Jie Yang

ContextWeave: A Real-World Workflow Benchmark

Bo Wang, Yuqian Yao, Enxi Wang, et al.

MEMPROBE: Probing Long-Term Agent Memory via Hidden User-State Recovery

Enze Ma, Yufan Zhou, Wei-Chieh Huang, et al.

SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing

Genhao Tian, Taihua Xu, Shuyin Xia, et al.

Beyond Rating: A Comprehensive Evaluation and Benchmark for AI Reviews

Bowen Li, Haochen Ma, Yuxin Wang, et al.

VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

Xiangbo Gao, Sicong Jiang, Bangya Liu, et al.

Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection

Ruilin Li, Heming Zou, Xiufeng Yan, et al.

This paper addresses a problem in continual learning (where AI models learn new tasks sequentially without forgetting old ones) by improving how randomly initialized projection layers work with pre-trained models. The authors propose SCL-MGSM, a method that intelligently selects which random features to use rather than using purely random ones, making the system more stable and accurate when learning new tasks with limited examples.