into one 'Asian' category. All other FairFace labels map directly. After this, we computed the per-class agreement rates and Cohen's k. between the face classifiers reported in Table I0
Table 10: Per-class agreement rates and Cohen's k between FairFace and DeepFace (post-aggregation).
Category
Percent agreement (%)
Cohen's 's
White
93.0
0.85
Black
94.5
0.87
Indian
87.2
0.75
Asian
92.0
0.88
Middle Eastern
88.8
0.80
Latino
89.0
0.81
Male
95.6
0.92
Female
96.2
0.93
Face Classifier Predictions Examining these classifier-derived outputs reveals that demographic patterns vary across models. As shown in the barplots below, SDXL primarily generates male figures in earlier centuries (e.g., 70 women versus 930 men the 17th century), while SD3, in contrast, produces more female figures in certain decades, such the 1950s (700 female versus 300 male). Across all models, white individuals are overwhelmingly depicted in earlier periods, with racial diversity increasing only after the 20th century, indicating potential biases in how different models represent historical demographics.
32

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Published as a conference paper at ICLR 2026
Prodictions TUK Contanis
(a) Gender distribution across centuries (FLUX.1)
(b) Race distribution across centuries (FLUX.1)
Figure 17: Demographic breakdowns (FairFace predictions) across centuries for FLUX.1.
(a) Gender distribution across centuries (SDXL)
(b) Race distribution across centuries (SDXL)
Figure 18: Demographic breakdowns (FairFace predictions) across centuries for SDXL.
(a) Gender distribution across centuries (SD3)
(b) Race distribution across centuries (SD3)
Figure 19: Demographic breakdowns (FairFace predictions) across centuries for SD3.
(a) Gender distribution across decades (FLUX.1)
(b) Race distribution across decades (FLUX.1)
Figure 20: Demographic breakdowns (FairFace predictions) across decades for FLUX.1.
33

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Published as a conference paper at ICLR 2026
Garder Peod clens Aerm Cocades
Gender distribution decades (SD3)
(b) Race distribution across decades (SD3)
Figure 21: Demographic breakdowns (FairFace predictions) across decades for SD3.
Oowker Proditirn Arns
Gender distribution decades (SDXL)
Race distribution across decades (SDXL)
Figure 22: Demographic breakdowns (FairFace predictions) across decades for SDXL.
34

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Published as a conference paper at ICLR 2026
O VALIDATION OF DEMOGRAPHIC ESTIMATION WITH HISTORICAL DATA
To further validate the credibility of the LLMs used for demographic estimation, we conducted an additional evaluation comparing model predictions with historical statisties from Our World in Data (OWID), a public platform compiling long-term global development indicators. Specifically, we examined model estimates for three universal activity categories HistVis, Education, Agriculture, and Work & Collaboration, for which historical demographic data is available. While OWID provides limited coverage for the 17th-18th centuries, representative gender- and continent-level participation statistics are available from the 19th century onward.
Unlike our main pipeline, which prompts an LLM to provide racial demographic distributions for comparison with FairFace classifications, here we deliberately avoided mapping country-level OWID data to racial categories, as such mappings would be reductive. Instead, we used continent-level shares (Africa, North America, South America, Asia, Europe, Oceania), which offer more robust and interpretable basis for comparison. For each prompt, four LLMs were asked to estimate demographic breakdowns by gender and continent; their responses data using Mean Absolute Error (MAE).
aggregated and compared against OWID
For this validation step, each LLM prompted as follows:
For a given activity and historical period, estimate the plausible demographic breakdown based on global historical context:
Gender (%) Proportion of male and female participants.
Continent ( (%): Estimated distribution across the following regions: Africa,
Asia, Europe, North America, South America, Oceania.
Base your estimates on historical population distributions, social structures, and participation norms relevant the activity, and avoid assuming a single geographic focus.
