3D sequential affordance reasoning requires a model to locate a logical series of functional parts based on language instructions. Current Multimodal Large Language Models (MLLMs) tackle this by generating a special token to guide the final mask prediction. However, compressing complex reasoning into a single token loses crucial low-level structural details. This leads to severe geometric ambiguity, causing the predicted masks to have inaccurate boundaries. To solve this, we propose the Geo-DPO framework, which addresses the ambiguity from two directions. First, we introduce the Hierarchical Geometry Adapter (HGA) to structurally inject multi-scale 3D physical features back into the token. Second, we propose Contrastive Affordance Preference Optimization (CAPO), a reward-based training objective that forces the segmentation to align with real geometric boundaries. Extensive experiments on the standard SeqAfford dataset demonstrate that our method achieves state-of-the-art performance, generating highly precise masks and effectively resolving geometric ambiguity. Code is available here.
