Topic: Image Segmentation

We introduce LENS (Learning to Segment Anything with Unified Reinforced Reasoning), a novel framework accepted as an Oral paper at AAAI 2026. Traditional image segmentation models relying on Supervised Fine-Tuning often hit a 'capability ceiling' due to static pattern matching and information bottlenecks between reasoning and execution. To overcome this, we implement an end-to-end reinforcement learning mechanism that co-optimizes high-level Chain-of-Thought reasoning with pixel-level segmentation. By utilizing a Multi-modal Large Language Model like Qwen2.5-VL-3B-Instruct and a dedicated Context Module, LENS bridges the gap between 'thinking' and 'acting,' enabling self-correction even from imperfect initial prompts. This architecture significantly enhances generalization and robustness in complex, open-world scenarios. We believe this advancement offers a strategic path for developing more sophisticated embodied AI and human-robot interaction systems.