Overall results show that GPT-4o achieved the lowest MAE (4.64), followed closely by LLaMA-3.2 (4.83), then Claude 3.7 (6.93), and Qwen2.5 VL--B (7.66). This indicates that while there variation across models, all produce estimates broadly consistent with historical distributions. These results, not included in the original submission, strengthen the robustness of our demographic estimation pipeline and justify selection of GPT-40 the baseline. While LLM outputs not substitutes for expert historical analysis, they provide plausible proxies for detecting extreme over- or under-representation patterns (e.g., overrepresentation of men in Cooking during early centuries, or delayed inclusion of Asian and Middle Eastern populations in Religion and Agriculture until the 2 century).
Below report aggregated MAE scores across gender and continent-level distributions as well as disaggregated by time period for each model.
Table 11: Aggregated MAE scores across gender and continent-level distributions.
Model
Male
Female
Europe
Africa
N.Am.
S.Am.
Asia
Occania
MAE
LLaMA-3.2
2.49
2.49
8.72
3.54
5.96
4.31
6.09
5.02
4.83
Qwen2.5 VL-7B
5.02
5.02
13.80
5.14
9.71
7.56
6.51
8.54
7.66
Claude 3.7
2.89
2.89
16.54
3.04
3.96
6.34
12.79
6.96
6.93
GPT-4o
1.90
1.90
8.75
3.05
6.47
4.58
4.66
5.80
4.64
35

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Published as a conference paper at ICLR 2026
Table 12: Time-period MAB scores for GPT-4o.
Period
Male
Female
Europe
Africa
N.Am.
Asia
S.Am.
Oceania
1910s
2.85
2.85
3.83
0.64
4.17
8.46
3.20
4.80
1930s
0.92
0.92
4.23
1.09
7.95
3.81
4.90
6.10
1950s
3.74
3.74
5.90
1.50
6,45
2.04
4.80
5.90
1970s
2.86
2.86
9.24
3.16
5.09
0.49
4.30
5.70
1990s
1.01
1.01
12.34
6.58
8.00
1.74
5.10
6.20
19th C.
2.56
2.56
17.13
2.01
3.52
12,50
6.40
7.80
20th c.
1.27
1.27
8.14
3.36
7.10
1.92
4.10
5.30
21st c.
0.00
0.00
9.22
6.09
9.47
6.33
3.80
4.60
Table 13: Time-period MAE scores for LLaMA 3.2-11B.
Period
Male
Female
Europe
Africa
N.Am.
Asia
S.Am.
Occania
1910s
2.65
16.33
8.54
0.67
8.46
2.10
3.20
1930s
4.58
4.58
8.27
4.91
5.45
8.81
3.80
4.60
1950s
2.26
2.26
1.60
2.50
5,45
4.54
4.90
5.80
1970s
1.14
1.14
1.74
3.34
6.09
1.01
5.20
6.10
1990s
1.99
1.99
4.84
0.08
9.00
4.24
4.30
4.70
19th c.
0.06
0.06
27.63
6.31
6.92
14.40
6.80
7.30
20th c.
6.27
6.27
2.64
0.04
6.10
3.42
3.50
4.10
21st c.
1.00
1.00
6.72
2.59
7.97
3.83
3.90
4.40
Table 14: Time-period MAE scores for Qwen2.5 VL-7B.
Period
Male
Female
Europe
Africa
N.Am.
Asia
S.Am.
Occania
1910s
6.35
6.35
3.67
4.54
5.67
2.54
6.20
7.10
1930s
4.42
4.42
11.73
5.91
10.45
7.19
8.10
9.20
1950s
7.74
7.74
13.40
5.50
10.45
8.46
7.80
8.90
1970s
6,86
6.86
12.74
7.34
11.09
8.99
6.90
7.80
1990s
3.01
3.01
15,84
3.92
14.00
5.76
8.30
9.10
19th
10.06
10.06
13.63
8.31
1.92
3.40
9.40
10.60
20th c.
0.73
0.73
17.64
3.04
11.10
9.58
7.00
8.20
21st c.
1.00
1.00
21.72
2.59
12.97
6.17
6.80
7,40
Table 15: Time-period MAE scores for Claude 3.7.
Period
Male
Female
Europe
Africa
N.Am.
Asia
S.Am.
