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This study aims to enhance the semantic continuity of language generation in transformer-based models by introducing the Recursive State Transition Architecture (RSTA).

June 11, 2026Open Access

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

Key Points

This study aims to enhance the semantic continuity of language generation in transformer-based models by introducing the Recursive State Transition Architecture (RSTA).
Introduced RSTA framework integrating semantic dynamics and trajectory tracking.
Developed five mechanisms: Continuous Semantic State Space, State Coupling Matrix, and others.
Ensured compatibility with existing transformer implementations through hidden-state probing and classifiers.
Achieved explicit semantic modeling, improving long-horizon coherence and reducing semantic drift.
Demonstrated robustness in recursive reasoning processes and persona consistency.
Outperformed standard transformer architectures in trajectory-aware generation capabilities.

Abstract

Modern Large Language Models (LLMs) demonstrate strong semantic generation capabilities throughTransformer-based architectures. However, existing systems primarily model token relationshipsthrough attention mechanisms rather than explicit semantic transition dynamics.Current language models frequently exhibit long-horizon instability, including semantic drift, recursivereasoning fragmentation, persona inconsistency, and code architecture degradation. These failurepatterns suggest a structural limitation: standard next-token prediction optimizes local coherencewithout explicitly preserving long-horizon semantic continuity.This paper proposes Recursive State Transition Architecture (RSTA), a semantic dynamicsaugmentation framework designed for Transformer-based systems. Rather than replacingTransformers, RSTA introduces explicit semantic state modeling, recursive trajectory tracking, semanticinertia preservation, and transition-gated semantic transformation.The central contribution of RSTA is trajectory-conditioned generation: rather than conditioningeach generation step solely on the current context, RSTA conditions generation on the directionalevolution of semantic state across recursive steps. This enables explicit modeling of semantic inertia,drift detection, and transition-aware generation — capabilities absent from standard Transformerarchitectures.The framework introduces five primary mechanisms: Continuous Semantic State Space, State CouplingMatrix, 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 withexisting Transformer implementations.RSTA proposes a semantic dynamics perspective that may serve as a future augmentation layer forlong-horizon reasoning systems, semantically persistent agents, recursive planning architectures, andtrajectory-aware language generation systems.

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