Strategic Doctrine Language Models (sdLM): A Learning-System Framework for Doctrinal Consistency and Geopolitical Forecasting
cs.LG, cs.CL
Submitted: 2026-01-21
Updated: 2026-09-06
Comments: The authors have identified critical methodological flaws and inconsistencies in the experimental evaluation pipeline that invalidate the primary forecasting results and core conclusions. Upon mutual agreement among all co-authors, the manuscript is withdrawn
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Attention Is All You Need
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- Reformer: The Efficient Transformer
- Rethinking Attention with Performers
- Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
- Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- Adam: A Method for Stochastic Optimization
- Decoupled Weight Decay Regularization
- Improving neural networks by preventing co-adaptation of feature detectors
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- On Calibration of Modern Neural Networks
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
- Measuring Massive Multitask Language Understanding
- BERTScore: Evaluating Text Generation with BERT
- Learning to Communicate with Deep Multi-Agent Reinforcement Learning
- Deep reinforcement learning from human preferences
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