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:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.
arXiv:2610.01519v1 Announce Type: cross Abstract: Neuro-Symbolic (NeSy) predictors incorporate prior knowledge into the prediction process of neural networks, ensuring that outputs satisfy specified...
The paper introduces GEARS, a framework that treats ranking optimization as an autonomous discovery process within a programmable experimentation environment. By encapsulating ranking expert knowledge into reusable agent skills, GEARS allows operators to steer systems through high-level intent personalization rather than static model selection. The framework also includes validation hooks to enforce statistical robustness and filter out brittle policies, and experimental results show that GEARS consistently finds near‑Pareto‑efficient policies while maintaining deployment stability.
arXiv:2508.15757v2 Announce Type: replace Abstract: Configuration optimization remains a critical bottleneck in machine learning, requiring coordinated tuning across model architecture, training stra...
arXiv:2510. 14538v3 Announce Type: replace Abstract: Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.
arXiv:2605.18202v2 Announce Type: replace-cross Abstract: Neuro-Symbolic Concept-based Models (NeSy-CBMs) are a family of architectures that integrate neural networks with symbolic reasoning for enha...
The paper introduces little m, an AI agent that helps formulate industrial process control models by combining a domain-specific knowledge repository with LLM-driven interaction. It tackles the challenge of converting messy real-world specifications, including natural language and spatial diagrams, into rigorous mathematical optimization models. The authors also present IPC-Bench, a multimodal dataset of 50 canonical scenarios, and show through automated and human evaluations that little m outperforms state‑of‑the‑art LLMs in generating semantically correct models.
arXiv:2608. 12325v1 Announce Type: new Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today.
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.
SysML v2's textual syntax enables compiler-based validation of model structure and language conformance. However, semantic mistakes that preserve syntactic validity but violate domain rules cannot be detected through compilers.
arXiv:2606. 17962v1 Announce Type: cross Abstract: Reasoning about what agents can achieve through strategic interaction is a core challenge in Multi-Agent Systems (MAS).
arXiv:2606. 00189v1 Announce Type: cross Abstract: Automated design and optimization of agentic LLM-based systems leads to sophisticated systems that substantially improve result quality over off-the-shelf agentic patterns.