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Min Yang

4 papers hereh-index 234 citations6 works total

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

author position
  • first author1
  • middle author1

Across the 2 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI3
  • cs.LG1
same name
  • Min Yang — 23 papers
  • Min Yang — 19 papers, h 15
  • Min Yang — 19 papers, h 6
  • Min Yang — 19 papers, h 6
  • Min Yang — 13 papers, h 8
  • Min Yang — 13 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

most citedPartially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention

23 citations · 23 across the 3 of their papers we have counts for

collaborators

4 papers

cs.LG2026

VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agents

Jiaju Chen, Min Yang, Jinghua Piao +4

Multi-turn LLM agents often receive sparse task feedback across several interactions, while generating each response token by token. This creates two related credit-assignment ques…

cs.AI2026

From Scoring to Acting: Outcome-Verified Comparative Self-Distillation for LLM Agents

Xu Xia, Jinghua Piao, Min Yang +3

Recent work on LLM agents is shifting from external capability elicitation to capability internalization, enabling agents to retain useful skills without retrieval at inference tim…

cs.AI2026

SkillMaster: Toward Autonomous Skill Mastery in LLM Agents

Min Yang, Jinghua Piao, Xu Xia +4

Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and selection are typically governed…

cs.AI2023★ 23 cited

Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention

Min Yang, Guanjun Liu, Ziyuan Zhou

Traditional multi-agent reinforcement learning algorithms are difficultly applied in a large-scale multi-agent environment. The introduction of mean field theory has enhanced the s…

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