arXiv AI

Constitutive Priors for Machine Intelligence: A Legitimacy Theory of the Artificial Physical World

arXiv:2608. 15147v1 Announce Type: new Abstract: Machine intelligence has conquered the symbolic world but stalled at the physical one.

arXiv AI
Sep 15

Semantic Knowledge Technologies: what the Semantic Web lost sight of, and what it never had

The paper critiques the Semantic Web’s failure to deliver machine‑interpretable knowledge, arguing that its standards omitted key elements—conditions for claims, operational grounding, and coverage scope—making truth, applicability, and boundary recognition impossible. It proposes a new framework, Semantic Knowledge Technologies, with a seven‑layer architecture and five measurable tests of understanding (check, connect, derive, act, delimit). The authors introduce concepts such as Large Knowledge Models, SLKMs, and a falsifiable definition of Semantic Artificial General Intelligence, presenting a research agenda to address these gaps.

By Achille Zappa
arXiv AI
Aug 25

Walking on the DARKSIDE

arXiv:2608.23370v1 Announce Type: new Abstract: Large Language Models (LLMs) recognise patterns but do not natively track the path of exclusions that a coherent discourse demands. When an input rests...

By Aldo Gangemi, Emanuele Bottazzi
arXiv AI
Sep 16

Models as Governed Interfaces for AI-Native MBSE: Read-Side Adequacy and Write-Side Admissibility

The paper argues that while machine-readable models like SysML v2 enable AI participation in systems engineering, the data architecture surrounding these models must also be robust. It introduces the concept of epistemic adequacy, split into read‑side adequacy (ensuring derivations, status, and provenance are answerable) and write‑side admissibility (filtering AI contributions before they enter the record). The authors illustrate their ideas using the public Apollo 11 SysML v2 reconstruction and propose a Governed‑Query Architecture Framework to enforce these principles.

By Jason Gower, Michael J. de C. Henshaw, Siyuan Ji
arXiv AI
Sep 24

A hierarchy of faithfulness criteria for knowledge base completion

The paper introduces a hierarchy of four increasingly strict faithfulness criteria—discrimination, logical admissibility, monotonic logical faithfulness, and probabilistic logical faithfulness—for evaluating knowledge base completion models, particularly when the target is a description logic knowledge base. It demonstrates that ranking accuracy alone does not guarantee logical faithfulness and shows that current embedding models fail to satisfy any of the criteria across the hierarchy. The authors provide a formal grounding for the strongest criterion using relative model counts and evaluate several models on εL ontologies, revealing gaps between performance metrics and logical correctness.

By Olga Mashkova, Robert Hoehndorf