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...
By Solim LeGris, Philipp Mondorf, Jinran Jin, Akshay K. Jagadish, Brenden M. Lake, Todd M. Gureckis
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.
By Nicolas Gertler, Claudio Novelli, Luciano Floridi
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.
By Stefania Dumbrava, Efthymia Tsamoura
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.
By Danilo Valerio, Philipp Kogler, Stefan Bischof, Thomas Hubauer, Huzefa Rangwala
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.
By Gadi Evron
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.
By Xiangbo Gao, Xiukun Huang, Boyu Lu, Junge Zhang, Mengjie Mao, Jiachen Li, Wei Xiong, Zhengzhong Tu