Advances in Art: Orthogonal Disruption and the Beauty in Schematics

• URL: http://arxiv.org/abs/2604.20865v1
• Date: Wed, 25 Mar 2026 09:40:46 GMT
• Title: Advances in Art: Orthogonal Disruption and the Beauty in Schematics
• Abstract summary: This paper introduces Orthogonal Art, a proposed artistic discipline that emerges in dialectical response to artificial intelligence.<n>Unlike AI-augmented creative practices, Orthogonal Art is structurally defined by occupying the generative and conceptual spaces that current AI systems cannot access.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces Orthogonal Art, a proposed artistic discipline that emerges in dialectical response to artificial intelligence rather than in service of it. Unlike AI-augmented creative practices, Orthogonal Art is structurally defined by occupying the generative and conceptual spaces that current AI systems cannot access. As a founding instantiation of this framework, the paper presents a novel artistic practice in which technical schematics serve as the primary medium. A significant secondary contribution is the pedagogical dimension of the work: by grounding artistic practice in schematic logic and algorithmic structure, the framework provides an accessible entry point into the advanced field of Augmented Machines systems, enabling cross-disciplinary literacy within Humanities at the intersection of art, engineering, and philosophy.

Related papers

• Supporting Belonging in Software Engineering Through Role Models Exposure [2.918940961856197]
  This paper presents an analytic account of integrating historically grounded role models into routine software engineering teaching practice.<n>We characterize how brief, topic aligned contextualizations of pioneers were incorporated into core technical lectures without altering learning objectives or assessments.<n>Results suggest that embedding historically grounded representation within technical content may serve as a practical mechanism for supporting inclusivity while preserving technical rigor in engineering education.
  arXiv  Detail & Related papers  (2026-04-28T01:10:25Z)
• Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style [60.562013130566726]
  We characterize the mechanisms underlying VLMs' ability to predict artistic style.<n>We employ a latent-space decomposition approach to identify concepts that drive art style prediction.<n>In cases where an irrelevant concept was used to successfully predict style, art historians identified possible reasons for its success.
  arXiv  Detail & Related papers  (2026-03-11T17:49:45Z)
• Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment [51.40989269202702]
  aesthetic quality assessment task is crucial for developing a human-aligned quantitative evaluation system for AIGC.<n>We propose ArtQuant, an aesthetics assessment framework for artistic images which couples isolated aesthetic dimensions through description generation.<n>Our approach achieves epoch state-of-the-art performance on several datasets while requiring only 33% of conventional trainings.
  arXiv  Detail & Related papers  (2025-12-29T12:18:26Z)
• Artism: AI-Driven Dual-Engine System for Art Generation and Critique [7.209828820174739]
  We present two interconnected components: AIDA (an artificial artist social network) and the Ismism Machine, a system for critical analysis.<n>The framework explores a shift from traditional unidirectional critique toward an intelligent, interactive mode of reflexive practice.
  arXiv  Detail & Related papers  (2025-12-17T18:58:42Z)
• Compose Your Aesthetics: Empowering Text-to-Image Models with the Principles of Art [61.28133495240179]
  We propose a novel task of aesthetics alignment which seeks to align user-specified aesthetics with the T2I generation output.<n>Inspired by how artworks provide an invaluable perspective to approach aesthetics, we codify visual aesthetics using the compositional framework artists employ.<n>We demonstrate that T2I DMs can effectively offer 10 compositional controls through user-specified PoA conditions.
  arXiv  Detail & Related papers  (2025-03-15T06:58:09Z)
• Diffusion-Based Visual Art Creation: A Survey and New Perspectives [51.522935314070416]
  This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation. We aim to shed light on the mechanisms through which AI systems emulate and possibly, enhance human capacities in artistic perception and creativity.
  arXiv  Detail & Related papers  (2024-08-22T04:49:50Z)
• AI-Generated Imagery: A New Era for the `Readymade' [0.7386189738262202]
  This paper aims to examine how digital images produced by generative AI systems have come to be so regularly referred to as art. We employ existing philosophical frameworks and theories of language to suggest that some AI-generated imagery can be presented as readymades' for consideration as art.
  arXiv  Detail & Related papers  (2023-07-12T09:25:56Z)
• Pathway to Future Symbiotic Creativity [76.20798455931603]
  We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist to a Machine artist in its own right. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. We propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle.
  arXiv  Detail & Related papers  (2022-08-18T15:12:02Z)
• Formalising Concepts as Grounded Abstractions [68.24080871981869]
  This report shows how representation learning can be used to induce concepts from raw data. The main technical goal of this report is to show how techniques from representation learning can be married with a lattice-theoretic formulation of conceptual spaces.
  arXiv  Detail & Related papers  (2021-01-13T15:22:01Z)
• Defining Computer Art: Methods, Themes, and the Aesthetic Problematic [0.0]
  The application of computer technology in the field of art has given rise to novel modes of artistic practice, including media art. There is a triad of themes: the relationship between art and technology, the problem of machine creation, and the ontology of art. The anticipation that computer art presents and responds to the above-mentioned aesthetic problematic suggests the plausibility of computer art being a legitimate category of art.
  arXiv  Detail & Related papers  (2020-10-10T07:28:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.