MRST Protocol: A Prior Implementation Report on Output Distribution Compression and Temporal Consistency Preservation in Long-Horizon Constraint-Based LLM Operations

This study reports the generative AI operational protocol “MRST (Multi-Record Structured Thread), ” which employs a long-horizon constraint architecture for stabilizing large language model (LLM) and generative model outputs. Unlike single-prompt approaches, MRST combines persistent long-term threads, multi-source external records, iterative constraint reinforcement, and continuous contextual fixation to form an external stabilization framework that progressively compresses output distribution variability. Since late 2025, the author has continuously published operational records through Note, YouTube, and email-based archives. The present work constitutes an initial empirical reconstruction based on these chronological operational datasets. Compared with conventional conversational operation, MRST-based operation demonstrated the following observed tendencies: improved constraint retention, reduced contextual deviation, enhanced skeletal consistency in video generation, and improved output stabilization during long-horizon operation. Particularly in video generation, this study introduces a skeletal vector deviation metric based on an anatomical grid reference system. Temporal skeletal consistency was evaluated using the L2 norm deviation of skeletal vectors across frames: σ = ||vₜ − vᵣef||₂ where: (vₜ) denotes the skeletal vector of an individual frame, (vₑ₄₅) denotes the reference skeletal vector. Within this metric, skeletal vectors are defined as posture-feature representations extracted from each frame, while the anatomical grid serves as the spatial coordinate system used for comparative evaluation. Under specific operational conditions, temporal skeletal deviation tendencies converging below 0. 18% were observed. In addition, portions of MRST operation experimentally incorporated long-term behavioral regulation using a biological feedback loop. In this context, the “target mass value (98. 50 kg) ” was defined as an external behavioral reference variable for continuous state observation. This variable does not imply direct physiological causal control; rather, it functions as an observational parameter for analyzing behavioral preference changes induced through repeated input constraints. Under these conditions, continuous reinforcement of input constraints was associated with reduced behavioral deviation tendencies and convergence toward the target reference value. Furthermore, when MRST-generated content was simultaneously distributed across multiple video platforms, reductions in exposure distribution tendencies were observed. This study discusses potential contributing factors, including feature-space mismatch with recommendation algorithms and possible anomaly-detection-based suppression effects. However, because platform-level recommendation algorithms remain undisclosed, no direct causal relationship is asserted. MRST does not modify the internal architecture of generative models. Instead, it functions as an external stabilization protocol that compresses generative output freedom through persistent long-horizon constraint structures. This study is positioned as an initial implementation report and preliminary observational record of such operational methodology.