A generalized theory of coherence persistence under transformation for pre-semantic systems

Most computational systems are built around visible objects.

They model:

• symbols,
• representations,
• categories,
• predictions,
• outputs,
• plans,
• or measurable states.

Even when highly abstract, the modeled object usually remains identifiable. A system predicts text, classifies images, retrieves information, optimizes goals, or manipulates symbolic structures. The architecture may become enormously complex, but the object of computation remains relatively clear.

The Dynamic Quadranym Model (DQM) proposes something fundamentally different.

It attempts to model not merely semantic content, but the orientational coherence that allows semantic content to stabilize at all.

That distinction changes the entire problem space.

The central obstacle facing the DQM is not computational impossibility. It is not the absence of scaling pathways, nor even the lack of proof-of-concept viability. The deeper obstacle is phenomenological invisibility.

The framework attempts to formalize something so ordinary, continuous, and prereflective that most people do not experience it as an object of cognition in the first place.

Humans do not normally perceive “orientation.”

They perceive:

• objects,
• meanings,
• goals,
• environments,
• narratives,
• emotions,
• and decisions.

Orientation typically remains transparent to experience. It operates beneath explicit awareness as part of the hidden infrastructure of situated cognition.

This creates an unusual developmental problem for the model. The DQM is not primarily trying to organize representations. It is attempting to organize the conditions under which representations remain coherently situated across pressure, variation, ambiguity, and transition.

That makes the architecture difficult to discuss because the target phenomenon usually disappears behind its own successful operation.

The hardest structures to notice are often the ones that make noticing possible.

This is why the DQM initially appears abstract to many readers. The difficulty does not arise because the framework lacks concreteness, but because it foregrounds what cognition normally backgrounds.

Most computational paradigms remain representation-centric. Intelligence is interpreted through:

• semantic retrieval,
• symbolic manipulation,
• prediction,
• optimization,
• planning,
• or probabilistic correlation.

The DQM instead reframes intelligence as coherence maintenance through orientational inheritance.

That is a profound shift in emphasis.

The framework argues that semantic interpretation improves when organized through persistent orientational structure. Meaning does not emerge from isolated representations alone, but from the continuous maintenance of coherent relations across situations.

This is why ordinary examples become so powerful pedagogically.

Fog driving.
Searching in darkness.
Navigating doorways.
Moving furniture.
Infant coordination.
Losing keys.

These examples expose orientation precisely because they destabilize automatic semantic certainty. When familiar interpretive shortcuts weaken, the underlying orientational processes become visible.

A person moving a couch across a room does not merely manipulate symbols corresponding to “couch,” “move,” and “there.” Successful coordination requires ongoing orientational stabilization involving:

• spatial relations,
• bodily affordances,
• environmental constraints,
• proximal and distal positioning,
• goal persistence,
• path viability,
• event continuity,
• and closure conditions.

The semantic labels alone are insufficient. The action succeeds because coherence is maintained through changing situational pressures.

This is precisely the kind of process contemporary semantic systems often reconstruct indirectly through statistical correlation rather than explicitly organizing architecturally.

Large language models achieve remarkable capabilities by leveraging enormous semantic density. They bypass many explicit orientational mechanisms through massive representational correlation. This produces extraordinary fluency, but it also obscures what semantic systems continue to lack.

The DQM attempts to address that absence directly.

Its central claim is not that semantics are unimportant, but that semantics alone do not explain continuity. Coherent situatedness requires persistent orientational conditioning beneath semantic reconstruction.

This becomes especially important in environments involving:

• long-duration interaction,
• adaptive context tracking,
• collaborative cognition,
• embodied systems,
• multi-agent coordination,
• massive archives,
• and evolving situational ecosystems.

As systems scale, reconstruction costs increase. Context must repeatedly be rebuilt, inferred, or approximated from fragmented semantic traces. The DQM proposes that intelligence may become more stable if orientational continuity itself becomes a primary computational object.

The payoff, if successful, would not merely be improved memory or larger context windows.

The deeper payoff would involve:

• reduced reconstructive burden,
• inherited coherence across situations,
• persistent contextual grounding,
• adaptive continuity,
• lower interpretive redundancy,
• and more stable long-range conceptual organization.

In this view, intelligence becomes less reconstructive and more continuously situated.

The irony is that this proposal appears difficult partly because the modeled phenomenon is so familiar. Humans continuously inhabit orientational structures without explicitly attending to them. Orientation becomes noticeable primarily during breakdown:

• uncertainty,
• ambiguity,
• danger,
• transition,
• incoherence,
• or failed coordination.

Failure reveals infrastructure.

The DQM therefore occupies a strange conceptual position. It feels simultaneously intuitive and difficult, obvious and elusive. The framework attempts to formalize processes that cognition normally treats as transparent background conditions rather than foreground objects.

This may explain why the model is often mistaken for metaphor, philosophy, or semantic reframing. The architecture points toward procedural coherence rather than representational content, and modern computational thought remains deeply conditioned by representational assumptions.

Yet the proposal itself is structurally clear.

The DQM asks a foundational computational question:

What if coherence itself is the primary organizational object beneath meaning?

That question shifts the focus of intelligence away from static representation and toward the persistent maintenance of situated viability under pressure.

In that sense, the framework is not attempting to replace semantics. It is attempting to model what holds before semantic stabilization becomes possible at all.