◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Xudong Han

10 papers hereh-index 579 citations16 works total

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

author position
  • middle author8

Across the 8 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CL8
  • cs.LG2
same name
  • Xudong Han — 20 papers, h 15
  • Xudong Han — 7 papers, h 7
  • Xudong Han — 6 papers, h 5
  • Xudong Han — 4 papers, h 2
  • Xudong Han — 3 papers, h 2
  • Xudong Han — 2 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

activity
20242026
most citedControl Illusion: The Failure of Instruction Hierarchies in Large Language Models

2 citations · 2 across the 10 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.LG2025

K2-Think: A Parameter-Efficient Reasoning System

Zhoujun Cheng, Richard Fan, Shibo Hao +28

K2-Think is a reasoning system that achieves state-of-the-art performance with a 32B parameter model, matching or surpassing much larger models like GPT-OSS 120B and DeepSeek v3.1.…

cs.CL2025

Llama-3-Nanda-10B-Chat: An Open Generative Large Language Model for Hindi

Monojit Choudhury, Shivam Chauhan, Rocktim Jyoti Das +27

Developing high-quality large language models (LLMs) for moderately resourced languages presents unique challenges in data availability, model adaptation, and evaluation. We introd…

cs.CL2025★ 2 cited

Control Illusion: The Failure of Instruction Hierarchies in Large Language Models

Yilin Geng, Haonan Li, Honglin Mu +5

Large language models (LLMs) are increasingly deployed with hierarchical instruction schemes, where certain instructions (e.g., system-level directives) are expected to take preced…

cs.CL2025

SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning

Renxi Wang, Honglin Mu, Liqun Ma +5

Long-context understanding has emerged as a critical capability for large language models (LLMs). However, evaluating this ability remains challenging. We present SCALAR, a benchma…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.