Strategic Doctrine Language Models (sdLM): A Learning-System Framework for Doctrinal Consistency and Geopolitical Forecasting
This addresses the need for improved geopolitical forecasting and plan plausibility with reduced doctrinal violations, though it appears incremental as it builds on existing methods like multi-document attention and temporal encoding.
The paper tackles the problem of multi-document strategic reasoning with doctrinal consistency constraints, introducing the Strategic Doctrine Language Models (sdLM) framework, which achieves higher strategic quality and better calibration than strong LLM baselines and remains competitive with human experts on long-horizon judgments.
We introduce Strategic Doctrine Language Models (sdLM), a learning-system framework for multi-document strategic reasoning with doctrinal consistency constraints and calibrated uncertainty. The approach combines multi-document attention, temporal encoding, and a doctrine-consistency layer to improve long-horizon forecasting and plan plausibility while reducing severe doctrinal violations. We evaluate sdLM using (i) expert-panel scoring of strategic scenarios (N=47), (ii) doctrine consistency on 336 doctrine publications (12,847 statements), and (iii) geopolitical forecasting on 127 historical counterfactuals (1945-2020) across 12-60 month horizons. Across these benchmarks, sdLM achieves higher strategic quality and better calibration than strong general-purpose LLM baselines, and remains competitive with human experts on long-horizon judgments. We further report ablations, scaling trends, and deployment-oriented performance/latency characteristics to clarify which components drive improvements and how they translate to operational settings.