Large language models (LLMs) are now used in scientific peer review, but their judgments can still be influenced by how information is presented. We study how the style of a paper’s title affects the way LLMs score scientific work. To control for content variation, we build the TitleTrap benchmark using abstracts generated by a language model for common research topics in computer vision and NLP. Each abstract is paired with three titles: a branded colon style, a plain descriptive style, and an interrogative style, while the abstract text remains fixed. We ask GPT-4o and Claude to review these title–abstract pairs under the same instructions. Our results show that title style alone can change the scores: branded titles often receive higher ratings, while interrogative titles sometimes lead to lower assessments of rigor. These findings reveal a presentation bias in LLM-based peer review and suggest the need for better methods to reduce such bias and support fairer automated evaluation.