SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication

TL;DR

SafeSieve is an adaptive multi-agent pruning algorithm that improves communication efficiency and robustness in LLM-based systems by dynamically refining agent interactions using a dual-mechanism approach.

Contribution

It introduces a novel dual-mechanism for progressive pruning, combining semantic evaluation with feedback, and employs 0-extension clustering to preserve agent group structures.

Findings

Achieves 94.01% accuracy with 12.4%-27.8% token reduction

Demonstrates robustness with only 1.23% accuracy drop under prompt attacks

Reduces deployment costs by 13.3% in heterogeneous settings

Abstract

LLM-based multi-agent systems exhibit strong collaborative capabilities but often suffer from redundant communication and excessive token overhead. Existing methods typically enhance efficiency through pretrained GNNs or greedy algorithms, but often isolate pre- and post-task optimization, lacking a unified strategy. To this end, we present SafeSieve, a progressive and adaptive multi-agent pruning algorithm that dynamically refines the inter-agent communication through a novel dual-mechanism. SafeSieve integrates initial LLM-based semantic evaluation with accumulated performance feedback, enabling a smooth transition from heuristic initialization to experience-driven refinement. Unlike existing greedy Top-k pruning methods, SafeSieve employs 0-extension clustering to preserve structurally coherent agent groups while eliminating ineffective links. Experiments across benchmarks (SVAMP, HumanEval, etc.) showcase that SafeSieve achieves 94.01% average accuracy while reducing token usage by 12.4%-27.8%. Results further demonstrate robustness under prompt injection attacks (1.23% average accuracy drop). In heterogeneous settings, SafeSieve reduces deployment costs by 13.3% while maintaining performance. These results establish SafeSieve as an efficient, GPU-free, and scalable framework for practical multi-agent systems. Our code can be found here: https://github.com/csgen/SafeSieve