Kiroshi: An Agentic Perception System for High-Accuracy Image Parsing

Parsing images with high precision for matting and segmentation is sig-nificantly more challenging than conventional dense prediction, as it requires accurateestimation of fine-grained details. Existing methods either lack semantic awareness orproduce suboptimal predictions; even interactive matting approaches rely on manualverification and repetitive checking, making fully automatic matting still unattainable.In this work, we propose Kiroshi , an agentic perception system for high-accuracyimage parsing. We train an Action Model with iterative refinement and mine pairedtrajectories by sampling grid prompts from residual maps, where each step yields apositive and a negative transition under the same intermediate prediction based onquantitative quality gains. These within-context preference pairs form a reliable su-pervision signal for post-training to align the MLLM policy toward more effectivegrid decisions. We also contribute a new High-Fidelity Referring Matting and Seg-mentation (HiFiRefMS ) benchmark to evaluate the performance of different mod-els. Experimental results demonstrate that our method surpasses state-of-the-art ap-proaches both quantitatively and qualitatively, and extensive ablation studies furthervalidate the effectiveness and superiority of our agentic design. Project will be releasedat https://github.com/haipengzhou856/Kiroshi.