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researcher

Shaofei Cai

Peking University

15 papers hereh-index 131.8k citations32 works total

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

author position
  • first author5
  • middle author8

Across the 13 of 15 papers where every author was matched, so the position is known.

fields
  • cs.AI5
  • cs.CV3
  • cs.CL2
  • cs.RO2
  • cs.AR1
  • cs.LG1
affiliations
  • Peking University
HomepageORCID 0000-0002-1195-7276
same name
  • Shaofei Cai — 3 papers, h 2

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
automated test generation 1code reasoning 1large language model evaluation 1model robustness 1problem augmentation 1

From the 1 of 15 linked papers with an AI index.

activity
20242026
collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies

Guangyu Zhao, Kewei Lian, Haoxuan Ru +8

Goal-conditioned policies enable decision-making models to execute diverse behaviors based on specified goals, yet their downstream performance is often highly sensitive to the cho…

cs.AI2025

ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment

Shaofei Cai, Zhancun Mu, Anji Liu +1

We aim to develop a goal specification method that is semantically clear, spatially sensitive, domain-agnostic, and intuitive for human users to guide agent interactions in 3D envi…

cs.AI2025

MineStudio: A Streamlined Package for Minecraft AI Agent Development

Shaofei Cai, Zhancun Mu, Kaichen He +4

Minecraft's complexity and diversity as an open world make it a perfect environment to test if agents can learn, adapt, and tackle a variety of unscripted tasks. However, the devel…

cs.AI2024

GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents

Shaofei Cai, Bowei Zhang, Zihao Wang +4

Developing agents that can follow multimodal instructions remains a fundamental challenge in robotics and AI. Although large-scale pre-training on unlabeled datasets (no language i…

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