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researcher

Hang Yan

5 papers hereh-index 341 citations8 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 5 papers where every author was matched, so the position is known.

fields
  • cs.AI3
  • cs.CL1
  • cs.SE1
same name
  • Hang Yan — 10 papers, h 5
  • Hang Yan — 10 papers, h 7
  • Hang Yan — 9 papers, h 24
  • Hang Yan — 4 papers, h 7
  • Hang Yan — 4 papers, h 3
  • Hang Yan — 4 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

works on
benchmark improvement 1data compilation 1executable agent training 1requirement-first synthesis 1task generation scaling 1

From the 1 of 5 linked papers with an AI index.

most citedBuilding Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark

1 citations · 1 across the 3 of their papers we have counts for

collaborators
Showing cs.AIShow all

3 papers · 1 filter

cs.AI2026

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning

Bihao Zhan, Jie Zhou, Junsong Li +9

Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to accumulating diverse information. T…

cs.AI2026★ 1 cited

Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark

Yuxuan Cai, Yipeng Hao, Jie Zhou +14

As AI advances toward general intelligence, the focus is shifting from systems optimized for static tasks to creating open-ended agents that learn continuously. In this paper, we i…

cs.AI2025

Black-box Model Merging for Language-Model-as-a-Service with Massive Model Repositories

Shilian Chen, Jie Zhou, Tianyu Huai +9

Model merging refers to the process of integrating multiple distinct models into a unified model that preserves and combines the strengths and capabilities of the individual models…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.