Toward Stable Long-Horizon Reasoning: A Conceptual Framework for Semantic Tension, Convergence Fields, and Multi-Layer Observation Protocols

Large Language Models (LLMs) demonstrate impressive generative capabilities, yet remain vulnerable to semantic drift, reasoning instability, and context collapse during long-horizon interactions. This paper introduces an observational and conceptual framework for understanding these behaviors, integrating three constructs: Semantic Tension, Convergence Fields, and Multi-Layer Observation Protocols (OE-Series). Across 3,500+ hours of structured multi-platform interaction logs, we identify recurring, architecture-agnostic behavioral patterns, suggesting that LLM reasoning follows predictable convergence dynamics. This work provides a theoretical foundation for future studies on stability, interpretability, and long-context cognition.