Toward a Process-Oriented Architecture of Situated Meaning 1. Introduction The Dynamic Quadranym Model (DQM) project addresses one of the central impasses in artificial intelligence research: the semantic wall—the point at which symbolic or statistical models can manipulate language but cannot inhabit its meaning. While current … Continue reading From Concrescence to Coherence: Whitehead through the Semantic Core (DQM)
Orientation precedes meaning. PD (novelty) presses; ND (coherence) holds. A gate condition—ND@S≥PD@O—governs when arcs admit potential, close, perish, and egress as superjective lineage. DQM instruments Whitehead’s creativity/concrescence/satisfaction/superject so we can diagnose, preserve, and reuse meaning as managed illumination.
The Unknowable Engine: Blind Spots as Foundational Features of Situated Cognition argues that intelligence does not rest on perfect knowledge but on structural limitations that guarantee perpetual motion. The Dynamic Quadranym Model (DQM) identifies two blind spots of orientation: the epistemological blind spot, rooted in the opacity of perception, and the procedural blind spot, rooted in the opacity of action. These blind spots are not errors but essential design features: they ensure that coherence is never final and that reorientation is always necessary. Distinguishing them from perturbations—situational disruptions that launch new arcs—the essay shows how blind spots are permanent constraints that make process thinking indispensable. Intelligence, in this view, is not the elimination of uncertainty but the art of navigating it, turning the opacity of being into an engine of change.
Orientation Beyond Language and Music: Introducing the DQM’s Semantic Core” explores how the Dynamic Quadranym Model (DQM) offers a groundbreaking framework for understanding meaning not as fixed content, but as emergent coherence shaped by embodied orientation across systems. Drawing from evolutionary musicology, cognitive science, and AI, the article traces how thinkers like Gary Tomlinson and Elan Barenholtz set the stage for the DQM’s central innovation: the Semantic Core—a procedural engine that tracks, relates, and resolves tensions across linguistic, perceptual, and motor systems. With detailed examples, including the “door” quadranym, and a Q&A that addresses the limitations of traditional models, this piece provides both a conceptual foundation and a practical lens for rethinking orientation, coherence, and intelligent behavior—human or artificial.
“That we cannot understand—or even perceive—anything outside the bounds of our existing language or categories.” This simple yet profound insight reveals one of the most persistent limitations in how we think, speak, and relate. Much of our cognitive process occurs beneath conscious awareness—invisible, unattended, and … Continue reading Orientation Before Understanding: Rethinking Language, Meaning, and the DQM
The DQM emphasizes that meaning evolves through process. It is not static but shifts in response to context, interaction, and orientation. This evolution is where the model focuses—not on meaning as a fixed entity but on the orientation that enables it to change.
The Dynamic Quadranym Model (DQM): Concise Breakdown Lead-In The DQM orients to meaning by dynamically adjusting and indexing words along continua (e.g., potential → actual). Using quadranyms as conduits and terminals across layers, the system shifts meaning through spatial modes and temporal states. Like an … Continue reading DQM: How it works.
Introduction: The Dynamic Quadranym Model (DQM) provides a semantic framework for analyzing situational and orientational dynamics. It captures transitions through Hyper Q layers and resolves contextual arcs within Q Units. Bifurcation and conflation serve as core mechanisms, adjusting expansive (potential) and reductive (actual) measures to … Continue reading DQM: Framework Overview
The DQM orientation grammar and scaffolding approach to situated semantics in AI offers a highly effective and elegant framework for understanding how meaning and context can be navigated dynamically. It’s an semantic approach that reflects how humans process the world: dynamically, flexibly, and with a focus on the big picture while attending to immediate details. The overlap between various semantic categories with sensory motor areas suggests that a common mechanism is used by neurons to process action, perception and semantics. Body & environment play important roles in thinking
By distinguishing between the dynamic context (external, situational) and the dynamical context (internal, orientation-driven), orientation grammar captures the interplay between meaning and process. This distinction ensures a coherent framework for analyzing how orientation aligns with potential while responding to situational goals and movements. Through this lens, narratives like Jan’s dive reveal how orientation and situation dynamically coalesce to create meaning.
Imagine a race car speeding down the track, its engine roaring as it navigates sharp turns and accelerates on straightaways. The race car—sleek, agile, and immensely powerful—represents the cutting-edge Large Language Model (LLM), while the carefully engineered track, with its twists, turns, and guardrails, symbolizes the Classic AI (CAI) system that provides the structure and direction of the Dynamic Quadranym Model (DQM). Together, they form a hybrid system that blends raw computational power with refined, rule-based guidance, creating a dynamic interplay that ensures both speed and stability.
The DQM targets understanding as an evolving process, coupling the agent’s orientation with the situation to produce dynamic-semantic-responses. By organizing semantic elements into quadranyms—four-faceted structures—the DQM bridges contextual analysis with adaptive reasoning. It integrates foundational concepts with AI systems such as Large Language Models (LLMs) and Classic AI (CAI).
The Quadranym Model uses quadranyms as building blocks to establish semantic orientation at every instance and level of its system. At first, it might seem like there are too many blocks to manage, but the key is understanding how they naturally stack and fit together.
Introduction The Dynamic Quadranym Model (DQM) is a framework for analyzing and generating meaning dynamically. It bridges contextual analysis and adaptive reasoning by organizing semantic elements into quadranyms—four-faceted structures that guide understanding across layers. This guide will introduce the model step-by-step, from foundational concepts to … Continue reading DQM Summary
Summary of original article: The Dynamic Quadranym Model (DQM): Integrating Semantic Structure and Responsiveness for a Situating AI Introduction Language is alive, shifting with context and intention. Yet, AI often treats meaning as static—a pattern to retrieve rather than a process to adapt. Imagine a … Continue reading DQM Summary: Integrating Semantic Structure and Responsiveness for a Situated AI
Imagine sitting on your couch, remote control in hand. You press a button to change the channel, adjust the volume, or browse streaming options. The remote, a simple and familiar tool, embodies an intricate interplay of meaning and action. It is proximal—immediate and tangible—while the … Continue reading From Remote to Rhythm: The Dynamic Symphony of Meaning-Making
Meaning is the coupling between the orientation and the situation.
Introduction In cognitive science, Daniel Kahneman and Amos Tversky revolutionized our understanding of human decision-making with their dual-system framework—System 1 (fast, intuitive) and System 2 (slow, analytical). This dual-system model has become a cornerstone in both psychology and artificial intelligence (AI), offering a structured way … Continue reading From System 1 & 2 to Adaptive Intelligence: Extending Kahneman and Tversky’s Insights
Introduction: The Child and the Doll Imagination is often simple to observe in daily life. Think of a child playing with a doll. There’s no deep or mysterious process happening—the child knows the doll isn’t real, but they still interact with it as though it … Continue reading How Orientation Supports Imagination
Lead-in Today, AI excels at generating content through effective but fundamentally static methods. It relies on vast data and learned patterns to produce responses that suit many contexts. But imagine a new way of thinking about meaning—one that moves beyond rigid definitions to offer a … Continue reading The Dynamic Quadranym Model (DQM): Integrating Semantic Structure and Responsiveness for a Situating AI
Build Intuit 