Comparative value of language-model-based anomaly design

Determine whether designing logical synthetic anomalies with a language model outperforms simpler synthesis procedures, such as cut-and-paste anomaly generation, while keeping the remainder of the synthetic-anomaly classification framework unchanged.

Background

The proposed framework uses a LLM to derive logical anomaly types and image-editing prompts from written category descriptions. The experimental results evaluate the complete framework but do not isolate the contribution of this language-model-based anomaly-design stage.

A controlled comparison against simpler synthesis strategies, including cut-and-paste anomalies, is needed to determine whether the LLM provides a measurable advantage rather than the performance gains arising from other components of the framework. The authors specify that the rest of the framework should remain unchanged in such a comparison.

References

Our results evaluate the framework as a whole and do not isolate the contribution of designing anomalies with a LLM. Whether this design outperforms simpler synthesis, such as cut-and-paste anomalies~\citep{li2021cutpaste}, remains open and would require a controlled comparison with the rest of the framework unchanged.

— Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomalies  (2610.18295 - Efe et al., 13 Oct 2026) in Section 6, “Limitations and Open Questions,” subsection “Role of the anomaly design”