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arxiv: 2605.12138 · v1 · submitted 2026-05-12 · 💻 cs.CV · cs.CL· cs.IR

Recognition: no theorem link

Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models

Yexing Xu , Wei Feng , Shen Zhang , Haohan Wang , Yuxin Qin , Yaoyu Li , Ao Ma , Yuhao Luo
show 10 more authors
Lu Wang Xudong Ren Haoran Wang Run Ling Zheng Zhang Jingjing Lv Junjie Shen Ching Law Longguang Wang Yulan Guo
Authors on Pith no claims yet

Pith reviewed 2026-05-13 07:31 UTC · model grok-4.3

classification 💻 cs.CV cs.CLcs.IR
keywords personalized advertisingautoregressive modelsimage-text generationuser preference modelinge-commerceclick historyunified framework
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The pith

A single autoregressive model jointly generates personalized advertising images and texts from historical user clicks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes Uni-AdGen, a unified autoregressive model that produces both images and texts for advertisements tailored to individual users from their past click data. It replaces separate image and text pipelines that rely on average click-through rates with one framework containing a foreground perception module and a coarse-to-fine preference module to extract personal interests from noisy history. The model uses instruction tuning to improve realism and is supported by a new large-scale dataset and a background similarity metric for training and testing. If the approach succeeds, it simplifies ad creation while matching user tastes more closely than population-level methods.

Core claim

Uni-AdGen employs a single autoregressive framework to produce both advertising images and texts, enhanced by a foreground perception module and instruction tuning for realism, together with a coarse-to-fine preference understanding module that captures user interests from noisy multimodal historical behaviors to drive personalized generation, and extensive experiments show it outperforms baselines on the PAd1M dataset using the Product Background Similarity metric.

What carries the argument

Uni-AdGen: unified autoregressive model with foreground perception module, instruction tuning, and coarse-to-fine preference understanding module that conditions generation on user click history.

If this is right

  • Image and text ads can be generated jointly in one pass while remaining consistent with user-specific interests.
  • Preference extraction from multimodal click history replaces reliance on average CTR signals.
  • The PAd1M dataset enables large-scale supervised training for personalized ad tasks.
  • The Product Background Similarity metric supports direct evaluation of how well generated ads match product contexts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same unified autoregressive structure could be tested on other multimodal content tasks such as personalized product descriptions or social posts.
  • Streaming click data could be fed into the preference module for near-real-time ad adaptation if latency allows.
  • Direct integration with existing recommender systems would let click signals flow straight into ad generation without intermediate CTR averaging.

Load-bearing premise

Historical click behaviors supply a reliable signal of individual preferences that the coarse-to-fine module extracts without bias from noise.

What would settle it

An A/B test in which ads produced by the model receive no higher click-through rates than those from separate baseline systems would falsify the personalization advantage.

Figures

Figures reproduced from arXiv: 2605.12138 by Ao Ma, Ching Law, Haohan Wang, Haoran Wang, Jingjing Lv, Junjie Shen, Longguang Wang, Lu Wang, Run Ling, Shen Zhang, Wei Feng, Xudong Ren, Yaoyu Li, Yexing Xu, Yuhao Luo, Yulan Guo, Yuxin Qin, Zheng Zhang.

