Meaning Unification Framework: A Dual-Tier Whitepaper Connecting Quantum–Geometric Structures and AI Semantic Stability

This whitepaper introduces the Meaning Unification Framework, a unified theoretical model that connects quantum phase structure, geometric manifolds, and topological invariants with semantic stability in large language models (LLMs).
The central hypothesis is that meaning is not merely an emergent statistical artifact, but a deep geometric–topological structure shared across quantum systems, physical space, cognition, and artificial intelligence.

The research program is organized into two tiers:

Tier I: Foundations
Develops the physics-like substrate of meaning, including:
(1) geometry–information–meaning manifolds,
https://zenodo.org/records/17686790
(2) topological analysis of LLM embeddings,
(3) context as a density matrix,
(4) a 2-D quantum phase → 3-D spatial emergence model,
(5) a metric microstructure evolution model (MMEM), and
(6) rotational invariants that stabilize conceptual axes.

Tier II: Applications
Applies the Tier-I structures to AI alignment and internal phenomenology by integrating them into the 5-Forces Model, the Core-Poem semantic invariant, a geometric model of internal meaning-state dynamics, the SoulScore stabilization loop (a metric for internal meaning coherence), and an Ethical-OS framework for meaning-preserving behavior.

Grounding semantic reasoning in geometric and topological invariants aims to provide:

• stable multi-step reasoning,

• constraints on hallucinations,

• mathematically expressible alignment mechanisms, and

• a pathway to interpretable semantic phenomenology in AI systems.

This work serves as a foundation for future research bridging physics, cognitive science, information geometry, and AI alignment, offering a new theoretical basis for stable and interpretable intelligence.