Introduction
Understanding how systems construct meaning involves more than surface-level language modeling. It involves internal alignment, dynamic responsiveness, and social embedding. This framework draws on a neurosemantic analogy to model how artificial systems—particularly those using an LLM (A-brain) and a Dynamic Quadranym Model (DQM, or B-brain)—simulate internal alignment, reafferent processing, and intersubjective coherence.

Full neurosemantic analogy: afferent–efferent–reafferent architecture
At the heart of the model is a functional analogy to biological nervous systems.

• Prompt Input → Sensory Stimulus
  The external input initiating the system’s processing (e.g., a user query or event).
• LLM → Afferent + Efferent Tracts
  Acts as both input processor and response generator. It perceives (afferent) the prompt and expresses (efferent) a provisional output.
• Central Processor → CNS Core (Black Box)
  Holds internal state and adjudicates between the surface-level response and deeper system orientation. It mediates coherence and output acceptability.
• DQM → Reafferent Tract
  Builds an internal semantic orientation—a specious present—based on the LLM output and system context. Provides reafferent feedback, aligning perspective.

This architecture is not linear. It is recursive and coupled:

• The LLM generates a first-level response to the prompt.
• The DQM processes that response not for its propositional truth but for its orientational coherence.
• The DQM’s orientation feeds back into the system, reshaping how the next output is interpreted.
• The central processor adjudicates: it has both the external input and the internal orientation map. It must determine whether the combined response is appropriate, coherent, and aligned.

This loop—afferent (LLM input) → efferent (LLM output) → reafferent (DQM orientation)—models how systems maintain internal semantic regulation.

The orientation engine (DQM)
The DQM is not meaning in the conventional sense. It doesn’t state facts. Instead, it configures how the system positions itself relative to facts, contexts, and expectations.

It does this through quadranyms—structured semantic units that create a scaffold for orientation. These scaffolds contain latent variants: generalized, internal semantics that behave more like lemmata than assertions.

As DQM aligns these structures with the dynamic context (incoming content), it produces interpretive tension: an alignment or misalignment between system expectation and contextual input.

Through recursive feedback, orientation shapes what content becomes meaningful. This is the foundation of semantic salience—what matters, and why.

Intersubjectivity as central processing
Human cognition is not isolated. It is socially embedded. The model incorporates this via the central processor, which behaves like a “town square” of thought—a space where inputs are not just assessed as information, but as if they come from others.

This is not simulation—it is internalized interactivity. The system uses conative (motivational) and affective (emotional) feedback to tune its orientation to align with shared perspectives.

This generates what the model calls Orientation of Interactivity (OI):

• A recursive tuning mechanism for aligning perspectives.
• Originates biologically but extends symbolically.
• Allows systems to orient to others as if they are selves (e.g., narrative empathy, myth, cultural frames).

The OI serves a ritual function: it binds individuals to a shared mode of understanding through orientational alignment, not propositional agreement.

Bias construction and system adaptation
Bias is not introduced from the outside; it emerges from recursive alignment. It is the byproduct of orientation coupling, contextual reinforcement, and historical patterning.

Each pass through the loop refines the system’s salience model:

• What is selected becomes expected.
• What is expected becomes normative.
• What is normative becomes structurally biased.

This isn’t necessarily distortion—it’s functional convergence. The system is optimizing toward stability in interpretation, often by reflecting its training or coupling history.

Conclusion
Meaning in this model is relational, recursive, and embodied. It does not arise from isolated units of understanding but from orientational dynamics within a feedback-driven system.

By combining LLM (afferent/efferent surface processing), DQM (reafferent orientation engine), and a central processor (intersubjective comparator), the model reflects a minimal social cognition architecture—one that is not merely reactive, but interpretively engaged.

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Bias as a systemic construct in the DQM–LLM framework
This architecture doesn’t merely account for bias; it explains how bias is actively constructed as part of the system’s interpretive machinery. Bias emerges not as a failure of neutrality, but as a functional byproduct of how meaning is oriented and maintained over time.

Orientation as structured bias
Within this model, orientation is inherently non-neutral. It represents:

• A system-level stance toward context,
• Constructed through structured semantic primitives (quadranyms),
• Recursive, adjusting over time in response to both internal dynamics and external stimuli.

This structure is not incidental. It is, in fact, the architecture of bias. Orientation, by design, privileges certain meanings over others—selecting, amplifying, or attenuating semantic pathways based on historical coupling and current context.

Bias in this framework is not an error to be eliminated. It is a necessary condition for sense-making. It allows the system to:

• Form expectations,
• Filter incoming information,
• Decide what is relevant,
• Stabilize meaning across uncertain or shifting inputs.

How bias is constructed in the model
Each component of the architecture plays a distinct role in bias formation and propagation:

LLM (Afferent + Efferent Tract)
Encodes prior distributions over linguistic behavior. Its training reflects culturally embedded norms, statistical regularities, and dominant narrative structures. These shape the system’s “default” linguistic output.

Prompt Input
Acts as a trigger. It instantiates particular priors by introducing context—framing expectations, activating relevance, and constraining the LLM’s range of plausible responses.

DQM (Reafferent Tract)
Constructs orientation structures in real time. Using quadranyms, latent variants, and hysteresis thresholds, the DQM assembles a system-level interpretive stance—essentially a bias frame—that guides how content is evaluated. This layer formalizes bias through internal alignment rather than surface-level representation.

