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Jie Fu

6 papers hereh-index 329 citations8 works total

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

author position
  • middle author3
  • last author2

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CL2
  • cs.PL2
  • cs.CV1
  • cs.LG1
same name
  • Jie Fu — 50 papers, h 25
  • Jie Fu — 30 papers, h 16
  • Jie Fu — 24 papers, h 16
  • Jie Fu — 14 papers, h 11
  • Jie Fu — 12 papers, h 3
  • Jie Fu — 11 papers, h 15

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
collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CL2025

UltraLLaDA: Scaling the Context Length to 128K for Diffusion Large Language Models

Guangxin He, Shen Nie, Fengqi Zhu +6

Diffusion LLMs have attracted growing interest, with plenty of recent work emphasizing their great potential in various downstream tasks; yet the long-context behavior of diffusion…

cs.PL2025

VeriEquivBench: An Equivalence Score for Ground-Truth-Free Evaluation of Formally Verifiable Code

Lingfei Zeng, Fengdi Che, Xuhan Huang +4

Formal verification is the next frontier for ensuring the correctness of code generated by Large Language Models (LLMs). While methods that co-generate code and formal specificatio…

cs.PL2025

Local Success Does Not Compose: Benchmarking Large Language Models for Compositional Formal Verification

Xu Xu, Xin Li, Xingwei Qu +2

We introduce DafnyCOMP, a benchmark for evaluating large language models (LLMs) on compositional specification generation in Dafny. Unlike prior benchmarks that focus on single-fun…

cs.CL2025

Re:Form -- Reducing Human Annotations in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny

Chuanhao Yan, Fengdi Che, Xuhan Huang +12

Existing informal language-based (e.g., human language) Large Language Models (LLMs) trained with Reinforcement Learning (RL) face a significant challenge: their verification proce…

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