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Strategic Doctrine Language Models (sdLM): A Learning-System Framework for Doctrinal Consistency and Geopolitical Forecasting

Olaf Yunus Laitinen Imanov, Taner Yilmaz, Derya Umut Kulali

TL;DR

This work introduces Strategic Doctrine Language Models (sdLM), a learning-system framework designed for doctrinally consistent, calibrated long-horizon strategic reasoning in defense contexts. It couples two specialized transformers, GIPFEL-I for grand strategic planning and SANDKASTEN-I for wargaming, with architectural innovations such as hierarchical multi-document attention, temporal encoding, and a doctrinal-consistency layer, trained via a three-phase pipeline (pre-training, supervised fine-tuning, and reinforcement learning from human feedback) on an expansive, curated military corpus. Rigorous evaluation against 47 senior strategists across 127 decisions shows state-of-the-art performance on strategic planning, high doctrine-consistency precision, and well-calibrated geopolitical forecasts, alongside significant operational gains in wargaming efficiency. Deployment across classified networks and integration with existing command-and-control systems demonstrate operational viability, while the work also candidly addresses limitations, ethical considerations, and future directions toward multimodal data, multi-agent coordination, and advanced optimization techniques. Overall, sdLM represents a substantive step toward reliable, human-aligned AI-assisted strategic planning with potential for substantial efficiency gains and enhanced doctrinal coherence in complex geopolitical environments.

Abstract

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.

Strategic Doctrine Language Models (sdLM): A Learning-System Framework for Doctrinal Consistency and Geopolitical Forecasting

TL;DR

This work introduces Strategic Doctrine Language Models (sdLM), a learning-system framework designed for doctrinally consistent, calibrated long-horizon strategic reasoning in defense contexts. It couples two specialized transformers, GIPFEL-I for grand strategic planning and SANDKASTEN-I for wargaming, with architectural innovations such as hierarchical multi-document attention, temporal encoding, and a doctrinal-consistency layer, trained via a three-phase pipeline (pre-training, supervised fine-tuning, and reinforcement learning from human feedback) on an expansive, curated military corpus. Rigorous evaluation against 47 senior strategists across 127 decisions shows state-of-the-art performance on strategic planning, high doctrine-consistency precision, and well-calibrated geopolitical forecasts, alongside significant operational gains in wargaming efficiency. Deployment across classified networks and integration with existing command-and-control systems demonstrate operational viability, while the work also candidly addresses limitations, ethical considerations, and future directions toward multimodal data, multi-agent coordination, and advanced optimization techniques. Overall, sdLM represents a substantive step toward reliable, human-aligned AI-assisted strategic planning with potential for substantial efficiency gains and enhanced doctrinal coherence in complex geopolitical environments.

Abstract

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.
Paper Structure (59 sections, 5 equations, 10 figures, 10 tables)

This paper contains 59 sections, 5 equations, 10 figures, 10 tables.

Table of Contents

  1. Introduction
  2. The Military Language Models Framework
  3. Limitations of Existing Approaches
  4. Contributions
  5. Related Work
  6. Domain-Specific and Scientific Language Models
  7. Long-Context, Efficient Attention, and Quantized Inference
  8. Retrieval-Augmented and Multi-Hop Reasoning
  9. Calibration, Proper Scoring, and Uncertainty Estimation
  10. Evaluation, Factuality, and Competitive Decision Environments
  11. The Strategic Doctrine Language Models Framework
  12. Design Philosophy and Requirements
  13. Two-Model Architecture Rationale
  14. Architectural Innovations
  15. GIPFEL-I: Grand Strategic Planning Model
  16. ...and 44 more sections

Figures (10)

  • Figure 1: Strategic scenario quality distribution (box-and-whisker) for five evaluators. Boxes show interquartile ranges with medians; whiskers indicate non-outlier ranges; red diamonds denote outliers. Post-hoc comparisons follow one-way ANOVA (F(4,230)=47.3, $p < 0.001$).
  • Figure 2: Geopolitical binary prediction accuracy versus time horizon (12, 24, 36, and 60 months). Error bars denote 95% confidence intervals; baselines include human experts and a base-rate predictor.
  • Figure 3: Calibration curves (reliability diagram) with 10 probability bins. The diagonal indicates perfect calibration. Brier scores are annotated to summarize probabilistic accuracy.
  • Figure 4: Scenario plausibility by type using violin plots with embedded box plots. Distributions correspond to 10-point Likert ratings from the expert panel.
  • Figure 5: Historical tactical alignment heatmap between predicted and observed adversary tactics. Diagonal cells reflect correct matches; off-diagonal cells highlight common confusions.
  • ...and 5 more figures