让下一篇重要论文，不再错过。

原文摘要

Affordance grounding asks which visible part of an object performs a requested operation, a basic capability for embodied manipulation and human--robot interaction. Most existing methods follow a System Two paradigm: a multimodal language model first writes a chain of thought and then turns its conclusion into a box or points for a segmenter. This incurs considerable inference cost, gives the semantic decision little direct control over the extent of the final region, and makes mistakes hard to correct. Yet locating an affordance is essentially a perceptual judgment that need not be argued out in words. We therefore propose, in which frozen segmenters propose candidate regions and a System One model decides directly which of them perform the operation, generating no text and reading each decision from a single forward pass. Our contributions are twofold. (1) An inference framework that confirms candidate regions level by level, from object to part to sub-part, with evidence passed between the levels in both directions so that the levels correct one another. (2) A System One model trained for this framework: existing System One models are built for general-purpose decisions, judge one candidate per request and are ill-suited to the framework's region questions, so ours learns directly from the requests the framework itself produces and judges all candidates of a level at once. Trained only on InstructPart and evaluated with definition-aligned operation words, outperforms chain-of-thought methods in domain and in zero-shot transfer to UMD and IIT-AFF, is better overall than existing System One models run in the same framework, and needs fewer judge requests. To our knowledge, is the first approach to bring System One models to affordance grounding.