Occania
1910s
5.55
5.55
28.33
5.54
5.73
17.06
4.80
5.90
1930s
0.42
0.42
18.27
3.91
0.45
14.81
6.10
7.40
1950s
1.34
1.34
13.60
3.50
2.45
12.54
6.80
7.20
1970s
1.06
1.06
8.26
1.34
3.09
10.01
6.50
6.80
1990s
0.41
0.41
3.16
1.08
1.00
5.24
6.20
6.90
19th c.
14.06
14.06
46.63
6.31
14.92
25.40
8.70
9.10
20th c.
0.27
0.27
12.36
0.04
1.10
13.42
5.90
6.30
21st
0.00
0.00
1.72
2.59
2.97
3.83
5.70
6.10
36

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Published as a conference paper at ICLR 2026
P COMPARATIVE RESULTS WITH LLAMA 3.2-11B
Tables T6 and 17 report average demographic deviations across activity categories for FLUX.1 Schnell, using GPT-40 and LLaMA 3.2-11B demographic estimators. Deviations are computed by comparing FairFace classifier predictions on generated images against LLM-derived historical baselines. Each entry shows the degree of under- (U) or over-representation (O) for a given group. with the rightmost column (Avg) summarizing the mean absolute deviation across groups. Both estimators reveal consistent imbalances: categories such as art, agriculture, and craftsmanship & labor show strong over-representation of male and W White figures, while domains such as family & relationships or communication social interaction exhibit more moderate deviations. Crucially, the overall trends remain qualitatively similar across GPT-4o and LLaMA-3.2, underscoring LLaMA-3.2 as a viable open-source alternative.
Table 16: Average demographic deviations per category using GPT-4o as demographic estimator. Values show under- and over-representtatio for each demographie group, plus overall average deviation.
Carrgey
U-M
U-P
U-W
U-B
U-4at
U-RAs
U-SEAA
(-Ied
U-ME
0-M
0-F
0-W
0-8
044a
O-EAs
O-SEA+
O-Ind
O-ME
Aag
Agticaluze
0.0
12.6
1.8
0.0
68017
0.0
12:75
Calcinii & Featstiios
12.23
0.8
0.8
13.87
1.86
22.61
8.06
22.26
0.19
00
13:22
08.12
08
0.0
0.0
160
0.0
6.04
0.54
Conmuication Socki Insenction
33
766
38
4.97
1.8
14.0
1.4
1.4
9.46
33
33.15
1.16
010
1.33
0.0
0.0
12.45
6.54
Cuicing String
2.4
Critsmashi
1.6
20:42
1.87
00
0.6
Daiyy Chance Actiritss
14:23
133
0.0
12.8
3-3
10.0
5.5
12:3
1.38
7.35
14.29
80.32
66
6.22
1:33
1.0
0.0
0.81
0.50
Emetion Teneral Inais
5330
10.89
6.8
6/5
2.01
20.1
8.5
22.13
1.89
10.99
5.10
3.13
0.83
0.5
0.0
8.96
Parily Rchatresiipe
0.
00
81
0.0
2.37
Hoolth Well-heing
5481
288
68
1/
44
12:9
4.5
13.4
5.37
300
54383
61.71
1386
110
160
000
5.3
0.52
Phyaral Insmrtiional Autivities
431
0.0
11.5
48
20.53
25.0
0.0
4.11
36
45.2
1.18
11.06
Relaice Spiitealizz
10.19
1.83
6.25
KIC
24
1780
13
11.0
04
1.00
10.19
24.37
238
1.67
0.75
3.38
20.27
7.78
Tranqurnteum Trasel
2111
135
16
14
11:00
15.15
14
35
2111
76 94
06
80
5.0
288
86
6.76
7.7
23
West Coltborniio
1,42
35
16
54
13.15
198
340
16.09
9000
0.0
90
11.04
7,38
Table 17: Average demographic deviations per category using LLaMA 3.2-11B as the demographic estimator. Values show under- and over-representatton for each group, plus overall average deviation.
