Industrial anomaly detection (AD) is bottlenecked by the scarcity of anomalous samples, making high-fidelity anomaly synthesis crucial for improving downstream performance. However, existing methods either rely on scarce real anomaly references or auxiliary data, while lacking complex reasoning and iterative optimization capabilities for generating realistic defects in open-set scenarios. To address these challenges, we propose CORE, a cost-efficient and training-free prompt optimization framework based on Large Multimodal Models (LMMs), which unifies zero-shot and one-shot anomaly synthesis. CORE first introduces an Anomaly Context Grounding module to transform sparse user context into defect-aware generation context, which is subsequently instantiated as structured anomaly prompts that disentangle normal-region preservation from anomaly-specific variation, enabling controllable and faithful defect generation. Furthermore, CORE autonomously refines prompts through a self-supervised contrast-and-reflect mechanism. This design empowers general-purpose LMMs to synthesize realistic anomalies under both zero-shot and one-shot settings without task-specific training. Extensive experiments on MVTec-AD and VisA demonstrate that CORE synthesizes realistic and diverse anomalies, yielding superior improvements in downstream AD performance. Furthermore, its model-agnostic and plug-and-play paradigm enables effortless integration with diverse LMMs, facilitating adaptation to unseen open-set scenarios.
Figures & tables
Figure 1: Comparison of representative anomaly synthesis paradigms, where existing (i) zero-shot and (ii) one-shot methods are limited by their reliance on auxiliary datasets and scarce anomaly references, respectively, while (iii) CORE unifies both settings through self-supervised closed-loop optimization for realistic and generalizable anomaly synthesis.
Figure 2: Overview of the CORE framework. CORE unifies zero-shot and one-shot anomaly synthesis within a self-supervised Execute-Evaluate-Optimize loop. Starting from user-provided inputs, CORE derives anomaly generation context and iteratively evaluates and refines the generation process, enabling realistic and diverse anomaly generation.
Figure 3: A running example of feedback-driven prompt optimization in CORE. Starting from an initial structured prompt, CORE performs generation, evaluation, and prompt optimization to progressively improve the synthesized image–mask pairs.
MVTec-AD
VisA
Method
I-AUC
I-AP
I-F1
P-AUC
P-AP
P-F1
PRO
I-AUC
I-AP
I-F1
P-AUC
P-AP
P-F1
PRO
One-Shot Anomaly Generation
TF-IDG ( Xu et al. 2025 )
89.9
95.6
93.0
88.5
48.6
50.2
76.2
90.9
93.1
86.9
93.7
44.3
47.4
80.6
O2MAG ( Rao et al. 2026 )
95.9
98.3
94.9
89.4
51.2
51.7
83.2
91.7
94.1
88.1
94.8
47.5
50.7
83.8
CORE (Ours)
96.5
98.7
96.3
96.5
66.7
64.6
87.2
92.5
94.5
88.7
97.9
53.0
55.1
84.9
Zero-Shot Anomaly Generation
Table 1: Comparison of one-shot and zero-shot anomaly synthesis methods on MVTec-AD and VisA. All results are reported as percentages, with the best and second-best results within each setting highlighted in bold and underlined , respectively.
MVTec-AD
VisA
Method
IC-L ↑
IS ↑
IC-L ↑
IS ↑
Zero-Shot Anomaly Generation
AnomalyAny ( Sun et al. 2025 )
0.29
2.04
0.10
1.66
Anomagic ( Jiang et al. 2026 )
0.28
2.09
0.34
1.83
AnoStyler ( So and Kang 2026 )
0.27
1.59
0.31
1.37
CORE (Ours)
0.28
2.13
0.34
1.54
Table 2: Comparison of anomaly generation on MVTec-AD and VisA under zero-shot and one-shot anomaly synthesis settings. Bold and underline represent optimal and sub-optimal results. On VisA, some zero-shot methods introduce larger changes at implausible locations, which may artificially inflate their IS.
Figure 4: Qualitative comparison of anomaly synthesis results. (a) compares CORE with representative zero-shot and one-shot anomaly synthesis methods on MVTec-AD and VisA, showing that CORE produces more realistic and localized anomalies while better preserving non-anomalous regions; (b) further illustrates its generalization to real-world scenes.
Setting
Variant
MVTec-AD
VisA
I-AUC
I-AP
I-F1
P-AUC
P-AP
P-F1
PRO
I-AUC
I-AP
I-F1
P-AUC
P-AP
P-F1
PRO
One-shot
w/o ACG
93.2
97.2
93.8
94.7
59.7
58.6
85.1
88.6
91.0
85.3
96.2
43.1
45.7
78.4
Initial (ACG)
94.2
97.9
94.6
95.3
63.5
61.6
85.2
91.3
93.2
87.3
96.0
45.1
49.3
83.0
Google
96.5
98.7
96.3
96.5
66.7
64.6
87.2
92.5
94.5
88.7
97.9
53.0
55.1
84.9
OpenAI
95.3
98.3
95.3
97.2
67.4
64.4
88.3
91.8
94.1
88.3
97.5
50.0
53.6
86.5
Zero-shot
w/o ACG
93.1
97.3
94.2
95.2
57.6
57.3
83.7
88.6
90.6
84.9
95.3
35.0
40.2
80.5
Table 3: Ablation study of ACG and prompt optimization with different image generators. Initial (ACG) is shared by the Google and OpenAI optimization backends, while w/o ACG removes anomaly context grounding.