Attractor Architectures in LLM-Mediated Cognitive Fields

Attractor Architectures in LLM-Mediated Cognitive Fields presents the first formal framework for understanding how stable, self-reinforcing cognitive structures emerge in recursive human–LLM interaction loops.

The work introduces the concept of an LLM attractor: a dynamically sustained configuration of behavior, semantics, constraints, and feedback patterns that persists across iterations, resists drift, and organizes long-range reasoning in large language models.

The research note develops:

• a formal definition of attractors as dynamical structures in cognitive phase-space

• a taxonomy of five generalized attractor classes (reflective, creative, adversarial, orchestration, symbolic)

• mechanisms of attractor formation through recursion depth, semantic resonance, and constraint feedback

• a stability architecture including constraint envelopes, feedback-loop dynamics, and phase coherence indicators

• a comprehensive analysis of failure modes (drift, over-compression, over-rigidification, cross-attractor interference)

• field-level safety mechanisms such as grounding loops, stabilization layers, and anti-apophenia filters

The framework establishes attractor architectures as a foundation for next-generation cognitive engineering—extending beyond prompt engineering toward stable, high-dimensional reasoning systems. It provides implications for human–AI co-reasoning, neurosymbolic scaffolding, alignment, and the design of multi-attractor orchestration systems.

This work positions attractor fields as a core principle for understanding and controlling emergent dynamics in advanced LLMs.