cs.SE · 2026-09-01 · No. 102

Software Engineering, 2026-09-01.

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

    Auditing Anonymous AI Models: A Four-Stage Protocol for Black-Box Identity Verification

    Yisen Xi

    cs.SE · cs.AI · cs.CR

    The 2025--2026 AI market has seen a wave of stealth releases: frontier models launched anonymously on developer platforms under codenames. For their users, identity determines data-handling terms, supply-chain risk, and capability expectations. No validated methodology exists for black-box identity verification of anonymous models: practitioner checklists lack accuracy evidence, and self-identification is untrustworthy by design. We propose a...

    arxiv.org/abs/2608.31142 · PDF

  2. 02

    LLM Post-Training as Brownfield Maintenance: An Industrial Perspective on Dataware Engineering

    Gopi Krishnan Rajbahadur, Amir M. Ebrahimi, Boyuan Chen, Ahmed E. Hassan

    cs.SE · cs.AI · cs.LG

    Industrial post-training is a brownfield regime. Teams inherit a deployed checkpoint and must land targeted improvements under fixed compute and mixture budgets without regressing the rest. The maintained artifact is increasingly dataware: behavior governed by a curated post-training mixture, updated via bounded mixture patches rather than clean-slate retraining. From an industrial code-generation improvement effort, we offer a maintainer's...

    arxiv.org/abs/2608.31102 · PDF

  3. 03

    On the Prospects of Dynamic LLM Conversations in Software Development

    Annemarie Wittig, Alina Mailach, Janet Siegmund, Norbert Siegmund

    cs.SE · cs.AI

    Large language models (LLMs) have become an essential tool for assisting developers, yet we still lack knowledge on ways to effectively support their interactions during development activities. That is, the quality of interactions with a chat-based LLM still strongly depends on how developers phrase prompts and which information they include. Our goal is to evaluate whether interventions into these interactions with LLMs have an effect on...

    arxiv.org/abs/2608.30756 · PDF

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