Google DeepMind 与 Stanford 论文：提示图中的红心能让代码模型从 0% 递归变 100%
DeepMind 和 Stanford 发的论文，往编码提示里塞一张红心图，Claude Opus 4.7 和 GPT-5.4 就全改成递归写法了，做图像智能体的得看看。
Google DeepMind 与 Stanford 的论文测试了在编码提示中加入无关彩色形状（如红心）对模型的影响，覆盖 288 种形状。在二叉树问题上，红心使 Claude Opus 4.7 和 GPT-5.4 的递归解法比例从 0% 升到 100%。Gemini 3.1 Pro 被形状影响时，71.5% 的思考轨迹称图像与任务无关。论文提醒开发者：智能体处理图像时应在有无图像两种条件下分别测试，且不能依赖推理轨迹发现视觉影响。
so interesting. 🤔
New Google DeepMind and Stanford paper finds that a red heart picture in the prompt can decide how AI write code, that simple shapes in images can steer AI on unrelated tasks, often without the models saying so.
plain colored shapes in a prompt change how AI models write code, though the shapes have nothing to do with it.
Vision-language models now read screenshots and images as part of normal work. If an unrelated picture changes the model's choice, builders need to see it, ideally in the model's own reasoning.
They added a plain colored shape, like a red heart, to a coding prompt and told the model to look at it. Across 288 shapes, they tracked how often Claude Opus 4.7, Gemini 3.1 Pro, and GPT-5.4 chose recursion.
A red heart moved Claude Opus 4.7 and GPT-5.4 from 0% recursive solutions to 100% on a binary tree problem. When a shape did steer Gemini 3.1 Pro, 71.5% of its thinking traces called the image irrelevant.
If your agent sees images, test its decisions with and without them, and do not count on reasoning traces to flag visual influence.