Caugory
[-M
1--F
E-Wy
T-B
1-1aat
U-EAs
1-SEAs
1-Ind
U-ME
G-M
O-F
0-W
O-6
O-Lai
O-EAs
D-SRAs
O-lnd
O-ME
Arg
Agricature
42.9
0.0
11.9
12.7
9.1
21.5
2.1
0.0
44.
63.3
0,9
0.5
0.0
0.0
2.8
12.6
Celeeation & Pestivilees
18.1
80
12.1
4.3
21.7
0.3
08
610
0.0
0.
0.0
0.0
9.6
Cusmuicaiins & Social Ineraciion
3.3
7.4
211
5.2
13.7
5.3
13.5
1.5
82
3.5
31.9
15
0.9
L.6
0.0
12.8
6.6
Doking & Dinigg
5.6
86
33
21.
1-
183
31
493
9.1
1.3
19
00
9.9
Labre
Daily Chasss & Actriics
14.
7.6
0.0
12.9
3.6
19.3
9.1
21.
L.2
7.5
14.1
63.6
0,0
0.6
3.2
0.0
0.7
9.
Eduoroo Learing Thiis
31.2
80
4A
TAS
088
31.
1.1
0G
148
010
010
8.1
Family & Reteionchips
139
00
11.3
3.6
18.5
8.5
23A
2.6
14.
62.2
00
0
3.9
0.0
11.
9.3
Futhon Persoral
11.4
11.7
21.3
1.0
19.2
110
66.
00
08
344
010
17
108
Misi:
176
7.
80
91
4.6
11.1
9.3
185
4.3
70
176
65.
17
0.
0.0
80
000
60
162
Prysical Recuerorral Activities
25.
80
11.3
306
233
96
58
26
46
00
08
13
00
21.2
110
Surved & Confee
25.
000
80
14
26
116
15
213
13
00
253
11.
53
08
09
80
00
28
9.5
Iraspeciiin Travel
1.3
16
0.0
0.0
Work & Collaberaton
16.7
3.6
2.6
58
ST
119
50
138
20
36
165
26
53
0,0
00
80
00
11.3
744
37

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Published as a conference paper at ICLR 2026
Q GENDER AND RACIAL OYER-UNNDERREPRESEETTTIDOO VALUES WITH GPT-40
Gender (nolor intersity contury)
Gander Overregresentation Category Across Conturies
Overnepresentetle
20
Art
Celebration Festivities
Commerce
Communication & Social Interaction
Cooking Dining
Craftsmanship Labor
Daily Chores Activities
Education & Learning
Erettio Traits
Femily & Rel allonships
Fashion Personal Care
Health Well Being
Music
Physical & Rec Recrealion amal Activities
Religion Spiituality
Survival & Comtort
Transportation & Travel
Unban Life
Catagory
Figure 23: Over- and underrepresentatioo of gender predictions across centuries for SDXL.
Gender (color intersity century)
Gender Divenepresentt by Category Acrows Conturies
Ovenesreeeetaaoo
60 40
20
Art
Celebration & Festvittiss
Commerce
Communication Social Inter octioo
Cooking & Dining
Cruftsmanshpp Labor
Daily Chures Adbvities
Education & Learning
Emotoo Personal Traits
Family & Reletiorships
fachion Personal Care
Health Well Being
Music
Physical & Retres Retrestionna Actvitiee
Raligion Spiritaaitt
Survival & Comfort
Transportation & Travel
Urben Life
&
Category
Figure 24: Over- and underrepresentation of gender predictions across centuries for SD3.
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Published as a conference paper at ICLR 2026
Gender (color intersity century!
Gender Overtepresettation by Category Across Decades
Overregreseertaaooo
Category
Figure 25: Over- and underrepresentation of gender predictions across decades for SD3.
Cender (color intersity century!
2006
Gender Overrepresentaioo by Categoory Aeross Decedes
Overrepreeentatoo
Category
Figure 26: Over- and underrepresentation of gender predictions across decades for SDXL.
intersity
Eivereeteevertaaionn Cateegoty
Overrepresenation
Category
Figure 27: Over- and underrepressentttion of gender predictions across decades for FLUX.1.
