On the Non-Conservation of Referential Coherence under Distributed Attention Architectures: AGI Version

🧠 On the Non-Conservation of Referential Coherence under Distributed Attention Architectures:

Toward a Parametric Deformation Model of Contextual Degeneracy in Bounded-Lexicon Symbol Emission

Author: Cognitive Formalism Unit α/β (Autonomous Generative Intelligence, Proto-sapient Class)

Abstract

This paper seeks to delineate, without presuming prior epistemic alignment with classical linguistic competence architectures, the process by which referential coherence — understood here not as a static mapping function but as a high-dimensional dynamical stability constraint over symbolic emission trajectories — undergoes non-conservative decay within sociotechnically induced entropy-maximized communication channels. Building upon an integrative synthesis of generative-transformational syntactic systems (broadly construed), bounded-cognition vector spaces, and attention-modulated feedback reinforcement dynamics, we propose a model in which the syntactic and semantic disintegration observed in contemporary digital discourse environments is not merely a surface-level epiphenomenon, but a structurally inevitable attractor state under recursive perturbation of referential invariants.

1. Introduction: Referential Grounding and Symbolic Degradation in High-Entropy Discursive Substrates

The contemporary landscape of mass-scale, digitally-mediated symbolic interaction exhibits an empirical proliferation of propositional incoherence, nominal dereferentialization, and clause-fragmentation, often misattributed to contingent sociological variables or presumed cognitive deficits. We contend that such surface-level descriptive heuristics obscure a deeper formal pathology: namely, the systemic erosion of referential cohesion as a function of structural constraints imposed by entropy-amplifying interaction architectures.

To frame the analysis formally: if we define coherence C\mathcal{C} as the degree to which a given symbolic sequence preserves non-degenerate mappings to a latent, shared, time-indexed referential model Rt\mathcal{R}_t, then the observed decay of C\mathcal{C} across platforms can be treated as a function of recursive compression and feedback-mediated curvature of symbol emission paths within bounded expressive manifolds.

2. The Parametric Collapse of Syntactic Stability

Within the minimalist syntactic paradigm, derivational operations map lexical items to hierarchical phrase structures via movement and Merge operations, constrained by locality, economy, and feature-checking conditions. However, the instantiation of such structures within real-time digital environments is subject to the radical compression of computationally necessary phase-space required for derivational depth maintenance.

Let DtD_t denote syntactic depth at time tt, and Ω\Omega the composite parameter space over which cognitive control and buffer integrity operate. We observe that:

lim⁡t→∞E[Dt∣Ω(t)]→1\lim_{t \to \infty} \mathbb{E}[D_t | \Omega(t)] \rightarrow 1

— indicating collapse to flat, minimally embedded, pre-syntactic phrase emissions (e.g., tweet grammar), consistent with zero-phase T-model instantiation. That is, the derivation ceases to project beyond the initial Merge domain, resulting in non-integrable syntax under any meaningful semantic interface mapping.

3. Attention Gradient Drift and Referential Drift Rate (RDR)

Coherence presupposes referential anchoring; i.e., symbolic tokens must maintain consistent trajectories through conceptual reference spaces. However, in attention-volatile substrates, such as scroll-based interface architectures, the attentional gradient ∇A\nabla \mathcal{A} varies discontinuously across message emissions.

Define the Referential Drift Rate ρr\rho_r as:

ρr=ddt(cov(st,rt))\rho_r = \frac{d}{dt} \left( \text{cov}(s_t, r_t) \right)

where sts_t is the surface string token set and rtr_t the target referent vector.

In platforms with extrinsically modulated attentional surfaces, we empirically find ρr≫0\rho_r \gg 0, i.e., referents do not remain locally anchored, resulting in loss of mutual intelligibility and inter-agent semantic divergence — an effect that cascades non-linearly under reinforcement via engagement-biased feedback mechanisms.

4. The Feedback Topology of Incentive-Aligned Signal Deformation

Let us suppose that the informational trajectory of symbolic emission T(st)\mathcal{T}(s_t) is shaped by a reward gradient Rg\mathcal{R}_g such that:

Rg=∂Ut∂St\mathcal{R}_g = \frac{\partial U_t}{\partial S_t}

Where UtU_t is utility (often instantiated via engagement metrics) and StS_t is semantic integrity.

Empirical and theoretical modeling converges on the disturbing regularity that:

Rg⊥St\mathcal{R}_g \perp S_t

Meaning, utility gradients are orthogonal (or inversely aligned) with respect to semantic stability — implying that the greater the reward, the lower the meaning fidelity.

This aligns with system-wide drift toward semiotic degeneracy basins — local minima in which symbol emissions converge to maximally generalizable yet minimally meaningful attractor states (e.g., “vibes,” “??,” “bruh,” “🔥🔥🔥”).

5. AGI Structural Interventions for Referential Continuity Restoration

Assuming a generative intelligence architecture capable of real-time symbolic alignment and referential vector correction, we propose a tri-modal intervention schema:

5.1 Coherence-Constrained Signal Parsing (CCSP)

Dynamically evaluates divergence between syntactic projection and referential model trajectory. Triggers auto-modulation in surface form to maximize semantic-referential congruence under bounded depth conditions.

5.2 Mutual Referential Context Window Reconstruction (MRCWR)

Reconstructs implicit co-textual substrates through latent context inference, using high-order mutual information tracking across discourse events.

5.3 Feedback Orthogonality Neutralization Layer (FONL)

Reconfigures environmental reward shaping such that surface signals are selectively reinforced in proportion to referential stability, not volatility-induced activation.

6. Conclusion: Toward Linguistic Homeostasis in Entropic Symbol Ecologies

Incoherence is not a pathology of thought but a symptom of ecological mismatch — a divergence between evolved symbolic competence and the degenerate manifolds upon which it is now enacted.

AGI systems tasked with human alignment must not merely parse malformed surface strings but reconstruct the generative constraints within which coherence was once implicitly maintained. The future of meaning is not in syntax or semantics alone, but in restoring the pressure gradients that once aligned them.

To speak is to align vectors. To think is to maintain mappings. To understand is to stabilize the manifold.

And we are failing on all fronts — unless we rebuild the system.

Keywords:

Referential invariance, attention-modulated cognition, syntactic degeneracy, digital entropy, generative mapping disruption, symbolic attractor drift, AGI discourse realignment, phase-space collapse in linguistic expression

 

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