arXiv:2603.26245v1 Announce Type: cross
Abstract: In a previous work (Elaarabi et al., 2025b), the Sequence Encoder for online dynamical system identification (Elaarabi et al., 2025a) and its combination with PINN (PINN-SE) were introduced and tested …
arXiv:2603.26244v1 Announce Type: cross
Abstract: Domain-driven design (DDD) is a powerful design technique for architecting complex software systems. This paper introduces a prompting framework that automates core DDD activities through structured la…
arXiv:2406.06751v3 Announce Type: replace-cross
Abstract: We propose a novel deep symbolic regression approach to enhance the robustness and interpretability of data-driven mathematical expression discovery. Our work is aligned with the popular DSR fr…
arXiv:2603.26221v1 Announce Type: cross
Abstract: Open agentic systems combine LLM-based planning with external capabilities, persistent memory, and privileged execution. They are used in coding assistants, browser copilots, and enterprise automation….
arXiv:2402.07153v3 Announce Type: replace-cross
Abstract: This paper provides rigorous error bounds for physics-informed neural networks approximating the semilinear wave equation. We provide bounds for the generalization and training error in terms o…
arXiv:2603.23610v3 Announce Type: replace
Abstract: Although large language models (LLMs) have advanced rapidly, robust automation of complex software workflows remains an open problem. In long-horizon settings, agents frequently suffer from cascading…
arXiv:2603.26203v1 Announce Type: cross
Abstract: Technology change happens quickly such that new trends tend to crowd out the focus on what was new just yesterday. In this paper the peak popularity of the confluence of Object Technologies with early …
arXiv:2603.15636v2 Announce Type: replace
Abstract: As AI-driven document understanding and processing tools become increasingly prevalent in real-world applications, the need for rigorous evaluation standards has grown increasingly urgent. Existing b…
arXiv:2601.08323v3 Announce Type: replace
Abstract: Equipping agents with memory is essential for solving real-world long-horizon problems. However, most existing agent memory mechanisms rely on static and hand-crafted workflows. This limits the perfo…
arXiv:2511.19669v2 Announce Type: replace
Abstract: Conventional AI-driven AMS design automation algorithms remain constrained by their reliance on high-quality datasets to capture underlying circuit behavior, coupled with poor transferability across …