most citedLLM-based Human Simulations Have Not Yet Been Reliable

5 citations · 7 across the 37 of their papers we have counts for

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cs.SE2026

SemaPLC: A Project-Grounded, Verification-Gated Agent Harness for PLC Code Generation

Yanlun Tu, Huacan Wang, Jin Xie +11

In industrial practice, AI-generated programmable logic controller (PLC) code must integrate with existing projects and implement the required behavior across scan cycles. Compilat…

cs.SE2026

Sema Code: Decoupling AI Coding Agents into Programmable, Embeddable Infrastructure

Huacan Wang, Jie Zhou, Ningyan Zhu +8

AI coding agents have become central to developer workflows, yet every existing solution locks its reasoning capabilities within a specific delivery form, such as a CLI, IDE plugin…

cs.SE2026

MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences

Qihao Wang, Ziming Cheng, Shuo Zhang +12

While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a "closed-world" limitation: they attempt to fix bugs from scratc…

cs.SE2025

GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveraging

Ziyi Ni, Huacan Wang, Shuo Zhang +15

Beyond scratch coding, exploiting large-scale code repositories (e.g., GitHub) for practical tasks is vital in real-world software development, yet current benchmarks rarely evalua…

cs.SE2025

RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solving

Huacan Wang, Ziyi Ni, Shuo Zhang +11

The ultimate goal of code agents is to solve complex tasks autonomously. Although large language models (LLMs) have made substantial progress in code generation, real-world tasks t…