# ReferFlow: Beyond Final-Target Localization for Complex Referring Expressions

arXiv: https://arxiv.org/abs/2610.20352 · HTML: https://mlsanity.com/paper/2610.20352/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 31 of its 42 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 30 of 31)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Modeling Context in Referring Expressions](https://mlsanity.com/paper/1608.00272/) (2016)
- [Large Language Diffusion Models](https://mlsanity.com/paper/2502.09992/) (2025)
- [Training Large Language Models to Reason in a Continuous Latent Space](https://mlsanity.com/paper/2412.06769/) (2024)
- [LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training](https://mlsanity.com/paper/2509.23661/) (2025)
- [SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation](https://mlsanity.com/paper/2510.06303/) (2025)
- [GRIT: Teaching MLLMs to Think with Images](https://mlsanity.com/paper/2505.15879/) (2025)
- [MiMo-VL Technical Report](https://mlsanity.com/paper/2506.03569/) (2025)
- [Latent Visual Reasoning](https://mlsanity.com/paper/2509.24251/) (2025)
- [Kwai Keye-VL 1.5 Technical Report](https://mlsanity.com/paper/2509.01563/) (2025)
- [Detect Anything via Next Point Prediction](https://mlsanity.com/paper/2510.12798/) (2025)
- [Revisiting Referring Expression Comprehension Evaluation in the Era of Large Multimodal Models](https://mlsanity.com/paper/2406.16866/) (2024)
- [Interleaved Latent Visual Reasoning with Selective Perceptual Modeling](https://mlsanity.com/paper/2512.05665/) (2025)
- [LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding](https://mlsanity.com/paper/2605.27365/) (2026)
- [Rex-Thinker: Grounded Object Referring via Chain-of-Thought Reasoning](https://mlsanity.com/paper/2506.04034/) (2025)
- [Referring to Any Person](https://mlsanity.com/paper/2503.08507/) (2025)
- [Youtu-VL: Unleashing Visual Potential via Unified Vision-Language Supervision](https://mlsanity.com/paper/2601.19798/) (2026)
- [WeDetect: Fast Open-Vocabulary Object Detection as Retrieval](https://mlsanity.com/paper/2512.12309/) (2025)
- [Visual Reasoning Tracer: Object-Level Grounded Reasoning Benchmark](https://mlsanity.com/paper/2512.05091/) (2025)
- [GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation](https://mlsanity.com/paper/2512.17495/) (2025)
- [Ref-Adv: Exploring MLLM Visual Reasoning in Referring Expression Tasks](https://mlsanity.com/paper/2602.23898/) (2026)
- [VGent: Visual Grounding via Modular Design for Disentangling Reasoning and Prediction](https://mlsanity.com/paper/2512.11099/) (2025)
- [Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction](https://mlsanity.com/paper/2606.05769/) (2026)
- [ProLaViT: Learning Progressive Latent Visual Thoughts in Structured Latent Space](https://mlsanity.com/paper/2607.02907/) (2026)

## Abstract (arXiv)

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 subsequent grounding. Trajectory-Aware Parallel Decoding then recovers all coordinate blocks in parallel through four geometry-constrained refinement rounds, with each block conditioned on its corresponding trajectory prefix, thereby reducing within-box sequential dependencies and eliminating cross-box autoregression. We further apply Grounding-Aware GRPO to promote consistent grounding across steps and learn adaptive trajectory generation from complex REC queries. ReferFlow achieves leading performance on general REC benchmarks and ReferFlow-Bench.