The paper proposes a shift from AI model storage to AI model management, introducing the concept of "learnware"—a combination of a model and its specification. Learnware specifications are generated without exposing developers’ training data, enabling models from different sources to be identified, reused, and assembled for new tasks. The Learnware Dock System (LDS) offers a framework for managing these learnwares and facilitates collaboration among independently developed models through a shared specification protocol.
By Zhi-Hua Zhou
arXiv:2604. 10311v2 Announce Type: replace Abstract: Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications.
By Fabio Porto, Eduardo Ogasawara, Gabriela Moraes Botaro, Julia Neumann Bastos, Augusto Fonseca, Esther Pacitti, Patrick Valduriez
arXiv:2608. 20201v1 Announce Type: new Abstract: Software form has undergone two paradigm shifts since its inception: Software 1.
By Wei Lin, Tao Zhou, Zhaofei Xie, Changgui Hong
TuiML is a machine‑learning library specifically designed for AI agents rather than human programmers. It offers native algorithms for supervised, unsupervised, time‑series, data handling, tuning, and evaluation tasks, with each component exposing machine‑readable metadata and parameter schemas so agents can search, inspect, compose, and validate workflows autonomously. The library ensures every call is validated, seeded, and traced, and sessions can be exported as runnable notebooks, making experiments reproducible by construction. Benchmarks indicate TuiML remains predictively competitive with scikit‑learn and Weka, while keeping data and models confined to the local machine.
By Nilesh Verma, Nick Lim, Albert Bifet, Bernhard Pfahringer
The paper discusses a third paradigm shift in software development, termed Software 3.0, where context and reasoning drive behavior. It proposes that Software 3.0 converges to three core components: a generalized database for all persistent state, a large model that performs reasoning and generation, and an agent that orchestrates the interaction between the two. The authors formalize this convergence, present a minimal reference architecture, and analyze its applicability and limits, noting that it applies best to task domains that are expressible, verifiable, externally stateful, and tool-complete.
arXiv:2602. 12430v4 Announce Type: replace-cross Abstract: The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice.
By Renjun Xu, Yang Yan
The paper argues that while machine-readable models like SysML v2 enable AI participation in systems engineering, the data architecture surrounding these models must also be robust. It introduces the concept of epistemic adequacy, split into read‑side adequacy (ensuring derivations, status, and provenance are answerable) and write‑side admissibility (filtering AI contributions before they enter the record). The authors illustrate their ideas using the public Apollo 11 SysML v2 reconstruction and propose a Governed‑Query Architecture Framework to enforce these principles.
By Jason Gower, Michael J. de C. Henshaw, Siyuan Ji
The paper introduces SMART, a symbolic performance‑modeling library for machine‑learning systems that relies almost entirely on natural‑language design documents rather than code. By using AI coding agents to regenerate the implementation from these documents, the framework eliminates the need for continuous refactoring as models and systems evolve. The authors demonstrate that regenerated implementations match hand‑audited reference models to round‑off precision, suggesting that design documents can serve as the durable artifact for ML‑systems co‑design tools.
By Samuel Kushnir, Kimia Noorbakhsh, Kavya Sreedhar, Liqun Cheng, Ming Liu, Parthasarathy Ranganathan, Mohammad Alizadeh, Fred Kjolstad, Suvinay Subramanian
arXiv:2606. 14350v1 Announce Type: cross Abstract: Artificial Intelligence (AI) systems must typically satisfy service-level objectives including accuracy, latency, and cost.
By Milos Gravara, Andrija Stanisic, Stefan Nastic
arXiv:2607. 17242v1 Announce Type: cross Abstract: Pretrained machine learning (ML) models help developers build ML-intensive software systems without training models from scratch.
By Md Erfan, Ahmed Ryan, Md Rayhanur Rahman
arXiv:2606. 00288v1 Announce Type: new Abstract: Large language models are undergoing a transition from model technology to system technology.
By Hai Lin
Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills introduces DisCo, a research agent that extracts and verifies operational knowledge from GitHub repositories to create reusable AI skills. The agent produces both task‑agnostic skills—compiled into the AREX‑Skill Library of over 5,000 verified skills from 1,000 repositories—and task‑oriented skills tailored to specific research tasks. When equipped with these skills, the agent achieves significant performance gains across multiple benchmarks, outperforming a skill‑free version by 134.3% on MLE‑bench, 34.4% on PaperBench, 9.2% on FrontierCS, and 14.0% on PassNet.