Revisiting Caption Gains When the Image Remains Available

In a multimodal conversation, an assistant generates a generic caption to summarize an image and retain it for later visual questions, even when the original image remains available at answer time. Whether such captions help beyond simply placing text in the conversation remains unclear. By generating one question-blind generic caption per model–image pair for seven vision-language models and testing 20 conditions on natural-image and medical-image question-answering datasets, we measure how much of the gain from caption history over a minimal exchange with a fixed, image-independent acknowledgement comes from the image description. To isolate the description's contribution, we compare each frozen caption with length-matched neutral text in the same assistant-history format. Neutral text alone accounts for much of this gain on the natural-image dataset, where replacing it with the caption adds only a small further gain and placing the same text alongside the question yields a caption–neutral difference of similar size. On the medical-image dataset, the caption adds no clear gain over neutral text in history, although on that dataset the average gain of neutral history over the minimal exchange and the average inline caption–neutral difference are each driven by one or two models. We further examine image placement and longer conversations to probe how these comparisons depend on the input sequence. With the original image available, a caption's description adds at most a small average gain beyond neutral text on either dataset. An image-removal replay shows that the captions carry answer-relevant content: when the image is removed, captions preserve most of the accuracy that length-matched neutral text loses on both datasets. A caption's value for later questions must therefore be understood in relation to continued access to the image.