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This paper proposes AgentDoG 1.5, a lightweight and scalable alignment framework for AI agent safety, using taxonomy-guided training with minimal samples to achieve performance comparable to leading closed-source models.
This paper presents a scalable heterogeneous graph neural network workflow for data-driven optimal power flow surrogate modeling, using distributed training on supercomputers and demonstrating improvements via fine-tuning pretrained models.
Equilibrium Reasoners (EqR) introduce a novel framework for scalable reasoning by learning task-conditioned attractors in latent dynamical systems, achieving over 99% accuracy on Sudoku-Extreme by unrolling up to 40,000 layers.
TideGS introduces an out-of-core training framework that enables 3D Gaussian Splatting with over one billion primitives on a single GPU by managing parameters across SSD-CPU-GPU hierarchy via block-virtualization, asynchronous pipeline, and differential streaming techniques.
M1 by Montage is an agentic UI platform that scales on demand.
PACER is a new scalable framework for causal discovery from large-scale interventional data that guarantees acyclicity by design, achieving up to two orders of magnitude speedups over penalty-based methods on benchmarks with thousands of variables.
This paper presents a scalable open-source pipeline using NVIDIA NeMo for training and inference of Video Foundation Models, addressing challenges in generating high-quality videos with accelerated dataset curation and parallelized training.