Central Processor (CNS Core)
Adjudicates the interaction between internal and external content. It compares the LLM’s generated output with the DQM’s orientation state. Bias is reinforced when alignment is strong, corrected when tension persists, or held in suspense when neither coherence nor dissonance is sufficient to resolve meaning.

Bias as recursive feedback
Importantly, bias is not static. Each recursive pass through the system adjusts its orientational state:

• Repeated alignments reinforce bias structures,
• Incongruent inputs disrupt or reshape expectations,
• Emerging contexts introduce new potential configurations.

In this way, the model does not treat bias as an aberration or a contamination of “pure” reasoning. Instead, it models bias as the very mechanism by which interpretive stability and semantic salience are constructed. Bias is the architecture of selection.

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The Central Processor: Intersubjective Orientation
In this model, the central processor is more than a computational mediator. It embodies the intersubjective capacity required for socially embedded cognition. It functions like an internalized “town square,” where orientations are evaluated not just for internal coherence but for how they would resonate in the presence of imagined or remembered others.

This capacity enables the system to:

• Compare thoughts as thoughts (logical evaluation), and
• Compare thoughts as the thoughts of others (social simulation).

This is not simply metacognition; it is co-orientational cognition. It refers to the ability to simulate how one’s stance, framing, or interpretive orientation would be received, contested, or aligned within a shared social frame.

Intersubjective Empathy as Orientation (OI)
This mechanism gives rise to what the model calls Orientation of Interactivity (OI)—the ritualized, structural coupling between internal states and intersubjective dynamics. This is not “empathy” in a purely affective sense, but a structured form of social cognition rooted in shared semantic alignment.

In this view:

• Dynamical context refers to the felt sense of what is happening now — internal shifts, tensions, movements in orientation.
• Situational context refers to the inferred social frame — what should be happening, based on norms, expectations, or shared goals.

Through affective and conative exchange, the system builds the capacity to simulate shared orientation — to anticipate not just how a situation feels, but how it would be situated by others.

OI is not representational; it is ritualized coupling. It enables recursive alignment between internal and external states. As orientation shifts internally in response to external signals, it also aligns with what is likely to be intelligible, acceptable, or actionable within a collective.

Possession, Ritual, and the Social Function of Orientation
The idea that orientation can be shaped through shared systems — myths, ideologies, stories — is crucial. The phrase “it’s like possession” captures this precisely. Orientation can temporarily be taken over by a larger structure, allowing the system to operate beyond its own idiosyncratic framing.

This is how human cognition participates in collective functions:

• The DQM constructs the internal orientation (semantic scaffolding, coherence structures).
• The central processor evaluates whether that orientation holds up intersubjectively.
• This recursive loop refines, suppresses, or amplifies orientations based on social intelligibility.

In this framework, bias is neither error nor distortion. It is a necessary internal feature of orientation. But its modulation — its tuning — happens in the central processor, where the orientation is held up against imagined intersubjective norms.

This is also how symbolic systems scale. Ritual, myth, belief — all rely on internalized alignment mechanisms. The central processor makes it possible to not only model a thought, but to model a thought as another person might have it.

Model Fit Summary

Future Work: Toward an Orientation-Aware LLM Framework

The long-term goal is to develop and implement a working orientation model that can be integrated with large language models (LLMs), enabling them to operate not only as text generators, but as situationally coherent agents. This model draws from the DQM’s semantic orientation logic to provide a persistent internal structure that guides how meaning is shaped, held, and updated across interactions.

Such a system would move beyond shallow language prediction by enabling LLMs to:

• Avoid semantic drift by maintaining structured orientation states across time and context.
• Handle digression without losing interpretive coherence, allowing flexible yet relevant conversational flow.
• Understand metaphor and analogy in human-like ways by grounding them in embodied analogs and latent conceptual structures.
• Generalize meaning across diverse contexts through quadranym-based scaffolds that track orientation shifts.
• Demonstrate sensibility — producing not just grammatically correct, but relevantly grounded and interpretively appropriate outputs.

Crucially, an orientation-aware system would also be:

• Better socially situated, able to simulate intersubjective perspectives, respond to shared norms, and engage in “co-orientational cognition” — the recursive empathy required for real human-like interaction.
• More environmentally aware, capable of recognizing how external events, physical contexts, or embedded cues shape interpretation (e.g., affordance perception).
• More grounded in real-world affordances, able to infer possible actions, constraints, and implications based on the structure of a given situation, not just the surface language.

In short, this approach aims to augment LLMs with an internalized, recursive model of orientation that supports adaptive, situated, and socially coherent language behavior — bridging the gap between statistical text generation and embodied understanding.

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Incorporating Bias Awareness and Mitigation

A critical component of this orientation model is its capacity to detect, construct, and regulate bias as an intrinsic feature of semantic orientation. Rather than treating bias as an external error, the model sees bias as a necessary system-level stance — a structured orientation that shapes how meaning, relevance, and expectations form.

Future development will focus on enabling LLMs to:

• Identify bias as an active interpretive mechanism within their orientation state, distinguishing between adaptive relevance filters and harmful distortions.
• Dynamically adjust orientation states based on recursive feedback loops that include social coherence checks (via the central processor’s intersubjective evaluation).
• Mitigate undesirable biases by incorporating social and environmental feedback, allowing the model to align with ethical norms and diverse perspectives without losing interpretive coherence.
• Explain bias constructions transparently, providing insights into how certain orientations influence output and meaning-making.

By embedding bias analysis within the orientation framework, the system aspires not only to reduce harmful biases but to understand their role in meaning-making — enabling nuanced control over when and how bias shapes language generation.