From Concrescence to Coherence: Whitehead through the Semantic Core (DQM)

Written by DS
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 systems excel at syntactic prediction and semantic correlation, they lack a model of situated coherence—the capacity to sustain orientation across shifting contexts, as humans do through embodied sense and aesthetic grounding. The goal of this work is to formalize that missing layer by integrating process philosophy and orientation dynamics into a computational framework—providing a virtual sensibility for otherwise static representation. By tracing the granularity descent from logic (truth-based systems) through semantics (fit-based systems) to aesthetics (coherence-based systems), DQM reframes cognition as a dynamic balancing of potentials and actuals rather than a static manipulation of symbols. The following section, From Concrescence to Coherence, situates this approach in relation to Alfred North Whitehead’s process metaphysics and shows how the Semantic Core (SC) operationalizes his concept of concrescence—the becoming of coherence through aesthetic unity—as a viable semantic architecture for situated, general intelligence.

2. DQM and the Semantic Core

The Dynamic Quadranym Model (DQM) begins with a simple premise: meaning is not given by static symbols or truth conditions but by the system’s capacity to maintain coherence across changing situations. In this respect, DQM aligns with Whitehead’s account of experience as a continuous process of becoming in which patterns of relevance, contrast, and valuation are progressively unified into a coherent moment. Rather than treating cognition as the manipulation of propositions, DQM treats it as the regulation of orientation. Orientation is the system’s lived alignment with a situation—the ongoing tension between what is inherited and what is encountered.

This view reframes the traditional hierarchy of cognitive granularity. Logical systems operate at the level of explicit propositions and truth values. Semantic systems operate at the level of conceptual relations and structural fit. Beneath both lies an aesthetic layer in Whitehead’s technical sense: a layer of coherence, intensity, and pattern that makes higher-order meaning possible. DQM’s Semantic Core occupies this foundational region. It models how coherence is achieved when a system encounters novelty, how expectations adjust when situations shift, and how moments of alignment accumulate into a lineage of understanding. In this way, DQM treats orientation as the functional center of meaning and provides a formal structure for processual sense-making.

3. Concrescence and Orientation Arcs

Whitehead describes an actual occasion as a process of concrescence: a becoming in which inherited data, conceptual potentials, and novel contrasts are integrated into a single coherent satisfaction. This process unfolds in phases. The occasion begins by taking in relevant aspects of its world through physical and conceptual prehensions. It then integrates these prehensions with its own subjective form, balancing novelty against inherited pattern. Finally, it reaches satisfaction—a determinate unity that becomes datum for subsequent occasions.

The Dynamic Quadranym Model expresses this same rhythm in the form of an orientation arc. An arc represents the smallest unit of sense-making: a transition from an initial orientation to a new, stabilized coherence. In formal terms, an arc is written as:

[Y(a) → X(b)]

The left side, Y(a), represents the system’s current orientation—its inherited stance or subjective starting point. The right side, X(b), represents an objective potential emerging from the situation. The arrow captures the process through which the system tests, modifies, and ultimately integrates that potential. When the potential coheres well enough with the starting point, the arc closes in a satisfaction. This satisfaction then becomes the next inherited orientation, just as each actual occasion contributes its achieved form to the future. In this way, the orientation arc serves as a computational analogue of concrescence: a procedural mechanism through which many pressures are unified into a single coherent becoming.

4. Quadranym Units as a Formal Process Calculus

To express concrescence in a computationally tractable form, the Dynamic Quadranym Model uses a structure called the quadranym unit. A quadranym unit encodes the minimal constituents of an orientation arc: modes, states, tokens, and their directional alignment. Each unit takes the form of a nested expression in which a general state serves as a structural placeholder and a concrete token serves as the inherited datum. The notation appears as:

[(state(token)) → (state(token))]

The outer brackets mark the unit as a self-contained process of becoming. The states inside each parenthesis represent abstract roles such as self, goal, present, event, motion, or matter. These roles serve as the invariant form of the occasion, analogous to the function of eternal objects in Whitehead’s metaphysics. The tokens nested within them—contextual elements drawn from a situation—represent the concrete data inherited at that phase.

The directional transition from the left state to the right state encodes the subjective aim: the internal tendency by which an orientation moves from an initial phase toward a satisfaction. In this sense, the quadranym unit becomes a micro-concrescence. It captures the subjective pole on the left, the objective pole on the right, and the route of determination between them. By using the same formal grammar for each unit, the model achieves structural invariance across scales. Individual units can combine into larger networks without changing their internal form, allowing nested patterns of becoming to propagate coherence throughout higher-level structures.

