The Missing Layer of AGI: From Pattern Alchemy to Coordination Physics

TL;DR

This paper proposes a new theoretical framework and architecture for enhancing Large Language Models with a coordination layer, aiming to overcome current limitations and advance towards Artificial General Intelligence.

Contribution

It introduces UCCT, a formal theory of semantic anchoring, and MACI, an architecture implementing coordination mechanisms to enable reasoning in LLMs.

Findings

UCCT models reasoning as phase transitions influenced by support and mismatch.

MACI architecture incorporates baiting, filtering, and persistence mechanisms.

Reframing objections as coordination failures suggests LLMs are key to AGI progress.

Abstract

Influential critiques argue that Large Language Models (LLMs) are a dead end for AGI: "mere pattern matchers" structurally incapable of reasoning or planning. We argue this conclusion misidentifies the bottleneck: it confuses the ocean with the net. Pattern repositories are the necessary System-1 substrate; the missing component is a System-2 coordination layer that selects, constrains, and binds these patterns. We formalize this layer via UCCT, a theory of semantic anchoring that models reasoning as a phase transition governed by effective support (rho_d), representational mismatch (d_r), and an adaptive anchoring budget (gamma log k). Under this lens, ungrounded generation is simply an unbaited retrieval of the substrate's maximum likelihood prior, while "reasoning" emerges when anchors shift the posterior toward goal-directed constraints. We translate UCCT into architecture with MACI, a coordination stack that implements baiting (behavior-modulated debate), filtering (Socratic judging), and persistence (transactional memory). By reframing common objections as testable coordination failures, we argue that the path to AGI runs through LLMs, not around them.