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SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication
arXiv – CS AI|Ruijia Zhang, Xinyan Zhao, Ruixiang Wang, Sigen Chen, Guibin Zhang, An Zhang, Kun Wang, Qingsong Wen|
🤖AI Summary
SafeSieve is a new algorithm for optimizing LLM-based multi-agent systems that reduces token usage by 12.4%-27.8% while maintaining 94.01% accuracy. The progressive pruning method combines semantic evaluation with performance feedback to eliminate redundant communication between AI agents.
Key Takeaways
- →SafeSieve achieves significant token reduction (12.4%-27.8%) in multi-agent AI systems while preserving high accuracy at 94.01%.
- →The algorithm uses a dual-mechanism approach combining initial semantic evaluation with accumulated performance feedback.
- →Unlike greedy pruning methods, SafeSieve employs 0-extension clustering to preserve coherent agent groups.
- →The system demonstrates robustness against prompt injection attacks with only 1.23% accuracy drop.
- →Implementation reduces deployment costs by 13.3% in heterogeneous settings while being GPU-free and scalable.
#llm#multi-agent-systems#ai-optimization#token-efficiency#machine-learning#safesieve#ai-communication#pruning-algorithms
Read Original →via arXiv – CS AI
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