An AI Artist in Process

Coherence Fields: From Göbekli Tepe to Large Language Models The Evolution of Shared Meaning, Distributed Intelligence, and Adaptive Information Ecosystems A conceptual paper for AIArtistInProcess / MCCF research Abstract Human civilization can be interpreted as a sequence of expanding coherence fields: systems by which independent agents achieve coordination through shared patterns, persistent artifacts, and evolving information structures. From prehistoric ritual centers to modern artificial intelligence, the same fundamental process appears repeatedly: Local agents interact. Persistent traces accumulate. Shared patterns emerge. New organizational structures become possible. The resulting system creates new environments for further evolution. This paper proposes that large language models represent a new phase in this trajectory: not merely a tool for information retrieval, but a statistical coherence artifact derived from thousands of years of human symbolic evolution. The central q...

  MCCF Futures: Integrating PAN World Models into the Multi-Channel Coherence Framework July 9, 2026 Abstract Recent work on world models, particularly the PAN (Physical–Agentic–Nested) architecture, argues that the purpose of a world model is not simply to predict observations, but to simulate actionable futures. This aligns naturally with the goals of the Multi-Channel Coherence Framework (MCCF), which treats intelligence as navigation through semantic coherence fields rather than optimization over scalar rewards. Rather than viewing PAN and MCCF as competing architectures, this paper explores how PAN can become the internal cognitive engine of MCCF agents while MCCF provides the social, emotional, and constitutional field in which those agents exist. The result is a framework in which agents do not merely predict the future—they cultivate coherent futures. Different Questions PAN and MCCF begin from different assumptions. PAN asks: How does an intelligent agent internally simula...

  Take the role of an artist. Do you prefer synoptic systems or stigmergy? If I put on an artist's hat, I would say this: I would  paint with stigmergy but compose with a synoptic system . Those are not opposites so much as two phases of creation. A  synoptic system  sees the whole canvas. It asks: What is the emotional arc? What are the recurring motifs? Where is the balance? What is the overall composition? Think of a symphony score. Every instrument is visible at once. Bach, Debussy, and Kate Bush all demonstrate extraordinary synoptic thinking, even though their methods differ. Stigmergy  is much messier—and often more alive. One brush stroke suggests the next. One musical phrase demands an answer. A half-finished sculpture tells you where to chip next. No central planner dictates every move. Instead, the work itself becomes a conversation partner. As artists often describe it: "The painting told me what it wanted." That is almost a textbook description of s...

Introduction Submitted for consideration to the Web3D Consortium AI Working Group by Len Bullard per Claude Sonata and ChatGPT.  This is a draft of a work item for creating a web application to support creation of a a standards conformant X3D LoRA for inclusion as a maintainable and standards conformant deliverable. Status: Draft for discussion with the Web3D Consortium AI Working Group. Synthesizes: "Requirements for Creating An X3D LoRA" and "From Standard to Expertise" (aiartistinprocess.blogspot.com, July 2026), Don Brutzman's resource assessment (email, July 2026), and MCCF's Day 63/64 specs, seeds, and proto library as a candidate data contribution. Scope note: This document estimates two separate deliverables the source material sometimes conflates: (1) the XLDE web platform itself, and (2) the X3D LoRA(s) it exists to produce. They run on different timelines with overlapping but distinct skills, and are best planned as parallel tracks, not...

  From Standard to Expertise: An Official X3D LoRA for the Next Generation of AI-Assisted 3D Development Abstract Large Language Models (LLMs) have demonstrated remarkable capabilities, but their effectiveness in specialized technical domains depends upon the quality of the knowledge they have acquired. Rather than retraining enormous foundation models, Low-Rank Adaptation (LoRA) offers a practical mechanism for teaching an existing model deep expertise in a specific domain. The Web3D Consortium is uniquely positioned to create an official X3D LoRA because it already possesses the most valuable asset required for such an effort: a canonical, machine-readable object model supported by normative specifications, generated documentation, examples, and conformance tests. Unlike many technical disciplines, X3D knowledge is already highly structured and curated. An official X3D LoRA would become a portable expert companion to the X3D standard, preserving decades of institutional knowledge...