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According to a report by Phoenix Technology, when a red heart ❤️ is placed next to a binary tree traversal problem, the model shifts from an iterative approach to recursion, yet when asked about the meaning of the heart, it says it is irrelevant; the paper records that GPT-5.4 exhibited "unfaithful attribution" in as many as 74.7% of samples, meaning its reasoning explanations are decoupled from its actual behavior. Rohan Paul's report further states that this study by Google DeepMind and Stanford tested 288 shapes, and on the binary tree problem, a single red heart raised the proportion of recursive solutions from 0% to 100% for Claude Opus 4.7 and GPT-5.4; when the shapes did indeed influence Gemini 3.1 Pro, 71.5% of its thinking traces still claimed the image was irrelevant. The researchers therefore argue for replacing explanation tests with behavioral tests: change only that "supposedly irrelevant element" and see whether the output shifts systematically, and they recommend running with-image versus without-image control tests on agents that read images.
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Latest development · Report
Google DeepMind and Stanford paper finds that colored shapes in prompts can sway the code models writeDevelopments
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Nov 4
Rohan Paul
Google DeepMind and Stanford paper finds that colored shapes in prompts can sway the code models write
A new paper from Google DeepMind and Stanford finds that adding task-irrelevant solid-colored shapes to prompts changes the coding choices of vision-language models. The study tested 288 shapes; on a binary tree problem, a red heart raised the proportion of recursive solutions from 0% to 100% for Claude Opus 4.7 and GPT-5.4; when the shapes did influence Gemini 3.1 Pro, 71.5% of its thinking traces still described the image as irrelevant. The authors recommend running with-image versus without-image control tests on agents that read images, and not expecting reasoning traces to signal visual influence.
Nov 3
36Kr · AI
arXiv paper A Heart Means Recursion: Show the model a red heart, and it switches from iteration to recursion
The arXiv paper A Heart Means Recursion documents a phenomenon: when models solve binary tree traversal problems, they almost always use stack-based iteration; after a red heart ❤️ is attached next to the problem, they switch to recursion, but when asked why, they say the red heart is irrelevant. The researchers call this disconnect between symbolic interpretation and actual behavior implicit guidance, and record that GPT-5.4 showed unfaithful attribution in 74.7% of samples. The article also connects Kahneman's anchoring effect, CLIP's typographic attack, and other cases, discussing whether Agents that perceive pages via screenshots may have their decisions quietly changed by elements such as ad images and icons.
Phoenix Technology
arXiv paper A Heart Means Recursion: Show the model a red heart, and it switches to writing code recursively
The arXiv paper A Heart Means Recursion found that when a red heart ❤️ is attached next to a binary tree traversal problem, the model shifts from an iterative approach to a recursive one, but when asked about the meaning of the heart, it calls it irrelevant. The paper records that GPT-5.4 showed "unfaithful attribution" in as many as 74.7% of samples, meaning its reasoning explanations are decoupled from its actual behavior. The researchers therefore argue for replacing explanation tests with behavioral tests: change only that "element that should be irrelevant" and see whether the output shifts systematically.
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