RSTA: Explicit Semantic Transition Modeling for Transformer-Based Language Systems

Modern Large Language Models (LLMs) demonstrate strong semantic generation capabilities through
Transformer-based architectures. However, existing systems primarily model token relationships
through attention mechanisms rather than explicit semantic transition dynamics.
Current language models frequently exhibit long-horizon instability, including semantic drift, recursive
reasoning fragmentation, persona inconsistency, and code architecture degradation. These failure
patterns suggest a structural limitation: standard next-token prediction optimizes local coherence
without explicitly preserving long-horizon semantic continuity.
This paper proposes Recursive State Transition Architecture (RSTA), a semantic dynamics
augmentation framework designed for Transformer-based systems. Rather than replacing
Transformers, RSTA introduces explicit semantic state modeling, recursive trajectory tracking, semantic
inertia preservation, and transition-gated semantic transformation.
The central contribution of RSTA is trajectory-conditioned generation: rather than conditioning
each generation step solely on the current context, RSTA conditions generation on the directional
evolution of semantic state across recursive steps. This enables explicit modeling of semantic inertia,
drift detection, and transition-aware generation — capabilities absent from standard Transformer
architectures.
The framework introduces five primary mechanisms: Continuous Semantic State Space, State Coupling
Matrix, Semantic Trajectory Detection, Transition-Gated Semantic Transformation, and State-
Conditioned Generation. Semantic state dimensions may be extracted through hidden-state probing,
learned latent projections, or structured semantic classifiers, making the framework compatible with
existing Transformer implementations.
RSTA proposes a semantic dynamics perspective that may serve as a future augmentation layer for
long-horizon reasoning systems, semantically persistent agents, recursive planning architectures, and
trajectory-aware language generation systems.