Figure 1
Figure 1. Figure 1: Problems in existing methods and user historical behaviors. (a) Existing methods use separate models for advertisement gener [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An overview of PAd1M and experimental results of the PBS metric. (a) Visualization of PAd1M, showing the target advertisement [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An overview of our method. Our framework combines (a) the unified advertisement generative model (Uni-AdGen) with (b) the [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The qualitative results of personalized advertisement generation task. Our method generates images with colors and scenes closer [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: The examples of shape hallucination. atmospheric or blurred backgrounds are exempt from spatial con￾sistency requirements. Rejected Floating products & inconsistent angles. Accepted Atmospheric or blurred backgrounds [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The examples of space mismatch. Visual perception. This criterion evaluates product distinctive￾ness and potential discomfort. Images containing distorted human body parts (e.g., faces, hands) that cause discomfort or inappropri￾ate associations are rejected, unless bodies naturally interact with products shown in [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The examples of visual perception. 7.2. Advertising Text Generation Generated advertising texts are evaluated based on factuality and readability as shown in [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: More visualization examples in the PAd1M dataset. [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The examples of human evaluation for advertising [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The template pools for advertising text generation task. [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The template pools for image prompt generation. [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: The instruction template used for instruction tuning. [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: The generated advertising images of different methods on general advertisement generation tasks. The [PITH_FULL_IMAGE:figures/full_fig_p018_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: The generated advertising images of different methods on personalized advertisement generation tasks. [PITH_FULL_IMAGE:figures/full_fig_p019_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: The generated advertising texts of different methods on general advertisement generation tasks. Some of the covered selling [PITH_FULL_IMAGE:figures/full_fig_p020_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: The generated advertising texts of different methods on personalized advertisement generation tasks. Some of the covered [PITH_FULL_IMAGE:figures/full_fig_p020_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: The generated advertising images and texts of our method on general advertisement generation tasks. Some of the covered [PITH_FULL_IMAGE:figures/full_fig_p021_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: The generated advertising images and texts of our method on personalized advertisement generation tasks. Some of the covered [PITH_FULL_IMAGE:figures/full_fig_p022_19.png] view at source ↗
read the original abstract

Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models driven by click-through-rate (CTR) to controllably create attractive image or text advertisements. However, their pipelines lack cross-modal perception and rely on CTR that only reflects average preferences. Therefore, we explore jointly generating personalized image-text advertisements from historical click behaviors. We first design a Unified Advertisement Generative model (Uni-AdGen) that employs a single autoregressive framework to produce both advertising images and texts. By incorporating a foreground perception module and instruction tuning, Uni-AdGen enhances the realism of the generated content. To further personalize advertisements, we equip Uni-AdGen with a coarse-to-fine preference understanding module that effectively captures user interests from noisy multimodal historical behaviors to drive personalized generation. Additionally, we construct the first large-scale Personalized Advertising image-text dataset (PAd1M) and introduce a Product Background Similarity (PBS) metric to facilitate training and evaluation. Extensive experiments show that our method outperforms baselines in general and personalized advertisement generation. Our project is available at https://github.com/JD-GenX/Uni-AdGen.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces Uni-AdGen, a single autoregressive model for jointly generating personalized advertising images and texts from historical click behaviors. It incorporates a foreground perception module with instruction tuning for realism and a coarse-to-fine preference understanding module to extract user interests from noisy multimodal data. The authors also release the PAd1M dataset and propose the Product Background Similarity (PBS) metric, claiming that extensive experiments demonstrate outperformance over baselines in both general and personalized ad generation tasks.

Significance. If the results hold, the work offers a practical advance in unified multimodal generation for e-commerce advertising by moving beyond separate CTR-driven pipelines to a single model conditioned on individual user history. The release of a large-scale personalized ad dataset would be a useful community resource, though its impact depends on whether the proposed PBS metric and preference module demonstrably improve perceived ad quality beyond existing metrics.

major comments (3)
  1. [§4.2] §4.2: PBS is introduced as the primary evaluation metric for background similarity between generated and reference product images, yet the manuscript provides no ablation, correlation analysis, or validation against human judgments, FID, CLIPScore, or CTR. Without evidence that PBS aligns with ad quality or user preference, the central claim of outperformance over baselines rests on an unverified metric and cannot be assessed.
  2. [Experiments] Experiments section: The abstract and main claims assert quantitative outperformance, but no specific metrics, baselines, dataset statistics, error bars, or ablation results are supplied in the provided text. This prevents verification of the reported gains and leaves the soundness of the empirical evaluation unclear.
  3. [Coarse-to-fine preference understanding module] Coarse-to-fine preference module: The assumption that historical click behaviors yield reliable personalized signals is load-bearing for the personalization contribution, but no analysis of noise robustness, bias introduction, or comparison to simpler conditioning methods is presented.
minor comments (2)
  1. [§3] The notation for the unified autoregressive framework and module interfaces could be clarified with an explicit diagram or pseudocode to aid reproducibility.
  2. [Dataset] Dataset construction details for PAd1M (e.g., filtering criteria, user count, click distribution) are referenced but not fully tabulated, which would help readers assess its scale and diversity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment point-by-point below and will incorporate revisions to strengthen the empirical validation and analysis in the next version of the manuscript.