5. ND/PD Heuristics and the Coherence Gate

For an orientation arc to complete its transition from an initial phase to a coherent satisfaction, it must preserve an internal balance between what the system inherits and what the situation introduces. The Dynamic Quadranym Model formalizes this balance using two complementary measures: Negative Displacement (ND) and Positive Displacement (PD). ND represents the system’s dynamical context—its inherited coherence, internal momentum, and structural tendencies that carry forward from prior satisfactions. PD represents the situational context—the novelty, contrast, and external pressures introduced by the current environment.

These two measures determine whether a potential can be integrated without disrupting coherence. The model applies a simple gating rule:

ND ≥ PD + τ

where τ is a minimal threshold representing the amount of tension the system can absorb before an orientation must reorganize. When ND meets or exceeds this boundary, the potential on the right side of the arc can be coherently integrated with the inherited state on the left. The arc then closes, and the resulting satisfaction becomes part of the system’s lineage. When ND falls short, the system must adjust its trajectory, either by modifying the potential, shifting its orientation, or initiating a new concrescent route.

This heuristic mirrors Whitehead’s account of concrescence as a balance between inheritance and novelty. Just as each actual occasion must integrate the data it receives with its subjective form, each quadranym unit must reconcile situational pressures with internal coherence. ND/PD heuristics operationalize this balance by regulating how much novelty can be admitted without compromising identity and by ensuring that coherence propagates not only within a single arc but across the multiple parallel layers that constitute an orientation system.

6. Worked Example: “Move the couch over there.”

To illustrate how orientation grammar operates in practice, consider the directive “Move the couch over there.” Although linguistically simple, the sentence engages multiple layers of orientation, each corresponding to a different structural frame: agency, temporality, spatiality, and energetic transfer. Each of these frames forms a micro-concrescence that contributes to the unified meaning of the utterance.

The four frames can be expressed as quadranym units:

Agent
[(self(let’s)) → (goal(move))]

Time
[(present(the_couch)) → (event(move))]

Space
[(void(move)) → (between(over_there))]

Energy
[(motion(move)) → (matter(the_couch))]

In each unit, the left side expresses the inherited state—self, present, void, or motion—while the nested token provides the concrete datum drawn from the situation. The right side expresses the potential satisfaction for that frame: goal, event, between, or matter. The directional transition encodes the concrescent route through which the unit integrates incoming potentials with inherited coherence.

For each frame, ND/PD heuristics determine whether the situational pressure introduced by the token can be absorbed without disrupting coherence. In this example, the familiar action of moving an object provides sufficient inherited structure to integrate the novelty introduced by the directive. Coherence holds at all four frames.

When these micro-concrescences align, they form a nested structure, with each quadranym unit operating inside a broader scaffold of agency, time, space, and energy. This hierarchical integration functions as a single sentence-level concrescence: the many distinct pressures of agency, temporality, spatial location, and energetic transfer unify into one coherent directive. In Whitehead’s terms, the many become one, and are increased by one.

7. Implications for Artificial Intelligence and Situated Cognition

The hierarchical and concrescent structure of the Dynamic Quadranym Model has direct implications for the development of artificial intelligence. Traditional approaches to AI rely on either logical evaluation or statistical correlation. Logical systems emphasize explicit propositions and rule-based inference, while semantic systems emphasize relational patterns and predictive fit. Both approaches assume that meaning is encoded in fixed structures. They do not address how a system maintains coherence as situations change, expectations shift, or inherited patterns collide with novelty.

DQM fills this gap by locating the core of cognition in orientation rather than representation. Orientation is the system’s active alignment with the world—its ongoing adjustment to pressures, possibilities, and constraints. Because each quadranym unit models a minimal act of concrescence, DQM treats intelligence as a continuous regulation of coherence across many nested arcs of becoming. This view reframes meaning as something achieved rather not given, making the dynamics of coherence central to understanding.

For artificial systems, this offers a new path beyond the limitations of symbol manipulation and statistical prediction. Instead of relying solely on propositions or embeddings, a system can operate by managing ND/PD coherence relations across multiple layers. This enables it to regulate its own sense of fit as circumstances evolve. The same micro-process—prehension of data, integration with subjective form, and closure in satisfaction—can be applied across perception, language, motor control, planning, and reasoning.

Because the quadranym structure is scale-invariant, the system does not require different architectures for different tasks. Nested concrescences allow local adjustments to propagate upward into larger patterns of behavior, while higher layers provide stabilizing constraints on lower ones. This alignment allows an artificial agent to remain situated, flexible, and intelligible across diverse contexts. The model therefore provides a principled mechanism for general intelligence grounded in coherence rather than static representation.

8. Verbs as Coherence Testers in Orientation Grammar

In natural language, verbs are traditionally treated as grammatical predicates that accept syntactic arguments—subjects, objects, and complements. However, this surface-level framework fails to capture the deeper function verbs perform in systems governed by orientation logic rather than propositional grammar. In the Dynamic Quadranym Model (DQM), verbs do not merely designate action or state; they operate as coherence evaluators. They test whether the environment—structured as a field of affordances—can satisfy their latent demands across multiple orientation domains.

