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Pengfei Liu

5 papers hereh-index 5665 citations11 works total

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

author position
  • middle author2
  • last author3

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

fields
  • cs.CV4
  • cs.CL1
same name
  • Pengfei Liu — 14 papers, h 7
  • Pengfei Liu — 13 papers, h 7
  • Pengfei Liu — 9 papers, h 5
  • Pengfei Liu — 6 papers, h 3
  • Pengfei Liu — 6 papers, h 2
  • Pengfei Liu — 4 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

collaborators

5 papers

cs.CV2026

Mantis: A Versatile Vision-Language-Action Model with Disentangled Visual Foresight

Yi Yang, Xueqi Li, Yiyang Chen +7

Recent advances in Vision-Language-Action (VLA) models demonstrate that visual signals can effectively complement sparse action supervisions. However, letting VLA directly predict…

cs.CV2025

LiveTalk: Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation

Ethan Chern, Zhulin Hu, Bohao Tang +4

Real-time video generation via diffusion is essential for building general-purpose multimodal interactive AI systems. However, the simultaneous denoising of all video frames with b…

cs.CV2025

Visual Programmability: A Guide for Code-as-Thought in Chart Understanding

Bohao Tang, Yan Ma, Fei Zhang +6

Chart understanding presents a critical test to the reasoning capabilities of Vision-Language Models (VLMs). Prior approaches face critical limitations: some rely on external tools…

cs.CL2025

LIMO: Less is More for Reasoning

Yixin Ye, Zhen Huang, Yang Xiao +3

We challenge the prevailing assumption that complex reasoning in large language models (LLMs) necessitates massive training data. We demonstrate that sophisticated mathematical rea…

cs.CV2025

Thinking with Generated Images

Ethan Chern, Zhulin Hu, Steffi Chern +5

We present Thinking with Generated Images, a novel paradigm that fundamentally transforms how large multimodal models (LMMs) engage with visual reasoning by enabling them to native…

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