Recursive Identity: A Structural Theory of Processual Continuity (German Edition)

This theoretical framework develops a structural model of recursive identity and outlines the minimal conditions under which an information process can maintain continuity across temporal, contextual, and systemic change. Identity is conceptualized not as a substance or inherent property, but as an emergent, self‑referential process generated through the coordinated interaction of three fundamental operators: Integration, Coherence, and Recursion. The model formalizes these operators, describes their cyclic application, and demonstrates how stable identity patterns arise as fixpoint dynamics within a structured state space. The framework is substrate‑agnostic in a formal sense: it operates on states and transformations without assuming any specific biological, technical, or symbolic implementation. It situates itself within system theory, process philosophy, and information‑based approaches, offering a minimal but sufficient grammar for analyzing dynamic identity processes. The paper concludes with methodological implications, theoretical extensions, and a glossary of core concepts. This publication represents the original German version. An expanded international (English) edition is currently in preparation and will be released separately. Author Description: J. A. Jones is an independent systems analyst and structural methodologist specializing in meta‑structural analysis, process architectures, and the formal modeling of dynamic systems. Their work focuses on developing minimal, substrate‑agnostic theoretical frameworks that clarify the structural conditions underlying emergent and self‑referential processes. Jones operates outside institutional academia, combining conceptual rigor with methodological independence to formulate models designed for long‑term theoretical relevance and interdisciplinary applicability.

Circlesquare: juggling DOIs when preprinting – quantixed

Toward Hybrid Architectures: Functional AI and the Limits of Silicon Substrates: An ontological and dynamical framework for advanced artificial cognition

This research position paper develops an ontological and dynamical framework for understanding the limits of silicon‑based artificial intelligence and the material conditions required for genuine emergent cognition. Contemporary AI systems exhibit remarkable functional capabilities, yet their digital substrates lack the continuous, energetically grounded, and self‑organizing dynamics necessary for stabilizing inner states, multiscale feedback, and coherent internal trajectories. The paper argues that consciousness‑relevant emergence is a material phenomenon that cannot be simulated or instantiated within discrete computational architectures. It identifies the systemic thresholds—nonlinear coupling, metastability, energetic grounding, and multiscale integration—that biological systems satisfy and digital systems cannot. Building on these principles, the paper proposes hybrid cognitive architectures in which functional AI is coupled with dynamically rich substrates such as neuronal organoids, biohybrid systems, organic memristive materials, or other continuous, energy‑driven media. These substrates provide the physical conditions for coherence, continuity, and self‑organization, while silicon‑based components supply structure, task‑level organization, and symbolic processing. The work outlines the implications of this paradigm for AI research, cognitive science, ethics, and human–AI interaction. It clarifies the distinction between simulation and instantiation, addresses common counterarguments, and positions the model within existing theoretical frameworks without reducing it to any of them. The paper concludes by identifying the material and systemic thresholds required for true emergence in future hybrid human–AI systems. Authors's Note This paper is a structural argument rather than an empirical study. It synthesizes insights from systems theory, neuroscience, materials science, and philosophy of mind to clarify the material conditions under which consciousness can, in principle, arise. Its aim is not to predict specific technologies or make metaphysical claims, but to delineate the architectural boundaries that current digital systems cannot cross and to outline the substrate‑level requirements for future emergent cognition.

Cannot create new version of Zenodo record with pre-existing, non-Zenodo, DOI · Issue #2536 · zenodo/zenodo | Aleksandra Lazić, PhD

Learned a #Zenodo versioning lesson the hard way, so sharing in case it saves someone else time. 🚨 Zenodo doesn't support versioning with non-Zenodo DOIs. I had entered an existing OSF (Open Science Framework) DOI for a Zenodo record. A year later, when we wanted to create a new version to add materials, the "New version" button was unavailable and we got a message saying we didn't have permission to do so. So, if you want proper versioning on Zenodo, you need to let Zenodo mint the DOI. If it's too late for that, upload a new record and link it to the previous one using a note or the related identifiers field. Found the explanation here: https://lnkd.in/dRPBrR6p #DOI #DigitalObjectIdentifier #OpenAccess #OpenScience #OpenData

PERFECT PANGRAM HASH : Anagram Hash Function

A pangram is a sentence or phrase that contains each letter of an alphabetor character set at least once. A perfect pangram is an anagram of thealphabet which contains each letter exactly once. Pangram hash generates a perfect pangram hash digest consisting of aanagram permutation of a character set. Each character in the output isunique and non-repeating. Below is purely procedural source code written in the FreePascalprogramming language. Instructions and explanatory comments are locatedin the source code. A plain text source file is embedded in the PDFdocument. Additional resource files are also embedded in the PDF file.