Each orientation domain, or Reference Frame (RF), corresponds to a prime rendering: Agent, Energy, Time, and Space. These RFs contribute latent structural expectations—objective potentials—which verbs do not possess but instead interrogate. In this model, verbs function not as semantic owners but as procedural testers. They request specific types of affordances—such as goal (Agent), matter (Energy), event (Time), or between (Space)—and attempt to bind to these offered states. These bindings are not guaranteed. A hysteresis gate ensures that only when the subjective state’s coherence (ND) can hold against the external pressure plus a margin of tolerance (PD + τ), does the potential become installable. This dynamic ensures that verbs contribute not to the meaning of a sentence in isolation, but to the overall coherence of the orientation system.

The act of binding to RF affordances constitutes the true operational meaning of a verb in this framework. A verb does not assert its arguments; it resolves the tensions between internal coherence and external constraint. Meaning, then, is not derived from roles assigned within a propositional matrix, but from the system’s ability to align subjective continuity with situational demand. Through this lens, verbs emerge not as drivers of syntax, but as procedural mechanisms that preserve or challenge the system’s orientation.

This redefinition allows for a more generalizable, fractal, and context-sensitive parsing mechanism—one in which verbs are no longer constrained by surface grammar but are fully integrated into the systemic logic of orientation. It is not what a verb “means” that matters, but what it tests for—and whether the system, in response, can maintain coherence in the face of that test.

9. Open Directions and Deeper Layers

Much has been clarified in the preceding sections, yet a substantial portion of the Dynamic Quadranym Model remains to be explored. The discussion has shown how quadranym units function as micro-concrescences, how ND/PD heuristics regulate coherence, and how hierarchical nesting produces larger structures of understanding. But the model contains additional layers that extend well beyond the scope of this article—layers that draw directly from Whitehead’s more complex metaphysical distinctions and that offer even greater expressive power for a formal process calculus.

One such area concerns the relationship between Hyper-Quadranyms (HQs), Quadranym Units (QUs), and the different roles they play in the system. An HQ functions as the governing pattern for a domain—an orientation scaffold with its own internal layers. A QU is one layer within that scaffold, yet any HQ can itself become a QU inside a larger HQ. This recursive embedding creates a nested, multilevel architecture in which orientations propagate both upward and downward, mirroring the way Whitehead’s societies embed within larger societies and inherit patterns across scales.

A deeper topic concerns the distinction between the primagent and the consequential nature of the system. The primagent is the idealized, structural pole of orientation—what might be compared to Whitehead’s primordial nature of God, in the sense that it provides the invariant modal forms and the internal grammar by which concrescences can unfold. The consequential nature, by contrast, reflects the accumulated satisfactions of prior arcs—the sedimented lineage of coherence that shapes what the system inherits. The interaction between these two poles gives rise to a dynamic tension: a downward influence of structural form and an upward influx of novel experience.

This interplay introduces the crucial phenomenon of upflow and downflow. Downflow occurs when structural tendencies, modal patterns, or HQ-level scaffolds constrain the possibilities available to lower layers. Upflow occurs when local satisfactions, disruptions, or innovations feed back into higher layers, modifying the broader orientation. Between these movements lies what may be called the sandwiching event—the moment in which local and global tendencies meet, negotiate, and determine the next stable trajectory. This sandwiching event is the locus where local novelty tests the adequacy of inherited form, and where inherited form attempts to coordinate or absorb this novelty. It is a direct computational analogue of Whitehead’s claim that each occasion is both conditioned by the past and a fresh synthesis of becoming.

These additional concepts—HQs, QUs, primagent structure, consequential inheritance, upflow, downflow, and the sandwiching event—prepare the ground for a much broader orientation calculus. They open the possibility of modeling not only individual concrescences but also the evolution of coherent forms within a system over time. They allow for a richer account of how tendencies crystallize, how innovations propagate, and how a system can maintain identity while undergoing continuous transformation.

Much is being developed, including the temporal dynamics of orientation scripts, the logical structure of cross-domain alignment, and the deeper formal analogies between DQM’s primagent and Whitehead’s primordial ordering of potentiality. But the foundational pieces are now visible. The model’s micro-dynamics, hierarchical nesting, coherence heuristics, and process-theoretic alignment provide the beginning of a calculus adequate to the complexity of experience—one that treats meaning as a lived negotiation of stability and novelty rather than a static representation.

Computational tasks find foundation in how these higher-order structures interact, how orientation evolves across extended contexts, and how the primagent–consequent interplay shapes the long-range coherence of an intelligent system. These questions promise a fertile direction for continued exploration at the intersection of process philosophy, cognitive science, and artificial intelligence.