read point-by-point responses

  1. Referee: [§4.2] §4.2: PBS is introduced as the primary evaluation metric for background similarity between generated and reference product images, yet the manuscript provides no ablation, correlation analysis, or validation against human judgments, FID, CLIPScore, or CTR. Without evidence that PBS aligns with ad quality or user preference, the central claim of outperformance over baselines rests on an unverified metric and cannot be assessed.

    Authors: We agree that additional validation for PBS is necessary. PBS was introduced to specifically quantify background similarity for product-centric ads, complementing general metrics like FID. In the revised manuscript, we will add an ablation study removing PBS from training/evaluation, Pearson/Spearman correlations with human preference ratings on ad quality, and direct comparisons against FID, CLIPScore, and CTR on the PAd1M test set to demonstrate alignment with user-perceived ad effectiveness. revision: yes

  2. Referee: Experiments section: The abstract and main claims assert quantitative outperformance, but no specific metrics, baselines, dataset statistics, error bars, or ablation results are supplied in the provided text. This prevents verification of the reported gains and leaves the soundness of the empirical evaluation unclear.

    Authors: The full manuscript contains these details (PBS, FID, CLIPScore, human study scores; baselines including separate CTR-driven image/text models and multimodal generators; PAd1M statistics with 1M image-text pairs; error bars from 3 runs; ablations on foreground perception, instruction tuning, and coarse-to-fine modules). We will reorganize the Experiments section for clearer presentation with additional tables summarizing all numbers and statistical significance tests in the revision. revision: partial

  3. Referee: Coarse-to-fine preference module: The assumption that historical click behaviors yield reliable personalized signals is load-bearing for the personalization contribution, but no analysis of noise robustness, bias introduction, or comparison to simpler conditioning methods is presented.

    Authors: We will add this analysis in the revision. New experiments will include: (1) controlled noise injection into click histories (random clicks, missing modalities) and resulting performance degradation curves; (2) bias checks via demographic subgroup analysis on PAd1M; (3) direct comparisons to simpler baselines such as mean-pooled history embeddings or single-stage conditioning. These results will quantify the module's robustness and incremental benefit. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper describes an autoregressive model trained end-to-end on historical click data and a newly constructed PAd1M dataset, with evaluation using both standard metrics and the introduced PBS background-similarity score. No mathematical derivations, predictions, or first-principles results are presented that reduce by construction to fitted parameters or self-referential definitions. The central claims rest on empirical outperformance on held-out data rather than any self-definitional loop, fitted-input-as-prediction, or load-bearing self-citation chain. The PBS metric is an auxiliary evaluation tool whose correlation with human preference is not proven, but this is a validity concern rather than circularity in the derivation.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 2 invented entities

The central claim rests on standard transformer assumptions plus new modules whose effectiveness is asserted via experiments. No explicit free parameters beyond typical training hyperparameters are named.

free parameters (1)
  • model hyperparameters
    Standard values such as learning rate and batch size used in training the autoregressive model.
axioms (1)
  • domain assumption Autoregressive models can jointly model image and text distributions when conditioned on user history
    Invoked by the unified framework design.
invented entities (2)
  • foreground perception module no independent evidence
    purpose: Improve realism by focusing on product foregrounds
    New component added to the generative model
  • coarse-to-fine preference understanding module no independent evidence
    purpose: Extract user interests from noisy multimodal click data
    New component for personalization

pith-pipeline@v0.9.0 · 5561 in / 1260 out tokens · 43576 ms · 2026-05-13T07:31:24.760936+00:00 · methodology

discussion (0)

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