activity
20242026
collaborators

7 papers

cs.AI2026

LifeFuse-Mem: Lifecycle-Aware State Fusion Against Temporary Overwriting for Long-Term Memory

Hanyu Zhao, Yuqian Feng, Zhenyu Song +4

Long-running LLM agents require memory mechanisms that maintain coherent internal states across interactions. We study a lifecycle-labeled memory setting in which write episodes pr…

cs.AI2025

Accelerate Scaling of LLM Finetuning via Quantifying the Coverage and Depth of Instruction Set

Chengwei Wu, Li Du, Hanyu Zhao +4

Scaling the amount of data used for supervied fine-tuning(SFT) does not guarantee the proportional gains in model performance, highlighting a critical need to understand what makes…

cs.CV2025

CI-VID: A Coherent Interleaved Text-Video Dataset

Yiming Ju, Jijin Hu, Zhengxiong Luo +7

Text-to-video (T2V) generation has recently attracted considerable attention, resulting in the development of numerous high-quality datasets that have propelled progress in this ar…

cs.AI2025

Scaling Towards the Information Boundary of Instruction Sets: The Infinity Instruct Subject Technical Report

Li Du, Hanyu Zhao, Yiming Ju +1

Instruction tuning has become a foundation for unlocking the capabilities of large-scale pretrained models and improving their performance on complex tasks. Thus, the construction…

cs.CL2025

Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models

Jijie Li, Li Du, Hanyu Zhao +5

Large Language Models (LLMs) demonstrate strong performance in real-world applications, yet existing open-source instruction datasets often concentrate on narrow domains, such as m…

cs.CL2024

Beyond IID: Optimizing Instruction Learning from the Perspective of Instruction Interaction and Dependency

Hanyu Zhao, Li Du, Yiming Ju +2

With the availability of various instruction datasets, a pivotal challenge is how to effectively select and integrate these instructions to fine-tune large language models (LLMs).…

cs.CL2024

AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies

Bo-Wen Zhang, Liangdong Wang, Ye Yuan +24

In recent years, with the rapid application of large language models across various fields, the scale of these models has gradually increased, and the resources required for their…