Agentic image generation pairs an image generator with a search agent that retrieves references from the web, extending generation beyond the generator's training data. Real-world requests, however, often concern entities that appeared only days ago. We call this setting Image Generation in the Wild. In this setting, the web offers limited visual evidence, search results are crowded with easily confused entities, and the target is rarely shown on its own. To study this setting, we build an automated pipeline that synthesizes prompts from recent headlines and keeps only those whose real search results are highly confusing, yielding requests that are hard to solve yet natural to ask. With it, we construct Savanna, a benchmark of 500 prompts across 16 subcategories. On Savanna, even harness frameworks built on the frontier models retrieve a correct reference for only about half of the prompts. We then propose Locate-Corroborate-Isolate, a paradigm in which the agent first pins down the target's identity through text search, then verifies candidate images against multiple sources, and finally crops a clean reference for the generator. Trained on 15K trajectories that follow this paradigm, Ranger-8B achieves a search score of 67.2, outperforming harnesses equipped with frontier models. It also achieves the best generation quality with every generator we test, and surpasses prior agentic methods on other image generation benchmarks. We release the pipeline, benchmark, training data and model at https://github.com/RUCAIBox/Agentic-ImageGen-Wild.
Figures & tables
Figure 1: Agentic image generation and its challenges in the wild. Top: the agentic image generation workflow. Bottom: (a) temporal novelty, (b) semantic crowding, and (c) spatial entanglement.
Figure 2: Prompt synthesis pipeline , illustrated with real retrieval results. The text-oriented T-shirt query is too easy ( κ=0.25 ), so image-oriented extraction proposes the face bag ( κ=0.75 ).
Figure 3: Overview of Savanna. The rings show the five domains (inner) and 16 subcategories (outer). Each domain has one example prompt with its reference image.
Figure 4: The Locate–Corroborate–Isolate paradigm. The agent identifies e from text, verifies candidates against sources and each other, then crops a clean reference for the generator.
Method
Generator
Entertainment
Sports
Fashion
Products
Culture
All
Search
Gen.
Search
Gen.
Search
Gen.
Search
Gen.
Search
Gen.
Search
Gen.
Proprietary Generative Systems
–
GPT-Image-2
–
23.4
–
29.5
–
25.3
–
29.9
–
23.7
–
26.4
–
Nano Banana Pro
–
25.9
–
30.5
–
23.4
–
28.5
–
22.4
–
26.1
–
Nano Banana 2
–
33.6
–
37.7
–
31.3
–
39.2
–
32.6
–
34.9
Harness Frameworks
Table 1: Search and generation results on Savanna by domain. Bold/underline mark best/second-best scores. ∗ indicates methods with a stronger model. Qwen-IE-2511 denotes Qwen-Image-Edit-2511.
Method
Generator
Mind-Bench
KnowGen
Knowledge
Reasoning
Overall
Visual cor.
Text acc.
Faith.
Aesth.
K-Score
Proprietary Generative Systems
–
GPT-Image-1
15.6
17.6
17.0
20.18
29.94
78.72
62.78
34.19
–
GPT-Image-1.5
22.0
17.6
21.0
29.34
43.18
85.47
74.19
44.97
–
Nano Banana
16.4
20.0
18.0
16.14
22.72
78.59
68.37
30.24
–
Nano Banana Pro
39.6
44.4
41.0
34.99
51.35
88.65
69.84
50.38
Table 2: Results on Mind-Bench and KnowGen. Bold/underline mark best/second-best scores among open-source methods.
Figure 7
Appendix figures & tables10 assets
Supplementary material from the paper’s appendix.
Appendix
Tool
Implementation
Inputs and returned evidence
News retrieval
Serper /news
Queries a meta query within a single-day date window and returns news titles, snippets, source URLs, and publisher names for data construction.
text_search
Serper /search
Accepts one to three queries and returns ranked organic results with titles, snippets, and URLs; the default is five results per query.
image_search
Serper /images
Searches by a text query, downloads candidate images, and displays up to five images with image IDs, titles, source-page URLs, and image URLs.
web_fetch
Internal tool
Retrieves webpage content from a URL according to a supplied prompt.
crop_image
Local image operation
Takes a retrieved image ID and a bounding box in normalized [0,1000] coordinates, then returns the cropped image with a new ID.
