Edit Spillover as a Probe: Do Image Editing Models Implicitly Understand World Relations?

2026-03-18

AI Summary

This work investigates “edit spillover”—the unintended modification of semantically related but untargeted regions during local image editing—as a potential indicator of a model’s implicit understanding of real-world relationships. Treating spillover as a lens into latent world knowledge, the study introduces the first taxonomy categorizing spillover into spatial, semantic, hybrid, and random types, along with an automated detection pipeline. A new Chinese-language benchmark dataset of real-world editing scenarios is constructed to facilitate systematic evaluation. By integrating spatial decay patterns and semantic relevance analysis, the authors distinguish genuine semantic comprehension from mere attention leakage. Evaluations across five state-of-the-art models reveal that semantic spillover accounts for 40%–58% of cases and positively correlates with world understanding, while spillover density decays exponentially with distance—evidence that spillover is non-random and reflects an inherent trade-off between editing precision and embedded world knowledge.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Instruction-following image editing models are expected to modify only the specified region while keeping the rest of the image unchanged. However, in practice, we observe a pervasive phenomenon -- edit spillover: models alter semantically related but unspecified content outside the edit region. This raises a fundamental question -- does spillover reflect genuine implicit world understanding, or is it merely attention leakage? We propose EditSpilloverProbe, a systematic framework that repurposes edit spillover as a natural probe for world knowledge in image editing models. We introduce a spillover taxonomy (spatial, semantic, mixed, random), an automated detection-and-classification pipeline, and a benchmark dataset constructed from real-world Chinese text editing tasks, EditSpilloverBench. Systematic evaluation of 5 representative editing models reveals three core findings: (1) spillover rates vary dramatically across architectures, from 3.49% to 11.46%, with a 3.3x ratio; (2) absolute semantic spillover quantity reveals models' world understanding capability -- nano_banana produces the most semantic spillover (27.8 per image), while qwen_2511 has the most precise editing control but lower semantic spillover (16.3 per image), revealing a trade-off between editing control and world understanding; (3) spatial decay analysis shows spillover area density decays exponentially with distance, but the proportion of semantically relevant spillover remains constant (40%-58%), providing direct evidence that semantic spillover reflects genuine world understanding rather than spatial diffusion.

Problem

• edit spillover
• image editing models
• world understanding
• semantic spillover
• instruction-following

Innovation

• edit spillover
• world understanding
• image editing models
• semantic consistency
• EditSpilloverProbe