126 citations · 152 across the 9 of their papers we have counts for
10 papers
Stateless Language Agents: Scaling Long-Horizon Automated Research
Qizheng Zhang, Changxiu Ji, Isaac Sun +15
Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay growing histories, duplicate o…
SETA: Scaling Environments for Terminal Agents
Qijia Shen, Zhiqi Huang, Vamsidhar Kamanuru +19
Large language models (LLMs) are rapidly shifting toward agents that solve tasks through diverse interfaces, including web and graphical user interfaces (GUIs). Among these, the te…
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Qizheng Zhang, Changran Hu, Shubhangi Upasani +10
Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation: modifying inputs with instructions, strategies, or evi…
Enhancing Boundary Segmentation for Topological Accuracy with Skeleton-based Methods
Chuni Liu, Boyuan Ma, Xiaojuan Ban +5
Topological consistency plays a crucial role in the task of boundary segmentation for reticular images, such as cell membrane segmentation in neuron electron microscopic images, gr…
Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts
Han Cui, Alfredo De Goyeneche, Efrat Shimron +2
Image Quality Assessment (IQA) is essential in various Computer Vision tasks such as image deblurring and super-resolution. However, most IQA methods require reference images, whic…
Data privacy protection in microscopic image analysis for material data mining
Boyuan Ma, Xiang Yin, Xiaojuan Ban +4
Recent progress in material data mining has been driven by high-capacity models trained on large datasets. However, collecting experimental data has been extremely costly owing to…
End-to-End Learning for Simultaneously Generating Decision Map and Multi-Focus Image Fusion Result
Boyuan Ma, Xiang Yin, Di Wu +1
The general aim of multi-focus image fusion is to gather focused regions of different images to generate a unique all-in-focus fused image. Deep learning based methods become the m…
SESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion
Boyuan Ma, Xiaojuan Ban, Haiyou Huang +1
In this work, we propose a novel unsupervised deep learning model to address multi-focus image fusion problem. First, we train an encoder-decoder network in unsupervised manner to…
Boundary Learning by Using Weighted Propagation in Convolution Network
Wei Liu, Jiahao Chen, Chuni Liu +5
In material science, image segmentation is of great significance for quantitative analysis of microstructures. Here, we propose a novel Weighted Propagation Convolution Neural Netw…
Data augmentation in microscopic images for material data mining
Boyuan Ma, Xiaoyan Wei, Chuni Liu +10
Recent progress in material data mining has been driven by high-capacity models trained on large datasets. However, collecting experimental data (real data) has been extremely cost…