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AgentGrad improves multi-agent prompt optimization by using sequential intervention to identify target agents and semantic clustering of textual gradients, achieving state-of-the-art performance and reducing optimization time by 2.5 times.
GEM reformulates LLM data curation as a variational problem on the hypersphere, using geometric entropy mixing and a minorize-maximize algorithm to discover balanced semantic clusters, achieving state-of-the-art improvements in data mixing strategies by up to 1.2% average downstream accuracy.
RecMem is a recurrence-based memory consolidation method for long-running LLM agents that reduces token consumption by up to 87% while improving accuracy, by only invoking LLMs when semantically similar interactions recur.