GEAR-Seg: A Grounded Explainable Agent for Reasoning Segmentation and Data Engine

Reasoning segmentation requires localizing targets based oncomplex, implicit queries. Current end-to-end models typically entangleperception and deduction into an opaque black box, severely limitinginterpretability and scalability. To address this, we propose GEAR-Seg(Grounded Explainable Agent for Reasoning Segmentation), an explic-itly decoupled agent that shifts the paradigm by translating visual pixelsinto dense, attribute-rich text. By decoupling class-agnostic segmenta-tion, semantic description, and Large Language Model (LLM) deduc-tion, GEAR-Seg transforms implicit reasoning into an explicit, track-able logic chain. As a zero-shot inference framework, it achieves highlycompetitive performance across diverse reasoning and fine-grained re-ferring segmentation benchmarks. Furthermore, GEAR-Seg inherentlyfunctions as a highly scalable data engine. Utilizing this engine, we con-struct GEAR-131K, a massive benchmark (over 38k images, 656k QA-mask pairs) introducing a multifaceted taxonomy tailored for complexreal-world manipulation-oriented reasoning. Finally, distillation exper-iments demonstrate that lightweight models supervised exclusively byour automated pipeline closely match the upper-bound performance ofcostly human-annotated baselines.