SANet uses semantic-aware AI agents for cross-layer 6G optimization, achieving up to 14.61% performance gains with 44.37% of the FLOPs of prior methods via model partitioning and decentralized multi-objective algorithms.
MoA is all you need: Building LLM research team using mixture of agents
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MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.
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SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G
SANet uses semantic-aware AI agents for cross-layer 6G optimization, achieving up to 14.61% performance gains with 44.37% of the FLOPs of prior methods via model partitioning and decentralized multi-objective algorithms.
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MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration
MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.