The Unified Cognitive Consciousness Theory for Language Models: Anchoring Semantics, Thresholds of Activation, and Emergent Reasoning

Few-shot learning in large language models (LLMs) reveals a deep paradox: Some tasks generalize from minimal examples, while others require extensive supervision. We address this through the Unified Cognitive Consciousness Theory (UCCT), which reframes LLMs not as incomplete agents, but as unconscious substrates, repositories of latent linguistic and conceptual patterns that operate without explicit semantics or goal-directed reasoning. In this view, LLMs are not broken approximations of cognition, but necessary and foundational components of general intelligence. Semantic anchoring, through prompts, roles, and interaction, acts as a conscious control layer, binding latent structure to task-relevant meaning and enabling coherent reasoning. UCCT offers a unifying account of prompting, fine-tuning, retrieval, and multi-agent coordination, all grounded in probabilistic alignment between unconscious representation and external control. To support this model, we present the Threshold-Crossing Dynamics Theorem, which formalizes semantic anchoring as a probabilistic phase transition. But the central claim remains architectural: AGI will not emerge by discarding LLMs, but by aligning and integrating them into systems that reason, regulate, and adapt together.

@article{chang2025_2506.02139,
  title={ The Unified Cognitive Consciousness Theory for Language Models: Anchoring Semantics, Thresholds of Activation, and Emergent Reasoning },
  author={ Edward Y. Chang },
  journal={arXiv preprint arXiv:2506.02139},
  year={ 2025 }
}