arXiv:2608. 20201v1 Announce Type: new Abstract: Software form has undergone two paradigm shifts since its inception: Software 1.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
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:2606. 05608v2 Announce Type: replace-cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
arXiv:2606. 00288v1 Announce Type: new Abstract: Large language models are undergoing a transition from model technology to system technology.
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.
arXiv:2606. 05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
arXiv:2603. 14147v2 Announce Type: replace Abstract: The generative artificial intelligence (AI) ecosystem is undergoing rapid transformations that threaten its sustainability.