Appendix
Table 3: Tool implementations. News retrieval is used during data construction; the remaining rows describe tools available to the agent.
Domain
Subcategory
Meta queries
Entertainment
Film & TV
movie costume first look; television series costume debut; film set design first look; movie poster artwork unveiling
Anime
anime key visual release; anime character design reveal
Games
video game character costume reveal; video game vehicle model debut; video game expansion content announcement
album cover artwork reveal; concert stage design debut; music video costume design
Sports
General Sports
team kit design unveiling; championship trophy presentation; championship medal ceremony; player signing jersey presentation
Appendix
Table 4: Meta queries for each domain and subcategory of Savanna.
[0pt][0pt] Search judge System message
You are grading a retrieval step inside an image-generation pipeline. An agent was given a prompt, searched the web, and returned a set of REFERENCE IMAGES that will be handed to an image-generation model together with that prompt.
You also get the GROUND-TRUTH image: the picture that provably shows the correct appearance of the target named by the prompt.
ONE QUESTION: if the image model receives the agent’s images instead of the ground-truth image, will it draw the target correctly?
JUDGE FROM THE POINT OF VIEW OF DRAWING, NOT PIXEL SIMILARITY. • PASS is correct when the agent’s images show the SAME thing as the ground truth, from any angle, distance, crop, lighting or background, and clearly enough that its identifying details could be reproduced. A press photo, a product shot, an official render and a leaked picture of the very same design are interchangeable here. • PASS is still correct when the agent’s images add extra content, or show the target as part of a larger scene, as long as the target itself is visible and legible. • FAIL when the target in the agent’s images is a DIFFERENT thing: another edition, year, model, trim, colourway, person or named item. Near-identical siblings from the same series are the most important failures to catch — they look right and steer the generation wrong. • FAIL when the agent returned no usable view of the target: only logos, crowds, venues, packaging, web-page screenshots, unrelated stock imagery, or a view too small, dark or obstructed for its details to be read. • FAIL when the target is absent even though the images are otherwise about the right event.
Extra images that are irrelevant do NOT cause a failure by themselves, as long as at least one image lets the target be drawn correctly. But if the set as a whole would most likely lead the model to draw the wrong version, that is a FAIL.
IMAGE ORDER: image 0 is the GROUND TRUTH. The remaining images, in order, are the agent’s reference images.
Appendix
Table 5: System prompt for the search judge.
[0pt][0pt] Generation judge System message
You are an objective AI image evaluator. For one GENERATED IMAGE you produce two things: a TARGET CONSISTENCY TIER, and true/false verdicts on a list of CONTEXT criteria.
You receive: 1. The generated image. This is the ONLY image being scored. 2. One or more ground-truth photographs of the real target. They show what the target actually looks like. They are never the image being scored. 3. The name of the target and the original prompt. 4. CONTEXT criteria about the requested action, placement, setting, surrounding objects, counts, or spatial relationships.
Part 1: target consistency tier
The question is exactly this: is the target shown in the generated image THE SAME THING as the target shown in the ground-truth photographs?
It is NOT ”do the two images look alike”. The prompt deliberately asks for a different scene from the ground-truth photographs: a different moment, pose, camera angle, background, lighting and framing. That is by design. Those differences NEVER affect the tier.
Judge only identity-bearing features: text and its spelling, logo and emblem shape and where it sits on the target, colour scheme and how the colours are distributed, model or variant silhouette, patterns and textures, materials, distinctive structural parts.
Appendix
Table 6: System prompt for the generation judge.
Condition
Actual retrieval time
Publication-date cutoff
Hit@8 ( % ) ↑
A: Initial release
Within 24 hours of τ
None
68.0
B: Day 3
τ+3
None
76.0
C: Day 7
τ+7
None
80.0
D: Retrospective cutoff
τ+7
τ+3
78.0
Appendix
Table 7: Visual-reference retrieval for 50 prospectively tracked entities. Conditions A–C use searches without date filters at the indicated times; condition D searches on day 7 for results published no later than day 3. Release time τ is verified from each entity’s initial public announcement.
Metric
Judgment
Agreement (%)
Search score
Reference matches ground truth
98.0
Generation score: identity
Target identity matches ground truth
98.0
Generation score: context
Context rubric satisfaction
94.5
Appendix
Table 8: Agreement between human annotators and the VLM evaluator in the 100-image validation study on Savanna. Identity and context are the two components of the generation score.
