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Tag

#scalability

11 articles tagged #scalability

arxivMay 22bullish

Billion-Scale Graph Foundation Models

arXiv:2602.04768v2 Announce Type: replace Abstract: Graph-structured data underpins many critical applications. While foundation models have transformed language and vision via large-scale pretraining and lightweight adaptation, extending this paradigm to general, real-world graphs is challenging. I

GR1 model#graph-learning#foundation-models#pretrainingRead on arxiv →
arxivMay 21bullish

Fast and Featureless Node Representation Learning with Partial Pairwise Supervision

arXiv:2605.19916v1 Announce Type: cross Abstract: We introduce Contrastive FUSE, a fast and unified framework for scalable node representation learning in graphs with partially available pairwise node labels and no available node features. Unlike existing methods, we directly optimize a spectral con

CO1 model#machine-learning#graph-learning#optimizationRead on arxiv →
arxivMay 16bullish

Krause Synchronization Transformers

arXiv:2602.11534v3 Announce Type: replace-cross Abstract: Self-attention in Transformers relies on globally normalized softmax weights, causing all tokens to compete for influence at every layer. When composed across depth, this interaction pattern induces strong synchronization dynamics that favor

MEQWVI3 models#transformers#attention#efficiencyRead on arxiv →
arxivMay 11

When Stored Evidence Stops Being Usable: Scale-Conditioned Evaluation of Agent Memory

arXiv:2605.07313v1 Announce Type: new Abstract: Memory-agent evaluations report fixed-snapshot accuracy or retrieval quality, but these scores do not show whether evidence remains usable as irrelevant sessions (sessions not annotated as task-relevant evidence for the query) accumulate. We present a

HILIQW5 models · +2#evaluation#memory#agentsRead on arxiv →
arxivMay 1bullish

ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models

arXiv:2604.27467v1 Announce Type: cross Abstract: Code sandboxes have emerged as a critical infrastructure for advancing the coding capabilities of large language models, providing verifiable feedback for both RL training and evaluation. However, existing systems fail to provide accurate verificatio

#research#large-language-models#code-trainingRead on arxiv →
arxivApr 30bullish

Efficient Traffic Forecasting on Large-Scale Road Network by Regularized Adaptive Graph Convolution

arXiv:2506.07179v2 Announce Type: replace-cross Abstract: Traffic prediction is a critical task in spatial-temporal forecasting with broad applications in travel planning and urban management. To model the complex spatial-temporal dependencies in traffic data, Spatial-Temporal Graph Convolutional Ne

SPRE2 models#machine-learning#traffic-prediction#graph-convolutionRead on arxiv →
arxivApr 27

A Nationwide Japanese Medical Claims Foundation Model: Balancing Model Scaling and Task-Specific Computational Efficiency

arXiv:2604.22348v1 Announce Type: new Abstract: Clinical risk prediction using longitudinal medical data supports individualized care. Self-supervised foundation models have emerged as a promising approach for leveraging large-scale unlabeled healthcare records. In natural language processing, scali

TR1 model#healthcare#foundation-models#medical-researchRead on arxiv →
arxivApr 16bullish

METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues

arXiv:2604.11427v2 Announce Type: replace-cross Abstract: Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose \ours, a method that leverages large language models to autonomously induce both strategy actions and pla

ME1 model#dialogue-agents#language-models#scalabilityRead on arxiv →
arxivApr 16bullish

Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem

arXiv:2507.09503v4 Announce Type: replace-cross Abstract: This paper proposes a neural stochastic optimization method for efficiently solving the two-stage stochastic unit commitment (2S-SUC) problem under high-dimensional uncertainty scenarios. The proposed method approximates the second-stage reco

NE1 model#optimization#machine-learning#scalabilityRead on arxiv →
arxivApr 7bullish

Neuromorphic Computing for Low-Power Artificial Intelligence

arXiv:2604.04727v1 Announce Type: cross Abstract: Classical computing is beginning to encounter fundamental limits of energy efficiency. This presents a challenge that can no longer be solved by strategies such as increasing circuit density or refining standard semiconductor processes. The growing c

#neuromorphic#hardware#energy-efficiencyRead on arxiv →
arxivApr 3bullish

annbatch unlocks terabyte-scale training of biological data in anndata

arXiv:2604.01949v1 Announce Type: new Abstract: The scale of biological datasets now routinely exceeds system memory, making data access rather than model computation the primary bottleneck in training machine-learning models. This bottleneck is particularly acute in biology, where widely used commu

#machine-learning#genomics#scalabilityRead on arxiv →
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