NS-Copilot is a large‑language‑model driven multi‑agent system designed to automate neuroscience data analysis. It integrates domain‑specific pre‑trained models for modalities such as EEG and extracellular spike data, and uses a natural‑language interface to orchestrate agents that plan, generate code, and synthesize results. In benchmarks on Alzheimer’s, Parkinson’s, and working‑memory spike decoding, the system consistently outperformed strong baselines across multiple trials.
By Wuche Liu, Yiran Qiao, Linlin Hou, Rui Yang, Shusen Pu, Song Wang, Jing Ma
arXiv:2607. 15079v1 Announce Type: new Abstract: Understanding the brain increasingly depends on integrating evidence across scales, modalities, and disciplines.
By Haoxuan Li, Tianci Gao, Jianhe Li, Yang Fan, Runze Shi, Weiran Wang, Tianxiang Zhao, Zezhao Wu, Xiaoyang Jiang, Qihui Zhang, Jia Li, Xiao Xiao, Kai Du, Xiaoxuan Jia, Chao Xie, Lu Mi
arXiv:2607. 29347v1 Announce Type: cross Abstract: Modern neuroscience relies on integrating multi-scale, multimodal datasets to uncover the neural principles underlying intelligence.
By Jiamin Wu, Peishan Xiang, Jingyang Chen, Yuqing Zhu, Yuxi Li, Ling Luo, Qihao Zheng, Jialiang Zu, Yongchao Wu, Mindong Liu, Haitao Wu, Chaofan Hu, Yijie Sun, Yuqi Hang, Yu Zhu, Shuo Li, Yue Fan, Shiyang Feng, Wanghan Xu, Tianlei Zhang, Jie Zhang, Wenlong Zhang, Bo Zhang, Kai Wang, Lei Bai, Mianxin Liu, Wanli Ouyang, Jiulin Du, Chunfeng Song
As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous and holistic evaluation is essential for measuring progress toward real-world healthcare applications. We introduce HealthAgentBench, a suite of 54 agentic healthcare tasks across 7 categories each with its unique environment.
arXiv:2605. 09366v3 Announce Type: replace Abstract: Transforming neuroimaging data into clinically actionable biomarkers is a knowledge-intensive and labor-intensive process.
By Keqi Han, Songlin Zhao, Yao Su, Xiang Li, Yixuan Yuan, Lifang He, Carl Yang
AutoBCI is an agentic framework that uses a Designer Agent and a Forecaster Agent to discover and select EEG decoding architectures across diverse tasks such as emotion recognition, motor imagery, and sleep staging. The Designer Agent performs Pool‑Guided Architecture Discovery (PGAD) to generate and refine models, while the Forecaster Agent uses Performance Estimation from Early Knowledge (PEEK) to predict full‑budget validation performance from early learning curves. In experiments on 14 EEG datasets, AutoBCI with Claude Opus 5.5 achieved a 64.16% average test balanced accuracy, slightly surpassing the best baseline, and PEEK reduced prediction error by 38.1% compared to the best-observed-score baseline.