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Kun Hu

7 papers hereh-index 319 citations7 works total

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

author position
  • middle author4
  • last author2

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • cs.AI1
  • cs.CL1
  • cs.LG1
  • math.AG1
same name
  • Kun Hu — 16 papers, h 17
  • Kun Hu — 14 papers, h 5
  • Kun Hu — 4 papers, h 4
  • Kun Hu — 3 papers, h 4
  • Kun Hu — 2 papers, h 6
  • Kun Hu — 2 papers

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 citedDuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcasting

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

math.AG2026

A note on stability conditions on product-quotient surfaces

Uttaran Dutta

Let X be a product-quotient surface, which is by definition a minimal resolution of (C1​×C2​)/G, where Ci​ are smooth curves of genus ≥2 and G is a finite group…

cs.AI2026

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers

Huanxi Liu, Kun Hu, Jiaqi Liao +6

As Model Context Protocol (MCP) servers emerge as the core infrastructure for connecting LLMs with external tools, existing benchmarks leverage real-world MCP servers to evaluate L…

cs.LG2026

Charting Empirical Laws for LLM Fine-Tuning in Scientific Multi-Discipline Learning

Lintao Wang, Zhuqiang Lu, Yilin Zhu +6

While large language models (LLMs) have achieved strong performance through fine-tuning within individual scientific domains, their learning dynamics in multi-disciplinary contexts…

cs.CL2026

Benchmark^2: Systematic Evaluation of LLM Benchmarks

Qi Qian, Chengsong Huang, Jingwen Xu +13

The rapid proliferation of benchmarks for evaluating large language models (LLMs) has created an urgent need for systematic methods to assess benchmark quality itself. We propose B…

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