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Memory self-evolution uses task feedback to iteratively improve executable memory programs that store and retrieve information from past interactions. Existing approaches typically adopt holistic evolution, deriving revision directions from mixed feedback and judging progress by overall performance. This can obscure op...
DMG is presented, the first practical graph processing system on DM, which demonstrates superior system scalability and cache efficiency while delivering high performance and can elastically scale up both compute and memory resources.
This work proposes MESA (a Multi-structure Evidence Selection framework for long-horizon Agent), which builds five complementary structure views of each trajectory and learns from end-to-end answer-level feedback to select and fuse a query-specific subset for a frozen answer model.
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