CoEvolve: Construct-to-Edit Visual Grounding with Bidirectional State Refinement

arXiv:2610.01710 · cs.AI, cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.AI, cs.CV

Submitted: 2026-10-01

Updated: 2026-10-01

Project page: https://sundongwei.github.io/CoEvolve_Project

Terminology

• Grounding — 78× in this paper · explained in 3 episodes
• Visual Grounding — 17× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Transformer — 8× in this paper · explained in 2 episodes
• Adaptation — 7× in this paper · explained in 1 episode
• Region-Evolution Reinforcement — 7× in this paper
• Accuracy — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Bidirectional Denoising Refiner — 5× in this paper
• Consistency — 5× in this paper · explained in 3 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• GRPO — 4× in this paper · explained in 6 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Group Relative Policy Optimization — 2× in this paper
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• GRPO Objective — 1× in this paper · explained in 2 episodes
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• RSGround-R1: Rethinking Remote Sensing Visual Grounding through Spatial Reasoning
• Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• EchoChange: A Diffusion Language Model with Dual Pass Remasking for Factual Remote Sensing Disaster Change Captioning
• GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision
• ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
• EGM: Efficient Visual Grounding Language Models
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
• Hi-Token: Hierarchical Coordinate Tokenization for Generative Visual Grounding
• IoU-PD: IoU-Aware Privileged Distillation for Visual Grounding with Multimodal Large Language Models

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