#workflow-optimization2 articles

2 articles

AIBullisharXiv – CS AI · 4h ago2

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DenoiseFlow: Uncertainty-Aware Denoising for Reliable LLM Agentic Workflows

Researchers introduce DenoiseFlow, a framework that addresses reliability issues in AI agent workflows by managing uncertainty through adaptive computation allocation and error correction. The system achieves 83.3% average accuracy across benchmarks while reducing computational costs by 40-56% through intelligent branching decisions.

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AIBullisharXiv – CS AI · 4h ago1

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WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks

Researchers propose WirelessAgent++, an automated framework for designing AI agent workflows in wireless networks using Monte Carlo Tree Search. The system achieves superior performance on wireless tasks with test scores up to 97%, outperforming existing methods by up to 31% while maintaining low computational costs under $5 per task.