The authors introduce definitions and a qualitative model of AI sovereignty that identifies multi-scale contributors and leverage points nations can target through kinetic and non-kinetic actions to influence AI-driven national power.
The rising costs of training frontier AI models,
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Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
Cross-model semantic disagreement adds an epistemic uncertainty term that improves total uncertainty estimation over self-consistency alone, helping flag confident errors in LLMs.
Introduces Switching Efficiency (η) decomposed into data, routing efficiency, and port utilization factors to analyze and improve communication bottlenecks in AI data center networks for LLM training.
Lightweight numerical bandits on text embeddings match or exceed LLM accuracy in contextual bandits at a fraction of the cost, with an embedding-based diagnostic to choose between them.
FlexServe achieves up to 10x faster time-to-first-token for secure LLM inference on mobile devices by using flexible resource isolation in TrustZone compared to standard approaches.
A graph-spectral importance score based on layer-wise structural distortion between pre- and post-activation neuron graphs identifies removable neurons for iterative pruning without intermediate updates, followed by recovery fine-tuning.
FCP shards sequences at block level with flexible P2P communication and bin-packing to achieve near-linear scaling up to 256 GPUs and 1.13x-2.21x higher attention MFU in foundation model pre-training.
AI agents convert compute capital into cognitive labor units, so on substitutable tasks the competitive human wage is bounded above by relative productivity times compute intensity times the rental rate of compute.
Stronger physics priors in neural networks for spatio-temporal shear flow forecasting yield substantially lower training carbon footprints than weak or no priors, though inference savings are less consistent.
A review of AI sustainability studies finds inconsistent life cycle definitions and predominant reliance on coarse CO2e proxies, with limited coverage of water, materials, and multi-impact assessments.
Open-weight models have ended the foundation model era by eliminating pre-training as a durable moat and enabling sovereign AI control through direct access to model weights.
A survey taxonomy of LLMs identifies three scaling crises and six efficiency paradigms while tracing the shift from generation to tool-using agents.
Fine-tuned LLaMA 3.1-8B variants for the energy sector outperform the base model on domain QA benchmarks, with LoRA delivering similar gains at lower training cost.
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FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
FlexServe achieves up to 10x faster time-to-first-token for secure LLM inference on mobile devices by using flexible resource isolation in TrustZone compared to standard approaches.