ReferFlow: Beyond Final-Target Localization for Complex Referring Expressions

Tianyi Gao, Han Fang, Xin Wei, Yaoyao Zhong, Zhaoyan Pan +9

Multimodal Large Language Models have made significant progress in referring expression comprehension (REC), yet most REC benchmarks still focus on final-target localization. Complex queries often involve intermediate visual references with different dependencies on prior grounding, but final-only evaluation leaves this process unexamined. To address this gap, we formulate trajectory-level REC, in which models receive an ordered sequence of grounding steps constructed from a single complex query and localize the target regions associated with each step. We instantiate this setting in ReferFlow-Bench, which characterizes structured region transitions through four trajectory types, namely Refinement, Shift, Reset, and Mixture. Experiments show that current models struggle to consistently ground intermediate visual references across successive steps. We further propose ReferFlow, which decouples interleaved text-latent trajectory generation from coordinate decoding. Specifically, Stateful Region Latent Reasoning represents each target region as an object-indexed state composed of continuous latents, with explicit visual and spatial supervision to preserve region information for…