cs.SE · 2026-08-26 · No. 96

Software Engineering, 2026-08-26.

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

    Automatic Model Card Generation Using an LLM

    Tajkia Rahman Toma, Balreet Grewal, Cor-Paul Bezemer

    cs.SE · cs.AI

    Model cards are structured documents that summarize key information about machine learning models to improve transparency, usability, and accountability. However, they often lack a consistent structure, and many models provide no model cards, making comparison and interpretation difficult. This paper presents two contributions. First, we propose MCTidy, an LLM-based approach that reorganizes existing model cards into a standardized template...

    arxiv.org/abs/2608.24807 · PDF

  2. 02

    A Literate Programming Environment for Human and Machine Agents

    Adam T. Burke

    cs.SE · cs.AI · cs.PL

    This paper introduces an environment for constructing literate programs in concert with language-aware machine agents. This environment includes a grammar for executable program essays, a parser that treats names as first-class objects, an internal name-graph which relates prose, names and executable artifacts, and a binding mechanism for existing languages and testing toolsets. This supports co-location of code with its most relevant natural...

    arxiv.org/abs/2608.24644 · PDF

  3. 03

    LumiXAI: A Modular Full-Stack Framework for Feature Attribution

    Alfio Ferrara, Lorenzo Gatta, Sergio Picascia, Elisabetta Rocchetti

    cs.SE · cs.AI

    Feature attribution is a central tool of model interpretability, yet the software through which it is applied remains fragmented: individual tools specialize along narrow axes, such as a single modality, a code API or a GUI, or a fixed rather than extensible method set, and rarely combine these strengths. Moreover, many explainability tools are designed primarily for domain experts, requiring programming skills or familiarity with attribution...

    arxiv.org/abs/2608.24524 · PDF

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