Replay Before You Reply: Second-Look Prompting for Unified Multimodal Encoding

Solving multimodal tasks requires Vision-Language Models (VLMs) to form a unified understanding of the image and the question text. However, under causal attention in current VLMs, a token can attend only to what precedes it. Whichever modality comes first is therefore encoded under partial observation of the input, uninformed by the rest of the multimodal context. We introduce Second-Look Prompting (SLP), a training-free method that repeats the image-question pair in the input. In the repeated copy, every visual and textual token can attend to the complete input, yielding a unified multimodal encoding of the pair. Moreover, the added visual tokens contribute computation beyond re-exposing the image–even a blank image in their place can improve reasoning. Across 12 benchmarks, SLP improves the overall average score of every evaluated model, spanning nine open-source models (3B-72B) and three proprietary frontier models, with gains reaching 5.6 points for Gemini 3 Flash on MATH-Vision. Our analysis reveals two complementary mechanisms: the question guides where the second image attends, and the added visual tokens act as a prefill scratchpad whose hidden states retain answer-relevant information from the image and question. These findings highlight input presentation as a simple yet effective way to improve VLM performance.