cs.SE · 2026-06-24 · No. 33

Software Engineering, 2026-06-24.

3 new papers in cs.SE. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories

    Arsham Khosravani, Audris Mockus

    cs.SE · cs.AI

    Generative AI coding agents are entering the open-source supply chain, yet their diverse and often invisible traces leave their prevalence poorly understood. We introduce a multi-layered detection framework that integrates configuration-file scanning, commit-message analysis, author-identity matching, and bot-signature lookup across World of Code (180M+ Git repositories), classifying agent traces into four behavioral types. No single method...

    arxiv.org/abs/2606.24429 · PDF

  2. 02

    AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming

    Pingchuan Ma, Zhaoyu Wang, Zimo Ji, Yuguang Zhou, Zhantong Xue, Zongjie Li, Shuai Wang, Xiaoqin Zhang

    cs.SE · cs.AI · cs.CR

    Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments. However, their autonomy poses significant safety risks: agents may execute destructive commands, leak sensitive data, or violate domain constraints. Existing safety approaches face a fundamental tradeoff: hand-crafted rules are interpretable but brittle, with overly conservative rules blocking safe...

    arxiv.org/abs/2606.24245 · PDF

  3. 03

    Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy

    Youran Sun, Xingyu Ren, Chugang Yi, Jiaxuan Guo, Kejia Zhang, Jianda Du, Haizhao Yang

    cs.SE · cs.AI · cs.CL · cs.MA

    Large language models are making research production scalable, shifting the bottleneck from producing artifacts to judging claims. We present \textsc{Agon}, a research orchestrator that validates what can be checked inside the workflow and leaves the remaining judgments to human scientists. \textsc{Agon} is built on six design principles: Prompt Economy, Future-Facing, Minimal Prompts, OmniDisciplinary, Massive Parallelism, and Zero-Code. We...

    arxiv.org/abs/2606.24177 · PDF

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