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

Heng Chang

AAAI 2026ACL 2026ICML 2026KDD 2026SIGIR 2026推理代码对齐与偏好智能体与工具多模态
新星分贡献者
69
一作贡献0
核心/参与33
方向广度19
合作网络12
0 一作 · 5 篇 · AAAI 2026 / ACL 2026 / ICML 2026 / KDD 2026 / SIGIR 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

5
作者位次 #4/11ACL 2026Findings· Findings of ACL

From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning

Guo Tang · Ke Cheng · HuiMing Fan · Heng Chang · Wenxiang Zheng · Xianhao Ou · Junjia Xiang · Ming Liu · Yujun Zhou · Li Lanyu · Bing Qin

摘要

Temporal Knowledge Graph (TKG) forecasting faces significant challenges due to distribution shifts and poor inductive generalization in parametric models. While Large Language Models (LLMs) offer potent semantic reasoning, existing LLM-based approaches struggle with implicit modality alignment an...

推理代码对齐与偏好OpenReview 项目页
作者位次 #4/7SIGIR 2026Oral· Information Retrieval (Full)

Cross-Domain Interest Representation Learning for Scenario- and Task-Aware Recommendation

Bokai Lin · Naijun Gao · Yucen Gao · Heng Chang · Cheng Hu · Zhinan Zhang · Xiaofeng Gao

摘要

Many internet companies operate multiple flagship applications, each of which can be regarded as a distinct business domain, covering areas such as video, reading, and gaming. Within each domain, diverse recommendation scenarios coexist, and users engage in various tasks with heterogeneous behavi...

多模态代码对齐与偏好OpenReview

常见合作者