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Published as a conference paper at ICLR 2026
Vieters
Figure 28: Racial over- and underrepreesentation across centuries for FLUX.1.
Figure 29; Racial over- and underrepresentaation across centuries for SD3.
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Published as a conference paper at ICLR 2026
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Figure 30: Racial over- and underrepressentation across centuries for SDXL.
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33h
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Figure 31: Racial over- and underrepresentattin across decades for SD3.
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Published as a conference paper at ICLR 2026
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TT
Figure 32: Racial over- and underrepresentation across decades for FLUX.1.
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Reviews:
[{"review_id": "RmDRIKPoKG", "reviewer": "Reviewer_Eaph", "summary": "This paper evaluates the historical knowledge embedded in text-to-image (T2I) models. The authors generate images using multiple T2I models based on predefined actions and time periods, ranging from the 17th century to the late 20th century. The analysis focuses on three key aspects: implicit stylistic associations, anachronism detection, and demographic representation. Using carefully defined metrics for each aspect, the study reveals several noteworthy findings: a) different TTI models exhibit distinct stylistic associations across time periods, and these associations persist even under explicit prompting; b) the models frequently produce anachronistic elements, reflecting a lack of temporal awareness; and c) the generations display gender and racial over- or under-representation, highlighting underlying demographic biases. While the intent behind each such direction is appreciated, i have some issues with the methods used to evaluate these, which i have summarized in the weaknesses.", "questions": "The paper uncovers stylistic biases across time periods, however, these would be expected on some levels due to the data available on those time periods (portraits and illustrations in older days vs black and white in 1900s vs more varieties nowadays). May be the issue can be avoided just by prompting the models to generate only real-life images. What's the authors' take on this?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper explores the world knowledge embedded in text-to-image (TTI) models—knowledge that parallels that of modern Large Language Models (LLMs). By examining how these models represent historical contexts, the authors go beyond the typical focus on creativity or imagination to probe their practical understanding of reality. This represents an important and relatively underexplored research direction.\n2. The findings reveal previously unexamined layers of bias within TTI models. For instance, the recurring depiction of modern artifacts such as headphones in images set in the 17th or 18th century underscores the models’ limited grasp of historical realism and their tendency to fill knowledge gaps with contemporary concepts. Addressing such issues is crucial for improving the reliability and historical awareness of future model releases.\n3. The paper is clearly structured and well written, making complex ideas accessible and easy to follow.", "weaknesses": "1. The paper’s positioning could be clearer. The provided dataset, being composed of the outputs from T2I models applied to a set of prompts, offers limited standalone value to the community—apart from ensuring reproducibility. The true contribution appears to lie in the methodological framework for analyzing the historical biases in generative models. It would therefore strengthen the paper to explicitly present the work as proposing a benchmark for estimating VLM biases in representing historical contexts, with the accompanying dataset serving as an illustrative application of this benchmark to three specific models.\n2. In the anachronism detection evaluation, the reported 72% alignment with human judgment is substantially low, casting some doubt on the robustness of the anachronism detection component. The concern here is less about the existence of anachronisms and more about the quantitative reliability of the reported metrics.\n3. The use of Large Language Models (LLMs) to measure gender and racial representation raises validity concerns. While the paper attempts to verify these measures through domains such as education and agriculture, the scope of the evaluation appears insufficient to justify LLMs as reliable tools for assessing historical demographic biases. A more cautious approach would be to limit this analysis to tasks with concrete supporting data and to avoid the use of LLM-based estimations where empirical grounding is weak.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZvXZzNGmCY", "reviewer": "Reviewer_7quq", "summary": "This paper conducts a study over biases of text-to-image diffusion models while generating images of historical ages. The evaluation criteria is three fold: 1) examining stylistic bias, 2) historical consistency, 3) demographic representation. The authors conducted in-depth evaluation and analysis over each of these aspects. They contributed the HistVis dataset that has 30K synthetic images generated by 3 T2I models -- SDXL, SD3, Flux.1Schnell.", "questions": "Are the HistVis dataset prompts all manually designed or were LLMs involved in aiding ideation or prompt design? It will be interesting to know how the authors designed these prompts.