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Bei Liu

11 papers hereh-index 314 citations12 works total

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

author position
  • middle author11

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.AI4
  • cs.CV1
  • cs.SE1
same name
  • Bei Liu — 9 papers, h 11
  • Bei Liu — 6 papers, h 10
  • Bei Liu — 5 papers, h 5
  • Bei Liu — 4 papers
  • Bei Liu — 4 papers, h 12
  • Bei Liu — 4 papers, h 1

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 citedSkillOpt: Executive Strategy for Self-Evolving Agent Skills

1 citations · 1 across the 11 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

ReDeck: Step-Level Render-Grounded Refinement for Document-to-Slide Generation

Muzhao Tian, Zezi Zeng, Yifan Yang +14

Document-to-slide generation is challenging because slides are dense editable artifacts that require both faithful content selection and precise spatial layout. Recent slide agents…

cs.AI2026

Token Predictors Are Not Planners: Building Physically Grounded Causal Reasoners

Zheng Lu, Mingqi Gao, Qinlei Xie +8

Current benchmarks for embodied vision-language planning often favor linguistic next-token prediction over physically grounded next-state reasoning. This rewards models that mimic…

cs.AI2026★ 1 cited

SkillOpt: Executive Strategy for Self-Evolving Agent Skills

Yifan Yang, Ziyang Gong, Weiquan Huang +12

Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, an…

cs.AI2026

From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills

Zisu Huang, Jingwen Xu, Yifan Yang +13

Language agents increasingly improve by reusing \emph{skills} -- structured procedural artifacts distilled from past experience. In particular, \emph{domain-level} and \emph{model-…

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