Continual Semantic Segmentation (CSS) requires learning new classes without forgetting previously acquired knowledge, addressing the fundamental challenge of catastrophic forgetting in dense prediction tasks. However, existing CSS methods typically employ single-stage encoder-decoder architectures where segmentation masks and class labels are tightly coupled, leading to interference between old and new class learning and suboptimal retention-plasticity balance. We introduce DecoupleCSS, a novel two-stage framework for CSS. By decoupling class-aware detection from class-agnostic segmentation, DecoupleCSS enables more effective continual learning, preserving past knowledge while learning new classes. The first stage leverages pre-trained text and image encoders, adapted using LoRA, to encode class-specific information and generate location-aware prompts. In the second stage, the Segment Anything Model (SAM) is employed to produce precise segmentation masks, ensuring that segmentation knowledge is shared across both new and previous classes. This approach improves the balance between retention and adaptability in CSS, achieving state-of-the-art performance across a variety of challenging tasks.

Traditional video reasoning segmentation methods rely on supervised fine-tuning, which limits generalization to out-of-distribution scenarios and lacks explicit reasoning. To address this, we propose VideoSeg-R1, the first framework to introduce reinforcement learning into video reasoning segmentation. It adopts a decoupled architecture that formulates the task as joint referring image segmentation and video mask propagation. It comprises three stages: (1) A hierarchical text-guided frame sampler to emulate human attention; (2) A reasoning model that produces spatial cues along with explicit reasoning chains; and (3) A segmentation-propagation stage using SAM2 and XMem. A task difficulty-aware mechanism adaptively controls reasoning length for better efficiency and accuracy. Extensive evaluations on multiple benchmarks demonstrate that VideoSeg-R1 achieves state-of-the-art performance in complex video reasoning and segmentation tasks.

Automated scoring of open-ended questions continues to face significant challenges in modeling student cognition, ensuring scoring consistency, and providing interpretability. Although large language models (LLMs) have demonstrated substantial potential, single-model architectures exhibit structural limitations in handling complex reasoning and cognitive alignment. To address these issues, we propose CogMAS, a Cognitively-Grounded Multi-Agent Scoring Framework that incorporates three types of agents, i.e., student agents, teacher agents, and evaluation agents, to enable multidimensional and interpretable scoring of open-ended responses. CogMAS leverages Bloom’s taxonomy to construct a mapping between questions and cognitive dimensions, guiding teacher agents to perform dimension-aware scoring. A dual-stage semantic retrieval module is introduced to provide contextually relevant exemplars. Evaluation agents are responsible for detecting explanation path biases and deriving high-confidence reasoning chains and final scores. Teacher agents are further trained using Direct Preference Optimization (DPO) to improve the quality and consistency of scoring explanations. High-confidence score–explanation pairs are stored in a retrievable memory module to support continuous optimization in future tasks. Experiments on three public open-ended question scoring datasets demonstrate that CogMAS achieves state-of-the-art performance in both scoring accuracy and consistency, validating its effectiveness and generalizability.