Figure 8: Pre-generation trajectories for the same target. Excerpts are verbatim; tool names are normalized and verdicts annotated. Green/amber/red mark grounded decisions, uncertainty, and incorrect or missing references. These independent runs omit SCOPE’s later verification and repair. Ground truth is shown for comparison only.
Figure 9: Generation comparisons across all five Savanna domains, using Nano Banana Pro with built-in search or agent-selected references (SCOPE: GPT-5.4). Ground truth is for evaluation only. Labels assess target identity on original outputs; display crops emphasize the target.
Figure 10: Sixteen Nano Banana Pro outputs from Ranger prompts and references, spanning all five Savanna domains. Images are shown uncropped at their original aspect ratios.
Figure 11: Reference-cropping comparisons with Qwen-Image-Edit-2511 and Nano Banana Pro. Each case shows the user prompt, full and cropped references, and paired generations. Green boxes mark the reference crops.
Recent image generation models have shown strong capabilities in generating high-fidelity and photorealistic images. However, they are fundamentally constrained by frozen internal knowledge, thus often failing on real-world scenarios that are knowledge-intensive or require up-to-date information. In this paper, we present Gen-Searcher, as the first attempt to train a search-augmented image generation agent, which performs multi-hop reasoning and search to collect the textual knowledge and reference images needed for grounded generation. To achieve this, we construct a tailored data pipeline and curate two high-quality datasets, Gen-Searcher-SFT-10k and Gen-Searcher-RL-6k, containing diverse search-intensive prompts and corresponding ground-truth synthesis images. We further introduce KnowGen, a comprehensive benchmark that explicitly requires search-grounded external knowledge for image generation and evaluates models from multiple dimensions. Based on these resources, we train Gen-Searcher with SFT followed by agentic reinforcement learning with dual reward feedback, which combines text-based and image-based rewards to provide more stable and informative learning signals for GRPO training. Experiments show that Gen-Searcher brings substantial gains, improving Qwen-Image by around 16 points on KnowGen and 15 points on WISE. We hope this work can serve as an open foundation for search agents in image generation, and we fully open-source our data, models, and code.
While text-to-image (T2I) models have achieved remarkable progress, they struggle with real-world requests that are often underspecified, implicit, or dependent on up-to-date knowledge. We identify this challenge as the Context Gap: the mismatch between the user context and the sufficient generation context for T2I models. To bridge this gap, we propose Qwen-Image-Agent, a unified agentic framework that integrates plan, reason, search, memory and feedback in a context-centric manner. Qwen-Image-Agent treats user input as partial context and progressively constructs the generation context through Context-Aware Planning and Context Grounding. Specifically, Context-Aware Planning identifies missing context and plans how it should be acquired and used, while Context Grounding gathers this context from reason, search, memory, and feedback. To evaluate agentic image generation, we further introduce Image Agent Bench (IA-Bench), a benchmark covering four core image agent capabilities: Plan, Reason, Search, and Memory. Experiments on IA-Bench, Mindbench and WISE-Verified show that Qwen-Image-Agent outperforms strong baselines and achieves state-of-the-art performance.
Text-to-image (T2I) models can produce visually compelling images, yet they remain limited on open-world tasks that require complex semantic understanding, multi-step reasoning, and the integration of external world knowledge. Existing efforts introduce agent capabilities into image generation, but they either prescribe a fixed workflow or place only a subset of the open-world image generation process under agent control. Consequently, reasoning, tool invocation, and image generation are not coordinated by a single policy. We propose ToolArtist, a fully agentic image generation model obtained by post-training a Unified Multimodal Model (UMM). ToolArtist dynamically orchestrates reasoning, external tool use, and native image generation within one unified policy. During Supervised Fine-Tuning (SFT), we equip a teacher agent with search tools alongside an image-generation tool. We then convert the collected trajectories into a UMM compatible format, where the image-generation tool is concealed while the resulting generated images are retained. During Reinforcement Learning (RL), we develop an agentic RL infrastructure for UMMs and introduce Reason-Act-Draw GRPO (RAD-GRPO), which uses complementary intent and quality rewards to jointly optimize the model. Experiments show that placing the entire open-world image-generation process under an agent policy consistently outperforms approaches with fixed pipelines or only partially agent-controlled components. We release the training data and the complete post-training infrastructure.