arXiv:2606. 27960v1 Announce Type: cross Abstract: Software engineering is an intellectually demanding, creative discipline that juggles a web of interdependent tasks to design, build, and assure the quality of increasingly complex systems.
arXiv:2608. 02638v1 Announce Type: cross Abstract: Artificial Intelligence (AI) components are increasingly pervasive in several software systems, including Cyber-Physical Systems (CPSs).
arXiv:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.
arXiv:2602. 07666v4 Announce Type: replace-cross Abstract: DARPA's AI Cyber Challenge (AIxCC, 2023--2025) is the largest competition to date for building fully autonomous cyber reasoning systems (CRSs) that leverage recent advances in AI -- particularly large language models (LLMs) -- to discover and remediate vulnerabilities in real-world open-source software.
Building operations are energy-inefficient. Artificial Intelligence (AI)-driven control systems promise benefits through optimization and predictive control, but deploying them in real buildings revea...
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.
arXiv:2606. 05770v1 Announce Type: cross Abstract: AI is changing how software engineers work, but it often comes with hidden burdens and costs.
arXiv:2608. 16237v1 Announce Type: cross Abstract: Building operations are energy-inefficient.
arXiv:2607. 16845v1 Announce Type: new Abstract: Scientists at European XFEL conduct experiments that generate very large and complex datasets.
The paper "Neuro-symbolic AI for Industrial Configuration" discusses how Large Language Models (LLMs) fall short for industrial product configuration due to their probabilistic nature, which conflicts with the need for syntactically valid, semantically consistent outputs that align with extensive feature and rule knowledge bases. It proposes Neuro-symbolic (NeSy) AI as a promising solution, outlining three integration strategies—hybrid inference, hybrid fine‑tuning, and hybrid training—and presents a taxonomy of these approaches. The authors describe their efforts to implement a NeSy-based configuration copilot, derive practical design choices for trustworthy AI deployment in engineering settings, and highlight key research challenges, especially scaling NeSy methods from academic prototypes to full‑scale industrial configurators.
arXiv:2411. 18714v3 Announce Type: replace-cross Abstract: Self-driving cars increasingly rely on deep neural networks to achieve human-like driving.
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.
arXiv:2607. 10878v1 Announce Type: new Abstract: AI agents are evolving from answer engines into persistent teams that use tools, delegate work, learn from experience, and modify the artifacts that shape their future behavior.