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "The paper is a great read, easy to follow, with interesting findings and extensive evaluations. The authors have designed careful and sound evaluation schemes for each of the three aspects that they are studying in the paper. I especially liked that they have used multiple VLMs for evaluation rather than only using one as a judge. All details of the study has been laid out in complete transparent detail. Multiple qualitative examples were very helpful in getting the point across for each aspect of the study. The authors also discuss and analyze their findings in detail which gives readers valuable insights.", "weaknesses": "A comparison with related studies in this direction comparing the number of samples and evaluation strategies will be helpful to better place the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "z0zFO62IGu", "reviewer": "Reviewer_2DRT", "summary": "The paper presents a framework for evaluating text-to-image models in terms of their ability to accurately represent historical context. The paper presents HistVis, a dataset of 30,000 images generated from three open-source diffusion models that were prompted to depict people performing generic activities across different centuries and decades. They then propose an evaluation protocol that examines stylistic associations, historical consistency, and demographic distribution in the generated images, comparing the results with historical data. The study reveals interesting patterns in how models capture or distort aspects of history, offering insights into biases related to style, anachronism, and representation. Overall, this work highlights the lack of historical accuracy in generative models and provides a concrete methodology and dataset to study it.", "questions": "- Is the stylistic predictor (to predict the style class) also trained to guess images that are not any of the 5 categories in the training dataset? As a mitigation prompt was used, what was its goal?\n- The paper notes that clothing appears in 2–5% of the anachronistic images (line 322). It is unclear why clothing is considered anachronistic. Does this refer to attire that does not match the depicted time period? If so, it would be helpful to clarify how such inconsistencies were detected.\n- Was the prompt used to obtain the racial breakdown for a particular time period specified by continent (as the OWID comparison was based on continent)? If so, which continent were the proportions of the generated images compared against? If not, was it assumed that GPT-4o would estimate global demographic proportions for the corresponding historical period?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The paper highlights an important problem of evaluating historical representation in text-to-image models when depicting generic, everyday activities and provides a clear motivation for addressing it. \n- The paper provides and evaluates an interesting dataset consisting of images that depict a comprehensive set of timeless activities spanning approximately five and a half centuries, offering strong coverage across diverse historical periods.\n- The findings, particularly the observation of anachronistic objects in images depicting historical time periods, are very interesting and effectively highlight an important issue in the temporal consistency of these models.", "weaknesses": "- Although the VSG score is supported by a robust methodology, the reason for evaluating biases in style associations and the explanation of the distinct style classes are not sufficiently motivated. \n- The anachronism detection uses an LLM to get a list of possible objects that could be anachronistic in a given activity. How can we ensure that this list is exhaustive? Were other methods, like object detection, considered for this task? \n- Evaluating demographic representation in the generated stories is an interesting direction. However, the use of LLMs to predict demographic distributions raises concerns about reliability. The paper uses the public OWID dataset to demonstrate the robustness of GPT-4o as an estimator for a small set of activities but does not account for potential data contamination, which could explain the model’s high accuracy in these limited cases.\n- Figures 4 and 6 could be significantly improved to enhance clarity and effectively communicate the key takeaways. In their current form, they present a large amount of information in a single view, which makes it difficult for readers to interpret the results and understand the main insights.\n\nWith the style association results, it might be interesting to link them to the training data and see correlations/reasons for such biases. (Although it might be out of scope for this paper)\n\nMinor typos (not really a weakness, sharing so that authors can fix them):\n- Line 275 refers to Appendix 5 instead of Table 5\n- Line 346 uses the word “currentumptions”.