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This paper characterize in-context evolutionary stagnation and analytically propose the Improvement Chain proposition, showing how learning successive self-generated algorithms can locally increase the likelihood of neighboring algorithms.
A novel method, namely AnDPro, is proposed, which introduces a projection-based scoring function to more accurately measure token importance and guide more accurate token selection in key-Value cache eviction.
Experiments across diverse multi-agent workloads show that AGAO improves task effectiveness while reducing unnecessary computation, latency, and token consumption compared with existing graph-based execution strategies.
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