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Xuezhi Cao

22 papers hereh-index 7397 citations27 works total

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

author position
  • middle author21

Across the 21 of 22 papers where every author was matched, so the position is known.

fields
  • cs.AI7
  • cs.CL5
  • cs.CV4
  • cs.SE3
  • cs.LG2
  • cs.SD1
same name
  • Xuezhi Cao — 19 papers, h 3
  • Xuezhi Cao — 10 papers, h 14
  • Xuezhi Cao — 6 papers, h 4
  • Xuezhi Cao — 3 papers, h 1
  • Xuezhi Cao — 1 paper
  • Xuezhi Cao — 1 paper

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

activity
20232026
most citedA Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily

6 citations · 7 across the 21 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation

Kaining Ying, Hengrui Hu, Siyu Ren +6

Interactive world models are advancing rapidly, yet existing benchmarks cover only part of the required competencies, leaving no unified standard for systematic evaluation. To fill…

cs.CV2026

LongCat-Next: Lexicalizing Modalities as Discrete Tokens

Meituan LongCat Team, Bin Xiao, Chao Wang +86

The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal syste…

cs.CV2024

Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs

Peng Ding, Jingyu Wu, Jun Kuang +6

Multi-modal Large Language Models (MLLMs) have demonstrated remarkable performance on various visual-language understanding and generation tasks. However, MLLMs occasionally genera…

cs.CV2023★ 1 cited

Exchanging-based Multimodal Fusion with Transformer

Renyu Zhu, Chengcheng Han, Yong Qian +5

We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from…

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