Abstract
Promptable segmentation models such as SAM 3 achieve remarkable open-vocabulary performance but require dedicated architectures, large-scale annotation engines, and task-specific training. We ask a different question: can a general-purpose multimodal large language model (MLLM) segment images directly, without an added segmentation head, mask decoder, or training? We present LLMSeg, a training-free pipeline that turns a vision-capable LLM into a text-prompted image segmenter. LLMSeg decomposes segmentation into three LLM-driven stages. First, detect localizes all instances matching a free-form text query and describes their coarse morphology. Next, segment traces each instance using closed periodic B-splines confined to its detected box. These splines define an outer boundary and optional hole loops. Finally, refine iteratively proposes focus boxes around residual errors and retraces confined splines. The process ends when the mask stabilizes or the pass budget is exhausted. The output is a binary mask with inspectable intermediate geometry. We evaluate LLMSeg across ten referring and reasoning segmentation splits (ReasonSeg and RefCOCO/+/g). We also evaluate open-vocabulary instance segmentation (LVIS, COCO, and SA-Co), semantic segmentation, and zero-shot detection (ODinW13 and RF-100VL). With an off-the-shelf Gemini-3.7-Flash backend, LLMSeg is competitive with and often surpasses the specialist SAM 3 model on identical items. It reaches 71.5 cIoU on ReasonSeg val without additional segmentation-specific training. Backend model updates can improve accuracy without retraining the pipeline. The code is available at this https URL .
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URL
https://arxiv.org/abs/2610.29409