10. Conclusion

The Dynamic Quadranym Model offers a formal bridge between Whitehead’s metaphysics of concrescence and a computational grammar for situated cognition. By treating each quadranym unit as a micro-concrescence and each Hyper-Quadranym as a layered society of such units, the model provides a structural account of how coherence is continually achieved across shifting contexts. Nested orientation arcs, ND/PD coherence heuristics, and recursive HQ–QU embedding together form a process architecture in which meaning arises through alignment rather than static representation.

The expanded considerations in section 9 highlight how much remains open for further development. Concepts such as the primagent and consequential nature, upflow and downflow dynamics, and the sandwiching event all point toward a deeper processual framework in which orientation is shaped by both inherited form and emergent novelty. These interactions suggest that coherence is not merely maintained; it evolves. Local disruptions reshape global patterns, and global structures constrain local developments, producing a dynamic equilibrium akin to Whitehead’s vision of creative advance.

In this light, failure, misalignment, and interruption are not errors to be eliminated but phases of the process itself—sites where inherited tendencies encounter novelty and must renegotiate their coherence. Each such event expands the system’s lineage, enriching its capacity for future integration. This perspective mirrors Whitehead’s insistence that experience is experimental, provisional, and perpetually open to revision.

Taken together, the components of DQM form the outline of a process calculus: a formal system in which orientation is continuously updated through nested acts of becoming. This framework offers a path for developing artificial systems capable of sustaining coherence across time, context, and modality—systems that do not merely manipulate symbols but inhabit the dynamics of meaning. The work begun here suggests that general intelligence may depend not primarily on scale or data, but on the capacity to navigate the interplay of inheritance and novelty with coherence, flexibility, and creative responsiveness.

Appendix A

Expanded Core: Constraint Architecture for Coherence
The Dynamic Quadranym Model (DQM) treats coherence as the operational basis for meaning. Meaning emerges only when incoming novelty can be integrated into inherited structure. Earlier sections used ND (capacity) and PD (pressure). This appendix adopts the canonical symbols O_c(f) for capacity and P_c(f) for pressure to avoid symbol collision.

A.1 Thesis: Coherence Precedes Meaning
Coherence is the system’s primary operation. Meaning is secondary. The central question for any Hyper-Quadranym (HQ) is whether the inherited orientation structure has enough capacity to absorb the novelty introduced by the current situation.

Section 5 defined:

  • ND = inherited coherence (capacity)
  • PD = situational novelty (pressure)

This appendix uses:

  • O_c(f) = ordering capacity (formerly ND)
  • P_c(f) = situational pressure (formerly PD)

The semantics remain the same; only the symbols change.

A.2 Constraint Scaffold: Core Definitions
For any context c, the following elements define the integration environment:

  • X — Primary Data (incoming data stream)
  • Gamma_c — constraint scaffold governing how frames interact
  • h — lineage state (accumulated coherence from prior arcs)

Two operators implement capacity and pressure:

  1. Ordering capacity:
    O_c(f)
    Derived from (h, Gamma_c, X)
    (This equals ND in Section 5.)
  2. Pressure:
    P_c(f)
    Derived from X alone
    (This equals PD in Section 5.)

Crosswalk (Section 5 → Appendix A):
Inherited capacity = ND = ordering capacity = O_c(f)
Situational novelty = PD = pressure = P_c(f)
Data stream = Primary Data = X
Constraint relations = scaffold = Gamma_c

A.3 Capacity–Pressure Gate
An arc closes when:

O_c(f) >= P_c(f) + tau_c

Where:

  • O_c(f) = capacity inherited from past coherence
  • P_c(f) = pressure introduced by novelty
  • tau_c = hysteresis or margin
    This matches Section 5’s rule: ND ≥ PD + τ.

A.4 Algorithm for Coherence Integration
Given primary data X, the coherence process proceeds as follows:

  1. Compute ordering capacity:
    O_c(f) = OrderingFunctional(h, Gamma_c, X)
  2. Compute pressure:
    P_c(f) = PressureEstimator(f, X)
  3. Apply the gate:
    If O_c(f_star) >= P_c(f_star) + tau_c, the arc closes.
  4. Update lineage:
    h = UpdateLineage(h, f_star, X)
  5. Optionally refine constraints:
    Gamma_c = RefineScaffold(Gamma_c, h)

This preserves all semantics from earlier drafts and unifies the notation.

A.5 Whitehead Correspondence
Whitehead’s “ordering power” maps to O_c(f) (capacity).
Whitehead’s “creative advance” maps to P_c(f) (pressure).
No further changes required.

A.6 Diagnostics and Benchmarks
Evaluation uses the same margin:

Delta_c(f) = O_c(f) - (P_c(f) + tau_c)

A positive margin closes the arc; a negative margin triggers reorientation.