ReflectCAP: Detailed Image Captioning with Reflective Memory

Detailed image captioning demands both factual ground-ing and fine-grained coverage, yet existing methods have struggled toachieve them simultaneously. We address this tension with ReflectiveNote-Guided Captioning (ReflectCAP), where a multi-agent pipeline an-alyzes what the target large vision-language model (LVLM) consistentlyhallucinates and what it systematically overlooks, distilling these pat-terns into reusable guidelines called Structured Reflection Notes. At in-ference time, these notes steer the captioning model along both axes—whatto avoid and what to attend to—yielding detailed captions that jointlyimprove factuality and coverage. Applying this method to 8 LVLMs span-ning the GPT-4.1 family, Qwen series, and InternVL variants, Reflect-CAP reaches the Pareto frontier of the trade-off between factuality andcoverage, and delivers substantial gains on CapArena-Auto, where gen-erated captions are judged head-to-head against strong reference models.Moreover, ReflectCAP offers a more favorable trade-off between captionquality and compute cost than model scaling or existing multi-agentpipelines, which incur 21–36% greater overhead. This makes high-qualitydetailed captioning viable under real-world cost and latency constraints.