#scalable-ai News & Analysis

3 articles tagged with #scalable-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AINeutralarXiv – CS AI · Jun 26/10

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Coordination Graphs for Constrained Multi-Agent Reinforcement Learning

Researchers introduce CG-CMARL, a framework combining coordination graphs with Lagrangian duality to solve constrained multi-agent reinforcement learning problems. The approach decomposes complex joint action spaces into manageable pairwise regions, enabling scalability to larger agent teams while maintaining convergence guarantees and allowing dynamic Pareto front tracing without retraining.

AINeutralarXiv – CS AI · Jun 26/10

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Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems

Researchers introduce SCALR, a framework that generates synthetic user-item interaction data across recommendation system domains by leveraging observed events from source domains. The approach addresses data sparsity challenges in large-scale recommendation systems and demonstrates statistically significant improvements in industrial A/B testing.

AINeutralarXiv – CS AI · May 286/10

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Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey

A comprehensive survey examines how Mixture-of-Experts (MoE) architectures address multimodal learning challenges by enabling scalable modeling, enriching representation learning across modalities, and adapting to imperfect data scenarios. The research identifies critical gaps in interpretable routing, expert communication, and lifelong multimodal learning, positioning MoE as a foundational framework for building more efficient and flexible AI systems.