The article "Hybrid AI Patterns: Thirteen Ways to Couple Language Models with Rules, and How to Choose Among Them" presents a catalogue of thirteen design patterns for integrating language models with rule-based systems. It details each pattern’s intent, motivation, applicability, participants, collaborations, consequences, implementation, known uses, and related patterns, and identifies five invariants and five failure patterns that apply to all combinations. A decision guide is provided to help select an appropriate pattern based on problem context, supported by measurements of performance and design costs.
arXiv:2611.07088v1 Announce Type: new Abstract: Symbolic models offer a powerful account of cognition, but their scope has historically been limited by the need for hand-engineering and computational...
The paper proposes a portable specification framework for the infrastructure—called the harness—that surrounds large language models. It decomposes the harness into eight functional components, enabling consistent specification and comparison across systems. The authors argue that recording which model ran in which harness is essential because different configurations can drastically alter performance and expose varying attack surfaces, and current model or agent cards do not capture this sequence of effective configurations.
The article surveys rule‑based languages—Datalog, answer set, and probabilistic logic programs—used as the symbolic component of neurosymbolic AI systems. It examines these languages across semantics, expressiveness, neural integration, and evaluation mechanisms, and analyzes over 50 recent systems in databases, machine learning, vision, and robotics. A decision matrix links application scenarios to required features, and the paper concludes with open research problems.
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.
The paper introduces the concept of an "adversary brain," an LLM-based model that represents a threat actor’s behavior as an evolving logical signature. These signatures function as continuous detection mechanisms, matching activity patterns of campaigns rather than single indicators, and are built using the Analysis of Competing Hypotheses framework extended to thousands of hypotheses. Language models enable the creation, evaluation, and updating of these brains, allowing them to identify new indicators such as fresh command‑and‑control domains and transform unknown threats into known ones.
arXiv:2606. 23938v1 Announce Type: new Abstract: Driving VLA models incorporating Chain-of-Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose intermediate decisions in natural language, yet current rationales often lack the step-by-step decision semantics needed to keep the rationale causally connected to the planned motion.
The paper introduces Knowledge Cards, a new structured artefact designed to capture validated knowledge about specific concepts that AI systems use to make decisions. Unlike existing model, data, and system cards, Knowledge Cards focus on the layer between inputs and outputs, documenting entities, relationships, reasoning patterns, conditions for validity, and provenance, all grounded in a formal domain ontology and signed off by a domain expert. Prototype cards have been created in the energy and pharmaceutical domains, and the schema is released as a public draft for community engagement.
arXiv:2606. 13405v1 Announce Type: new Abstract: LLM-based agents are entering regulated industries where they automate judgment intensive quality management processes.
arXiv:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.
arXiv:2605. 22093v3 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle.
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:2607. 17948v1 Announce Type: new Abstract: Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents.