探索AI服从性：为何生成纯色图像比CyberPunk更困难
Exploring the AI Obedience: Why is Generating a Pure Color Image Harder than CyberPunk?
AI总结：
研究探讨生成纯色图像的困难源于模型在复杂场景生成中的系统性失败，提出AI服从性框架和Violin基准测试，揭示闭源模型在确定性精度上的优势。
摘要：
近期生成式AI的进展在复杂内容创作中展现出人类水平的表现。然而，我们识别出一个
英文摘要：
Recent advances in generative AI have shown human-level performance in complex content creation. However, we identify a "Paradox of Simplicity": models that can render complex scenes often fail at trivial, low-entropy tasks, such as generating a uniform pure color image. We argue this is a systemic failure related to uncontrollable emergent abilities. As models scale, strong priors for aesthetics and complexity override deterministic simplicity, creating an "aesthetic bias" that hinders the model's transition from data simulation to true intellectual abstraction. To better investigate this problem, we formalize the concept of AI Obedience, a hierarchical framework that grades a model's ability to transition from probabilistic approximation to pixel-level determinism (Levels 1 to 5). We introduce Violin, the first systematic benchmark designed to evaluate Level 4 Obedience through three deterministic tasks: color purity, image masking, and geometric shape generation. Using Violin, we evaluate several state-of-the-art models and reveal that closed-source models generally outperform open-source ones in deterministic precision. Interestingly, performance on our benchmark correlates with the benchmark in natural image generation. Our work provides a foundational framework and tools for achieving better alignment between human instructions and model outputs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
