paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
HJ

Hai Jin

ICLR 2026×4EuroSys 2026×3MICRO 2026×3NDSS 2026×3ISCA 2026×2AAAI 2026ACL 2026CCS 2026CVPR 2026HPCA 2026INFOCOM 2026KDD 2026OSDI 2026PPoPP 2026SIGIR 2026VLDB 2026效率与系统多模态检索与知识代码安全
新星分资深
58
一作贡献0
核心/参与155
方向广度19
合作网络12
0 一作 · 26 篇 · ICLR 2026 / EuroSys 2026 / MICRO 2026 / NDSS 2026 / ISCA 2026 / AAAI 2026 / ACL 2026 / CCS 2026 / CVPR 2026 / HPCA 2026 / INFOCOM 2026 / KDD 2026 / OSDI 2026 / PPoPP 2026 / SIGIR 2026 / VLDB 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

26
作者位次 #20/20OSDI 2026Oral· Operating Systems & Distributed Systems

JANUS: Cross-World, Cooperative Nested Virtualization for Secure Containers

Jiangshan Lai · Hang Huang · Quan Xu · Zhen Ren · Wenlong Hou · Wei Guo · Jia Rao · Hui Lu · Weidong Han · Jiesheng Wu · Jiang Liu · Naixuan Guan · Yibin Shen · Feng Yu · Xu Wang · Shiqiang Zhang · Zhiheng Tao · Yisheng Xie · Song Wu · Hai Jin

摘要

Secure containers such as Kata Containers strengthen isolation by running each container inside a lightweight VM. On today’s virtualized clouds, this creates an unavoidable form of nested virtualization with dominant cost in memory virtualization. Existing approaches entangle CPU virtualization a...

可解释性评测与基准检索与知识OpenReview
作者位次 #6/6ACL 2026Findings· Findings of ACL

Can AI Revise Research Papers with Human Review Feedback? An Empirical Study and Benchmark

Zihan Luo · Hong Huang · Jianxun Lian · Yu Chang · Xing Xie · Hai Jin

摘要

The rise of Human-AI collaboration can effectively speed up the research process for experts and allow anyone with critical thinking skills to conduct innovative work. A key part of this collaboration is the AI’s ability to improve a paper with human feedback—updating both the text and experiment...

评测与基准智能体与工具可解释性OpenReview 项目页
作者位次 #5/6SIGIR 2026Oral· Information Retrieval (Full)

SRAG: A Lightweight and Specialized Retrieval-augmented Generation System at the Edge

Ruikun Luo · Zihan Xing · Lin Gu · Song Wu · Hai Jin · Xiaoyu Xia

摘要

Retrieval-augmented generation (RAG) has shown strong potential for deploying large language models at the edge, yet existing designs largely rely on generic and monolithic knowledge bases that are poorly matched to the heterogeneous queries and resource-constrained edge computing environments. T...

检索与知识效率与系统对齐与偏好OpenReview

常见合作者