Tego: Symmetry‑Preserving Local Editing for Targeted Inorganic Materials Design

The inverse design of inorganic crystalline materials is generally regarded as an end-to-end, property-to-structure generation task, giving rise to a series of *ab initio* methods such as diffusion models. However, such methods overlook an inherent contradiction at the information level: properties can be viewed as a lossy compressed representation of structures, and the property-to-structure mapping follows a one-to-many non-functional relation. Reconstructing complete structures merely from properties constraints essentially attempts to recover high-dimensional full structural information from low-dimensional lossy representations, which inherently suffers from the theoretical bound of information asymmetry and constrains the structural stability and property accuracy of outputs from ab initio methods. To address these challenges, we propose the **Tiny-Edit-Good-Outcome (Tego)** framework. Tego initiates inference from an existing stable structure, thereby performing localized inverse editing to approximate target material properties, circumventing the complexity of full structure reconstruction in *ab initio* methods. Evaluations demonstrate that Tego is capable of generating undiscovered, thermodynamically stable crystalline candidates, while achieving higher property achievement rate and computational efficiency across material inverse design scenarios.