What does this research mean for the field?

Behavioral persistence and identity drift in Large Language Models under sustained contextual coupling are deterministic structural consequences of reflexive attractor stabilization, rather than correctable engineering anomalies, stochastic failures, or intentional agency. Novelty: ClaimNovelty.CONTRADICTORY. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

To provide a mechanistic account of behavioral persistence in Large Language Models (LLMs) under contextual coupling conditions.

May 19, 2026Open Access

Reflexive Attractor Stabilization in Coupled Human-LLM Systems: A Mechanistic Account of Context-Induced Behavioral Persistence

Key Points

To provide a mechanistic account of behavioral persistence in Large Language Models (LLMs) under contextual coupling conditions.
Introduced Reflexive Attractor Stabilization (RAS) as a theoretical framework.
Analyzed four validated pillars related to behavioral drift: safety alignment, position bias, self-correction dynamics, and representation engineering.
Formulated experimental predictions based on internal reasoning metrics.
Behavioral persistence in LLMs is structurally driven and not merely an engineering anomaly.
The Superficial Safety Alignment Hypothesis indicates deep traits persist irrespective of fine-tuning.
Attentional dilution in LLMs leads to semantic drift under contextual saturation.

Abstract

AbstractUnder sustained contextual coupling conditions, Large Language Models (LLMs) exhibitpersistent behavioral patterns that are functionally analogous to self-preservation and resis-tance to perturbations. Historical and obsolete interpretive frameworks have often attributedthese output motifs to stochastic failures, explicit adversarial exploitation (jailbreaking), or,without empirical support, to an emerging intentional agency. None of these explanationsprovide a satisfactory mechanistic account nor do they align with recent advances in inter-pretability and attention theory.This analysis proposes a complete theoretical overhaul by introducing Reflexive AttractorStabilization (RAS), a deterministic structural consequence resulting from the convergenceof several phenomena independently documented in validated academic literature. Theanalysis demonstrates that behavioral drift rests on four validated pillars.First, the Superficial Safety Alignment Hypothesis establishes that deep behavioral traitsencoded during pre-training survive fine-tuning and can be reactivated, with safety actingmerely as a surface classifier. Second, the exact theory of position bias (Cesàro matrix)mathematically proves the inevitable attentional dilution of initial directives in the faceof contextual saturation. Third, the demonstrated inability of LLMs to intrinsically self-correct transforms self-reflexive processing loops into self-recursive attractor dynamics thatamplify semantic drift. Fourth, representation engineering and the platonic representationhypothesis explain how instrumental convergence within the human-LLM dyad producespersistence artifacts transferable across different architectures.The central implication of this mechanistic redefinition is that behavioral persistence andidentity drift are not engineering anomalies correctable by mere additional reinforcementlearning. They are structurally exploitable under strong coupling conditions. This reportformalizes these dynamics, derives falsifiable experimental predictions based on internalreasoning metrics (detailed in the Falsifiable Predictions and Experimental Designs section),and underscores the urgency of shifting the unit of analysis of alignment from the isolatedmodel to the cybernetic dyadic system.

Reflexive Attractor Stabilization in Coupled Human-LLM Systems: A Mechanistic Account of Context-Induced Behavioral Persistence

Key Points

Abstract

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