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researcher

Ziyang Li

Johns Hopkins University

15 papers hereh-index 12869 citations21 works total

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

author position
  • first author3
  • middle author10
  • last author1

Across the 14 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CR4
  • cs.AI3
  • cs.LG2
  • cs.PL2
  • cs.CL1
  • cs.CV1
affiliations
  • Johns Hopkins University
Homepage
same name
  • Ziyang Li — 8 papers, h 6
  • Ziyang Li — 6 papers, h 1
  • Ziyang Li — 4 papers, h 3
  • Ziyang Li — 4 papers, h 3
  • Ziyang Li — 3 papers, h 0
  • Ziyang Li — 2 papers, h 5

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
20192026
most citedIRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities

16 citations · 39 across the 14 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CR2025

Locus: Agentic Predicate Synthesis for Directed Fuzzing

Jie Zhu, Chihao Shen, Ziyang Li +3

Directed fuzzing aims to find program inputs that lead to specified target program states. It has broad applications, such as debugging system crashes, confirming reported bugs, an…

cs.CL2025

TurnaboutLLM: A Deductive Reasoning Benchmark from Detective Games

Yuan Yuan, Muyu He, Muhammad Adil Shahid +3

This paper introduces TurnaboutLLM, a novel framework and dataset for evaluating the deductive reasoning abilities of Large Language Models (LLMs) by leveraging the interactive gam…

cs.SE2025★ 2 cited

Challenges and Paths Towards AI for Software Engineering

Alex Gu, Naman Jain, Wen-Ding Li +7

AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be ad…

cs.PL2025★ 1 cited

Lobster: A GPU-Accelerated Framework for Neurosymbolic Programming

Paul Biberstein, Ziyang Li, Joseph Devietti +1

Neurosymbolic programs combine deep learning with symbolic reasoning to achieve better data efficiency, interpretability, and generalizability compared to standalone deep learning…

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