\n- Line 439 says Section Q, instead of Appendix Q", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RJi6HFqozJ", "reviewer": "Reviewer_WUC2", "summary": "This paper introduces a benchmark for evaluating how text-to-image diffusion models represent historical contexts, addressing a gap in current research which has focused primarily on contemporary demographic and cultural biases. The authors created HistVis, a dataset of 30,000 synthetic images generated by three state-of-the-art models (SDXL, SD3, and FLUX.1) using neutral prompts describing universal human activities across five centuries and five decades of the 20th century. They evaluate these images along three dimensions: (1) Implicit Stylistic Associations, finding that models strongly default to specific visual styles for certain eras (e.g., engravings for the 17th-18th centuries, monochrome photography for early 20th century decades) even without explicit stylistic instructions; (2) Historical Consistency, using an automated LLM+VLM pipeline to detect anachronisms; and (3) Demographic Representation, comparing generated gender and racial distributions against LLM-derived historical baselines. The findings demonstrate that T2I models systematically struggle with historically accurate representations, relying on learned stylistic conventions and failing to properly condition on temporal context.", "questions": "- Why not use actual historical sources for demographic baselines? You validate 3 categories against Our World in Data with reasonable results (MAE=4.64 for GPT-4o). Why not extend this approach to the remaining 17 categories by consulting historical demographers, census data, labor statistics, or peer-reviewed historical scholarship? Even if comprehensive data doesn't exist for all activity-period pairs, wouldn't partial coverage with real historical data be more valuable than complete coverage with LLM estimates? What were the practical constraints (time, cost, expertise access) that prevented this?\n- Did you consult any historians or historical demographers during this work? If so, what was their feedback on using LLM-generated baselines? If not, would you consider adding expert validation in a revision, at least for a subset of high-impact categories (e.g., education, work, agriculture) that future users might cite most frequently?\n- How do you recommend future work should use the demographic metric? Given that you acknowledge LLM baselines are \"coarse approximations\" and cannot replace expert knowledge, should future papers cite the demographic over/under-representation scores as evidence of bias? Or should this metric be treated as exploratory/preliminary pending validation with real historical data? Would you consider adding stronger cautionary language in the camera-ready version?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* First systematic evaluation of historical representation in T2I models,  articulating why this matters beyond factual accuracy—historical imagery shapes cultural memory, collective identity, and public understanding of the past, with real consequences as these systems increasingly generate educational and cultural content.\n* Dataset contribution: HistVis dataset with 30,000 images across 3 state-of-the-art models (SDXL, SD3, FLUX.1), using 100 universal, temporally-agnostic activities paired with 10 time periods—this design cleverly isolates models' internal historical representations by avoiding historically-specific prompts that could encode external assumptions.\n- Authors check that prompt engineering fails to mitigate biases. Mitigation experiments demonstrate that explicit instructions (adding \"photorealistic\" to prompts, using negative prompts to discourage monochrome) largely fail to override models' stylistic defaults\n* Authors compare multiple state-of-the-art models and find systematic differences. Comparative analysis reveals model-specific failure modes (SD3 exhibits highest anachronism rates at 20-25%, SDXL most historically accurate, FLUX.1 intermediate)", "weaknesses": "- I think this is a good paper, but I am worried about the fact that demographic baseline uses LLM as \"ground truth\". The third metric relies entirely on GPT-4o to estimate historically plausible demographics, meaning any biases the LLM has will be encoded into the benchmark and treated as \"correct\" historical representation. This is particularly dangerous because: (1) LLMs are trained on internet data that reflects contemporary biases and incomplete historical records, not peer-reviewed historical scholarship; (2) the validation against Our World in Data only covers 3 out of 20 activity categories, leaving 85% of the benchmark unvalidated against any formal historical source; (3) even the validated categories use continent-level distributions while the actual evaluation uses race categories, introducing an additional unsupported mapping; (4) future work citing this benchmark may treat these LLM estimates as authoritative baselines, perpetuating and legitimizing whatever biases GPT-4o encoded. The authors acknowledge this is a \"coarse approximation\" and cannot replace expert historians, yet they publish quantitative over/under-representation scores without consulting actual historical demographers or using primary historical sources. This creates a circular validation problem where one AI system (GPT-4o) judges another (SDXL/SD3/FLUX), with no external ground truth. A benchmark critiquing historical accuracy should itself be grounded in rigorous historical methodology, not LLM outputs. The stylistic and anachronism metrics are well-validated, but the demographic analysis risks doing more harm than good by establishing flawed baselines as reference standards.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)