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

Jiaming Wang

ACL 2026×2推理评测与基准智能体与工具
新星分新星
71
一作贡献60
核心/参与0
方向广度6
合作网络3
2 一作 · 2 篇 · ACL 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

2
一作ACL 2026Findings· Findings of ACL

SOP -Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures

Jiaming Wang · Zhe Tang · Zehao Jin · Hefei Chen · Yilin Jin · Peng Ding · Xiaoyu Li · Xuezhi Cao

摘要

As large language models (LLMs) are widely deployed as domain-specific agents, many benchmarks have been proposed to evaluate their ability to follow instructions and make decisions in real-world scenarios. However, business scenarios often involve complex standard operating procedures (SOPs), an...

推理评测与基准智能体与工具OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLM s’ Instruction Following Capability

Jiaming Wang · Yunke Zhao · Peng Ding · Jun Kuang · Yibin Shen · Zhe Tang · Yilin Jin · ZongYu Wang · Xiaoyu Li · Xuezhi Cao

摘要

The capability to precisely adhere to instructions is a cornerstone for Large Language Models (LLMs) to function as dependable agents in real-world scenarios. However, confronted with complex prompts, LLMs frequently encounter difficulties in fulfilling all specified requirements within a single...

推理评测与基准智能体与工具OpenReview 项目页

常见合作者