KNOWING HOW MANY IS NOT KNOWING WHICH ONES:NATIVE COMPLETE-SET GROUNDING IN MULTIMODAL LARGE LANGUAGE MODELS

Generalized Referring Expression Comprehension (GREC) requires a model to recover all objects referred to by an expression, including no-target and multi-target cases. We identify a spurious-correlation failure mode in GREC: target cardinality remains partly predictable when visual evidence is removed or mismatched, revealing reliance on language and dataset priors. Yet accurate counting does not imply correct target selection. A strong query-based set decoder predicts the correct count on 98.38% of two-target expressions but achieves only 10.90% Pr for the complete target set. When the query changes to refer to a different pair of objects in the same image, the decoder usually preserves the count but rarely switches to the correct targets. We therefore propose Native Complete-Set Grounding, which trains the MLLM to select and localize all referred objects through its autoregressive localization pathway. In a matched comparison with a separate set-prediction decoder, Pr improves from 50.24% to 77.22%. Our 3B model achieves 81.24/76.18/68.11% Pr on gRefCOCO val/testA/testB, establishing state-of-the-art performance on all three splits. The gains persist at 7B scale while conventional REC performance remains strong.